AN ABSTRACT OF THE DISSERTATION OF
Teepakorn Kongraksawech for the degree of Doctor of Philosophy in Food Science &
Technology presented on March 16, 2012.
Title: Studies in Cereal Science: Arabinoxylans, Glutenins, and their Interactions;
Determining Optimum Water Addition in Noodle Doughs; and Quality and Nutritional
Traits in a Hard x Soft Wheat Cross.
Abstract approved:
_____________________________________________________________________
Andrew S. Ross
The major components of wheat flour are keys to its functionality in processing and
product quality. The major components, other than the lipids, are polymers: starch,
protein, and non-starchy polysaccharides (NSP). In wheat NSP are primarily
arabinoxylans (AX). These components are compartmentalized in the grain but are
forced into close contact after the disruption caused by the milling process. These
components further interact once water is added to the flour to create doughs and
batters. It is these interactions and the water holding capacities of these polymeric
components that are the unifying thread for most of this dissertation, other than the
inclusion of nutritional traits in chapters 6 and 7. This dissertation consists of three
independent studies, the last of which had two parts. Study one was “Effect of
carbonate on co-extraction of arabinoxylans (AX) with glutenin macropolymer
(GMP)”. The aim of this study was to investigate if the level of AX in GMP increased
under alkaline extraction conditions compared to extractions done in water. The
amount of wet GMP obtained from alkaline extraction was greater than that from
water extraction. Hard wheats had overall higher GMP wet weights than soft wheats.
The level of AX in GMP extracted under alkaline conditions was greater than that in
GMP extracted with water and the amount of increase was generally higher in soft
wheats.
Study two was “Optimization of water addition to noodle doughs”. The aim of this
study was to determine if a lubricated squeezing flow (LSF) technique could be useful
in determination of optimum water addition to noodle doughs. Comparing the LSF
method with alternative methods (Mixograph and sieving test), optimum water
additions predicted by LSF for both salted and alkaline soft-wheat derived noodle
doughs were equivalent or slightly higher than those predicted by the Mixograph and
sieving test. For both salted and alkaline hard-wheat derived noodle doughs, optimum
water additions predicted by the LSF method were substantial higher than those
predicted by the Mixograph but equivalent or slightly higher than those predicted by
the sieving test. Relaxation time was the most useful parameter in determining
optimum water addition for the soft-wheat noodle doughs. The LSF method in its
current form was found to be not adequate for all noodle types. Additional work with
LSF parameters altered to improve sensitivity and with more of samples should be
performed.
Study three was “Determination of wheat quality and quantitative trait locus
analysis”. Part 1 was to measure a comprehensive set of quality phenotypes (including
nutritional parameters) on a wheat population derived from the cross Tubbs [soft] x
NSA98-0995 [hard]; (T x N). Part 2 was to identify if there were quantitative trait loci
(QTL) associated with the traits determined in part 1. Considerable and transgressive
segregation was observed for many of the studied traits. The transgressive segregation
could useful, in that lines with superior soft-wheat quality can be identified that could
be introduced quickly into the wheat breeding program from this elite x elite cross.
Hardness index was significantly correlated with several important traits related to the
solvent absorption capacity of the flour. Composite interval mapping detected a total
of significant 28 QTLs on 10 wheat chromosomes for 15 end-use quality and nutrition
traits in 2 harvest years. QTLs for total antioxidant activity (TAA) and total phenolic
content (TPC) were identified for the first time. QTLs for TAA were on chromosomes
3B and 5BS, while the QTL for TPC was on chromosome 7AC. Hybridization
between Tubbs and NSA surprisingly produced superior soft-wheat quality with
potentially higher in nutritional values. The QTLs identified in this study could be
useful in marker-assisted selection for future pre-selection of progeny from Tubbs or
NSA.
©Copyright by Teepakorn Kongraksawech
March 16, 2012
All Rights Reserved
Studies in Cereal Science: Arabinoxylans, Glutenins, and their Interactions;
Determining Optimum Water Addition in Noodle Doughs; and Quality and Nutritional
Traits in a Hard x Soft Wheat Cross
by
Teepakorn Kongraksawech
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented March 16, 2012
Commencement June 2012
Doctor of Philosophy dissertation of Teepakorn Kongraksawech presented on March
16, 2012.
APPROVED:
___________________________________________________________
Major Professor, representing Food Science & Technology
___________________________________________________________
Head of the Department of Food Science & Technology
___________________________________________________________
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of
Oregon State University libraries. My signature below authorizes release of my
dissertation to any reader upon request.
___________________________________________________________
Teepakorn Kongraksawech, Author
ACKNOWLEDGEMENTS
I would like to express my deep and sincere gratitude to my major professor, Dr.
Andrew Ross for his support, encouragement, guidance, patience, and generosity
throughout my Ph.D. program at Oregon State University (OSU). I also would like to
thank my graduate committee, Drs. Patrick Hayes, Michael Penner, Gary Hou, and
Gregory Thompson. Also thanks Drs. Robert McGorrin, James Peterson, and James
Osborne for temporary serving in my committee.
This research was possible through funds granted by Oregon Wheat Commission.
I would like to thank everyone in the OSU Wheat breeding group (particularly Caryn
Ong, Adam Heesacker, Eddie Simons, and Mark Larson), the student workers (Robert
Larson, Jordan Smith, Jake Mattson, Mike Adam, and Matthew Savino), and Alfonso
Cuesta-Marcos for all their friendship and help in my research.
Thank all my friends here and in Thailand, the crop science and food science graduate
students, faculty and staffs in the department of Food Science and Technology and the
department of Crop and Soil Sciences for all the fun, support, and help during my
study at OSU.
Last but not least, I would like to express my deepest gratitude to my parents, brothers,
and sisters for their love, patience, support and encouragement. Special thanks to my
brothers and sisters for taking such good care of mom and dad while I am away. I am
always grateful for that. I honestly could not have accomplished any of this alone.
TABLE OF CONTENTS
Page
Chapter 1: Introduction .................................................................................................. 1 Chapter 2: Literature Review ......................................................................................... 8 2.1 Wheat ................................................................................................................... 8 2.2 Wheat classification ............................................................................................. 9 2.3 Wheat kernel anatomy ....................................................................................... 12 2.4 Kernel texture ..................................................................................................... 15 2.5 Wheat flour milling ............................................................................................ 16 2.6 Wheat applications ............................................................................................. 19 2.6.1 Bread ........................................................................................................... 19 2.6.2 Cookies........................................................................................................ 22 2.6.3 Asian noodles .............................................................................................. 25 2.7 Chemical composition of wheat ......................................................................... 30 2.7.1 Carbohydrates ............................................................................................. 30 2.7.1.1 Starch ................................................................................................... 30 2.7.1.2 Nonstarch polysaccharides ................................................................... 35 2.7.1.2.1 Arabinoxylans ................................................................................... 35 2.7.1.2.2glucan ............................................................................................ 39 2.7.1.2.3 Cellulose............................................................................................ 41 2.7.1.2.4 Arabinogalactan-peptides.................................................................. 42 2.7.2 Proteins........................................................................................................ 43 2.7.2.1 Gliadins ................................................................................................ 44 2.7.2.2 Glutenins .............................................................................................. 46 2.7.3 Lipids .......................................................................................................... 48 2.8 Health benefits of wheat consumption ............................................................... 51 2.9 Marker assisted selection ................................................................................... 56 Chapter 3: Materials and Methods ............................................................................... 59 3.1 Materials............................................................................................................. 59 TABLE OF CONTENTS (Continued)
Page
3.1.1 Effect of carbonate on co-extraction of arabinoxylans and glutenin
macropolymer (Chapter 4) ................................................................................... 59 3.1.2 Optimization of water addition to noodle doughs (Chapter 5) ................... 60 3.1.3 Determination of wheat quality and quantitative trait locus analysis
(Chapter 6 and 7).................................................................................................. 60 3.2 Methods .............................................................................................................. 60 3.2.1 Effect of carbonate on co-extraction of arabinoxylans and glutenin
macropolymer ...................................................................................................... 60 3.2.2 Optimization of water addition to noodle doughs ....................................... 63 3.2.2.1 Damaged starch .................................................................................... 63 3.2.2.2 Noodle dough making .......................................................................... 64 3.2.2.3 Determination of optimum water addition ........................................... 65 3.2.2.3.1 Mixograph method ............................................................................ 65 3.2.2.3.2 Sieving method ................................................................................. 66 3.2.2.3.3 Lubricated squeezing flow method ................................................... 66 3.2.3 Determination of wheat quality................................................................... 67 3.2.3.1 Kernel characteristics ........................................................................... 67 3.2.3.2 Polyphenol oxidase activity ................................................................. 67 3.2.3.3 Milling performance test ...................................................................... 68 3.2.3.4 Grain and flour protein contents .......................................................... 69 3.2.3.5 Damaged starch .................................................................................... 69 3.2.3.6 Arabinoxylan ........................................................................................ 70 3.2.3.7 Solvent Retention Capacity test ........................................................... 71 3.2.3.8 Polymeric proteins ............................................................................... 72 3.2.3.9 Sugar-snap cookie ................................................................................ 74 3.2.3.10 Total antioxidant activity and total phenolic content ......................... 76 3.2.4 Quantitative trait locus analysis (Chapter 7) ............................................... 79 3.2.5 Statistical analyses ...................................................................................... 79 Chapter 4: Results and discussion: Effect of carbonate on co-extraction of
arabinoxylans with glutenin macropolymer ................................................................. 80 4.1 GMP-wet weight ................................................................................................ 81 4.2 GMP-AX ............................................................................................................ 86 TABLE OF CONTENTS (Continued)
Page
Chapter 5: Results and discussion: Optimization of water addition to noodle doughs 89 5.1 Flour characteristics ........................................................................................... 89 5.2 Noodle dough optimum water addition determined by Mixograph ................... 90 5.3 Sieving test ......................................................................................................... 91 5.3.1 Salted doughs .............................................................................................. 91 5.3.2 Alkaline doughs .......................................................................................... 95 5.4 Lubricated squeezing flow rheology .................................................................. 97 5.4.1 Salted doughs .............................................................................................. 98 5.4.1.1 LSF maximum and residual force values ............................................. 98 5.4.1.2 LSF relaxation time values ................................................................ 102 5.4.2 Alkaline doughs ........................................................................................ 106 5.4.2.1 LSF maximum force values ............................................................... 106 5.4.2.2 LSF relaxation time values ................................................................ 108 Chapter 6: Results and discussion: Determination of wheat quality.......................... 112 6.1 Kernel characteristics ....................................................................................... 112 6.2 Polyphenol oxidase activity ............................................................................. 123 6.3 Milling performance test .................................................................................. 124 6.4 Grain and flour protein contents ...................................................................... 128 6.5 Polymeric proteins ........................................................................................... 132 6.6 Damaged starch ................................................................................................ 134 6.7 Arabinoxylans .................................................................................................. 135 6.8 Solvent retention capacity test ......................................................................... 139 6.9 Sugar-snap cookie ............................................................................................ 142 6.10 Total antioxidant activity and total phenolic content ..................................... 145 Chapter 7: Results and discussion: Quantitative trait locus analysis ......................... 148 7.1 Kernel characteristics ....................................................................................... 148 TABLE OF CONTENTS (Continued)
Page
7.2 Polyphenol oxidase activity ............................................................................. 155 7.3 Milling performance test .................................................................................. 156 7.4 Grain and flour protein contents ...................................................................... 157 7.5 Polymeric proteins ........................................................................................... 160 7.6 Damaged starch ................................................................................................ 161 7.7 Arabinoxylans .................................................................................................. 161 7.8 Solvent retention capacity ................................................................................ 162 7.9 Sugar-snap cookie ............................................................................................ 163 7.10 Total antioxidant activity and total phenolic compound ................................ 164 Chapter 8: Conclusions .............................................................................................. 166 8.1 Effect of carbonate on co-extraction of arabinoxylans with glutenin
macropolymer (Chapter 4) ..................................................................................... 166 8.2 Optimization of water addition to noodle doughs (Chapter 5) ........................ 166 8.3 Determination of wheat quality (Chapter 6) .................................................... 169 8.4 Quantitative trait locus analysis (Chapter 7) .................................................... 172 Bibliography............................................................................................................... 175 LIST OF FIGURES
Figure
Page
Figure 2.1: The structure of the wheat kernel (Dexter and Sarkar, 2004) ................... 12 Figure 2.2: The milling of wheat (Anonymous, 2000a)............................................... 18 Figure 2.3: Scanning electron photomicrograph of wheat starch granules (Delcour and
Hoseney, 2010b) .......................................................................................................... 30 Figure 2.4: Molecular structure of (a) amylose; and (b) amylopectin (Pérez et al.,
2009) ............................................................................................................................ 31 Figure 2.5: Chemical structure of unsubstituted xylan residue (a), xylan residue
substituted at O-2 with an arabinose residue (b), xylan residue substituted at O-3 with
an arabinose residue (c), xylan residue substituted at O-2 and O-3 with arabinose
residues. Structure c shows the link of ferulic acid to C-5 of an arabinose residue
(Delcour and Hoseney, 2010a). .................................................................................... 36 Figure 2.6: The primary structure of the -glucan (Chawla and Patil, 2010) .............. 41 Figure 2.7: Chemical structure of cellulose (Belitz et al., 2008) ................................. 41 Figure 2.8: Rheological behavior of gliadin and glutenin (Wrigley et al., 2006) ........ 44 Figure 3.1: A typical HPLC chromatogram of polymeric protein method .................. 73 Figure 4.1: Wet weight of glutenin macropolymer extracted from flour-water (solid
line), and flour-water-sodium carbonate (dashed line) slurries of 23 flour samples.
HRW, hard red winter; HS, hard spring; SWS, soft white spring; SWW, soft white
winter. Error bars represent ± least significant difference between water and alkaline
extractions at P = 0.05. ................................................................................................. 83 Figure 4.2: Arabinoxylan (as xylose equivalents) concentration in glutenin
macropolymer extracted from flour-water (solid line), and flour-water sodium
carbonate (dashed line) slurries of 23 flour samples. HRW, hard red winter; HS, hard
spring; SWS, soft white spring; SWW, soft white winter. Error bars represent ± least
significant difference between water and alkaline extractions at P = 0.05. ................. 87 Figure 5.1: The amounts of material retained and passing through the sieves from the
uncompressed crumble of the salted doughs prepared with the various water additions:
OR2050910 (a), Louise (b), Promontory (c), and WA7954 (d)................................... 94 LIST OF FIGURES (Continued)
Figure
Page
Figure 5.2: The amounts of material retained and passing through the sieves from the
uncompressed crumble of the alkaline doughs prepared with the various water
additions: OR2050910 (a), Louise (b), Promontory (c), and WA7954 (d) .................. 96 Figure 5.3: A typical LSF force/time curve ................................................................. 98 Figure 5.4: LSF maximum force for salted noodle doughs prepared with various water
additions ..................................................................................................................... 101 Figure 5.5: LSF relaxation time for salted noodle doughs made from soft (a) and hard
(b) at various water additions ..................................................................................... 105 Figure 5.6: LSF maximum force for alkaline noodle doughs prepared with various
water additions ........................................................................................................... 107 Figure 5.7: LSF relaxation time for alkaline noodle doughs made from soft (a) and
hard (b) at various water additions ............................................................................. 111 Figure 6.1: Distributions for HIH+S (a), KWH+S (b), and GMCH+S (c) in the 2009 RIL
population................................................................................................................... 117 Figure 6.2: Distributions for HIS (a), KWS (b), and GMCS (c) in 2010 RIL population
.................................................................................................................................... 118 Figure 6.3: Distribution for PPO activity in 2009 RILs ............................................. 123 Figure 6.4: Distributions for FY and BFY in 2009 and 2010 RIL population .......... 127 Figure 6.5: Distributions for grain and flour protein contents in 2009 and 2010 RIL
population................................................................................................................... 131 Figure 6.6: Distributions for LUPP (a) and TUPP (b) in 2009 RIL population ........ 133 Figure 6.7: Distributions for DS in 2009 RIL population .......................................... 135 Figure 6.8: Distributions for TOAX (a), WEAX (b), and WUAX (c) in 2009 RIL
population................................................................................................................... 138 Figure 6.9: Distributions for WSRC-W (a) and WSRC-R (b) in 2009 RIL population
.................................................................................................................................... 140 LIST OF FIGURES (Continued)
Figure
Page
Figure 6.10: Distributions for SSRC-W (a) and SSRC-R (b) in 2009 RIL population
.................................................................................................................................... 142 Figure 6.11: Distributions for CD (a) and CH (b) in 2009 RIL population ............... 144 Figure 6.12: Distributions for TAA (a) and TPC (b) in 2009 RIL population .......... 147 LIST OF TABLES
Table
Page
Table 2.1: Chemical composition of the whole wheat grain and its parts (% dry
weight) (Anonymous, 2000b) ...................................................................................... 15 Table 2.2: Crude fat content in wheat kernel (Morrison, 1978a) ................................. 49 Table 2.3: Distributions of fatty acids in wheat (Morrison, 1994)............................... 50 Table 3.1: Ingredients sugar-snap cookie baking trials for a pair of cookies .............. 75 Table 4.1: Identification, market class, flour protein, absorption, and dough and end
product characteristics of flour samples ......................................................................... 1 Table 5.1: Identification, market class, hardness index, protein content, moisture, and
damaged starch of flour samples .................................................................................. 90 Table 5.2: Optimum water additions determined by Mixograph, sieving, and LSF
methods ........................................................................................................................ 91 Table 5.3: Range of water additions used to prepare noodle doughs for the sieving and
LSF methods ................................................................................................................ 93 Table 5.4: LSF maximum and residual force values for salted noodle doughs prepared
with various water additions ........................................................................................ 97 Table 5.5: LSF relaxation time values for salted and alkaline noodle doughs prepared
with various water additions ...................................................................................... 104 Table 6.1: Values and range of 23 end-use quality traits in Tubbs, NSA, and their RILs
in 2009 and 2010 ........................................................................................................ 115 Table 6.2: Correlations between wheat quality traits of 2009 RILs .......................... 119 Table 6.3: Correlations between wheat quality traits of 2010 RILs .......................... 122 Table 7.1: QTLs detected by composite interval mapping in the T x N population .. 101 Studies in Cereal Science: Arabinoxylans, Glutenins, and their
Interactions; Determining Optimum Water Addition in Noodle
Doughs; and Quality and Nutritional Traits in a Hard x Soft Wheat
Cross
Chapter 1: Introduction
Wheat is one of the world's staple food crops, feeding more than one-third of the
world's population. Annual global production is about 600 million metric tons. Of this
the U.S.A. contributes around 60 million metric tons (USDA, 2011). In the U.S.A.,
wheat is an economically important crop. Approximately half of U.S. wheat is
exported and the other half is used domestically. Among all of the wheat grown
worldwide, common (hexaploid) wheat is the most widely grown. Common wheat
also accounts for about 95% of production in the U.S.A. Wheat is mainly used for
human consumption and is primarily utilized in the form of flour to produce a range of
different products such as bread, cookies, cake and noodles.
The major components of wheat flour are keys to its functionality in processing and
product quality. The major components, other than the lipids, are polymers: starch,
protein, and non-starchy polysaccharides (NSP). In wheat NSP are primarily
arabinoxylans (AX). These components are compartmentalized in the grain but are
forced into close contact after the disruption caused by the milling process. These
components further interact once water is added to the flour to create a dough (water
% w/w < flour %) or a batter (water % w/w > flour %). It is these interactions and the
2
water holding capacities of these polymeric components that are the unifying thread
for most of this dissertation, other than the inclusion of nutritional traits in chapters 6
and 7.
Chapter 4: Effect of carbonate on co-extraction of arabinoxylans with glutenin
macropolymer:
Chapter 4 examines the potential for interaction between gluten proteins and the native
wheat AX in processing of doughs under alkaline conditions.
Wheat endosperm proteins, particularly the glutenins, are an important determinant of
the end-use quality of wheat flour. Glutenins can form large aggregates via
intermolecular disulfide bonding. This so-called glutenin macropolymer (GMP) can be
isolated as the sodium dodecyl sulfate (SDS) insoluble fraction of glutenin. GMP
occurs as a transparent gel-layer after centrifugation of flour suspended in aqueous
SDS. GMP originally was extracted from doughs made only with flour and water in
investigations of the effects of GMP in breadmaking (Don et al., 2003b; Jood et al.,
2001; Skerritt et al., 1999a; Skerritt et al., 1999b; Weegels et al., 1997). These doughs
were neutral in pH and at relatively high water additions for doughs (~60% flour basis,
termed “conventional” in this document). The formation of GMP relies on the ability
of glutenin to aggregate. However, there is evidence in the literature that high
3
molecular weight polysaccharides can reduce gluten agglomeration and wet gluten
yields in gluten separation processes presumably by interfering with glutenin
aggregation (Frederix et al., 2004; Ross, 1995; Wang et al., 2003b).
The high molecular weight NSP in wheat flour, AX, are frequently solubilized and
extracted using alkaline reagents (e.g. D’Appolonia and Gilles (1971)). Under alkaline
processing conditions, alkali solubilized AX could affect gluten functionality via
interference with aggregation. This becomes relevant when it is noted that in some
wheaten products, notably Asian noodles, doughs are often made with alkaline
compounds, such as carbonates, as well as simply with NaCl. These doughs are also
much drier than conventional doughs at ~30% flour basis of water. When extracted
from alkaline noodle doughs GMP declines in wet weight as processing proceeds
through mixing, compounding, and sheeting, again presumably via glutenin
disaggregation. This decline was parallel to the decrease in GMP wet weight seen
when extracted from conventional doughs at sequential stages in mixing. Notably
alkaline doughs had a lower recovery of wet weight after dough resting compared to
GMP extracted from salted doughs (Ong and Ross, 2008) suggesting an inhibition of
glutenin re-aggregation specific to alkaline conditions. It is not just the recovery of
GMP that changes during dough processing but also the strength of the GMP gel. In
conventional flour-water doughs GMP gel strength decreases throughout mixing (Don
et al., 2003a). In contrast GMP gel strength increased as noodle dough processing
4
proceeded for both salted and alkaline dough variants. Additionally, in alkaline
doughs, GMP gel strength increased even further when GMP was extracted after 24 h
of dough resting, suggesting greater levels of polymer entanglement in the gel.
This evidence led to these hypotheses: under alkaline processing conditions: 1) we
would find AX to be associated with GMP and it would be at higher concentrations
than found at lower pH; and 2) GMP de-polymerization would be faster during mixing
and sheeting and re-polymerization slower on dough resting (Ong and Ross, 2008).
Evidence from the literature (D’Appolonia and Gilles, 1971) provided a plausible
mechanism by which the hypothesized interference with gluten properties might
occur: the possible enrichment of the dough liquor (aqueous phase) in alkaline noodle
dough formulations with AX solubilized by alkali, which would be then able to
interact with gluten.
Chapter 5: Optimization of water addition to noodle doughs
Noodles have accompanied humans since historical times. It has been believed that
noodles first originated in China about 7,000 years ago. Remains of thin millet noodles
preserved for 4000 years were found in Yellow river in northwestern China (Lu et al.,
2005). With travel and trade, Asian noodles migrated throughout the rest of Asia and
later spread to western countries. Asian wheat-flour noodles are consumed worldwide
5
today. Noodles are big business, for example approximately 40% of the wheat flour
consumed in Asia used for noodle making and the U.S.A. is one of the major suppliers
of noodle wheat (Hou and Kruk, 1998).
Water, an essential ingredient in noodle making, has profound effects on noodle
quality and appropriate amounts are required in dough for optimum processing and
end-product quality. Currently, the only method that is widely used to determine
whether a proper amount of water has been added during noodle making is the handfeel of an experienced noodle maker. However, this method is subjective and varies
from person to person. Various objective methods have been proposed for use in
determination of optimum water addition to noodles such as Mixograph (Lee et al.,
1998; Oh et al., 1986; Ohm et al., 2006), Farinograph (Kruger, 1996), and sieving tests
(Azudin, 1998; Hatcher et al., 2002). However, they have not been widely adopted.
Lubricated squeezing flow (LSF) is a technique for studying the rheological behavior
of semi-liquid foods including noodle dough (Ong et al., 2010; Ross and Ohm, 2006).
Although the fundamental rheological behavior of an optimally hydrated noodle
dough is not known precisely, LSF could be used: first as a tool to determine optimum
water addition to noodle doughs and secondly to characterize optimal rheological
behavior.
6
Chapters 6 and 7: Determination of wheat quality and quantitative trait locus analysis
Wheat buyers increasingly demand that wheat purchases meet tight specifications for
appropriate and consistent end-use quality. Developing new wheat varieties that meet
or exceed end-use customers’ demands, and that also have better yield, and improved
biotic and abiotic stress tolerance is key to the continued success of the wheat
industry. The fundamental basis of improving wheat quality is the selection of progeny
with superior traits. In classical breeding programs, selection relies on observable
traits (phenotypes). Phenotypic selection for many quality traits is generally time
consuming, laborious, and costly, particularly selection for some economically
important traits that have either low heritability (protein content) or are expressed late
in plant development (grain hardness, dough properties). Phenotypic selection for enduse quality is additionally complicated because it requires large quantities (at least 1
kg) of grain, which is generally only available in the later generations of the breeding
cycle.
The development of molecular (DNA) markers in the late 1970s has changed the
direction of selection from phenotype-based to genotype-based. Marker assisted
selection (MAS) is the process of using DNA markers tightly-linked to specific genes
to help phenotypic screening. By identifying the presence of a molecular marker,
plants that carry specific genes may be identified based on their genotype rather than
their phenotype. MAS therefore can be a cost-effective tool to phenotypic screening
7
for end-use quality. Before MAS can be utilized, it is necessary to identify
chromosome regions or quantitative trait loci (QTLs) affecting the traits of interest.
The identification process is called QTL analysis.
The Oregon State wheat breeding program has created a recombinant inbred line
(RIL) genetic mapping population that is a soft x hard cross (Tubbs [soft] x NSA980995 [hard]). Designated “T x N”, this population was designed primarily to introduce
new sources of tolerance to biotic and abiotic stresses to the OSU wheat germplasm
base.
The objectives of this project were to:
1. Determine if AX material was co-extracted with GMP and if so, did it increase
in concentration in GMP extracted from alkaline-carbonate flour slurries
(Chapter 4).
2. To see if the LSF technique could be used to determine optimum water
addition to noodle doughs (Chapter 5).
3. Determine wheat kernel and wheat flour characteristics, and identify QTLs of
T x N RILs and their parents (Chapter 6).
4. Identify QTLs for traits examined in objective 3 (Chapter 7).
8
Chapter 2: Literature Review
2.1 Wheat
Wheat (Triticum spp.) is the single most important food crop for humans due to its
adaptation to a broad range of environments, ease of storage, and nutritional value.
Other than adaptation, two significant factors make wheat an important staple crop:
firstly the unique properties of its endosperm proteins that enable them to retain gas
after they are hydrated and mechanically worked, which is crucial for leavened
products, and secondly the capability of wheat flour to make a wide variety of endproducts that are not possible using other cereal species (Curtis, 2002; Wrigley, 2009).
Wheat is grown on all of the continents except Antarctica. Wheat was one of the first
domesticated food crops and is believed to have originated in the Fertile Crescent, a
region between western Asia and northern Africa (Belderok, 2000; Davis, 2003). The
first cultivated wheat types were diploid including einkorn (T.monococcum), Aegilops
speltoides, and T. tauschii (Wrigley, 2009). Each species in this group has only one
genome (A, B, or D) that contains two groups of seven chromosomes, totaling 14
chromosomes (2x = 2*7 = 14 chromosomes, e.g. AA). The second cultivated wheat
types were tetraploid: Emmer (T. dicoccum) and durum (T. durum) (4x = 4*7 = 28
chromosomes, AABB). Emmer and durum contain the A and B genomes derived from
the hybridization of T. monococcum and A.speltoides (Wrigley, 2009). Modern
common wheat (T. aestivum) is hexaploid (6x = 6*7 = 42 chromosomes, AABBDD)
and originated from the natural hybridization of wild emmer (T. dicoccoides, AABB)
9
and T. tauschii (DD) (Kimber and Sears, 1987). More than 90% of the wheat grown
worldwide is common hexaploid wheat used to make dough and batter-based products
(Wrigley, 2009).
2.2 Wheat classification
For commercial purposes, common wheat is classified by three major factors: kernel
hardness, growth habit, and grain color. Based on kernel hardness wheat is classed as
hard or soft (Feiz et al., 2008; Jolly et al., 1996) and this has profound effects on the
spectrum of optimum end-uses for each type. As a result of higher levels of starch
damage and therefore higher optimum water absorption in their derived flours, hard
wheats are better suited to dough-based products such as breads. Lower starch damage
levels in soft wheats and therefore lower optimum water absorption in their flours
make soft wheats more suited to batter-based products such as cakes, cookies, and
pastries.
Based on growth habit wheat is classified as winter or spring. Winter wheats require a
period of winter cold temperature (0-5C for at least six weeks; vernalization) before
they can resume growth and form the heads containing wheat kernels (Atwell, 2001;
Curtis, 2002; Worland and Snape, 2001). Winter wheat is planted in fall to germinate
and develop into young plants. The plants stay in the vegetative stage during winter,
10
switch to the reproductive stage in spring and then are harvested in summer. In
contrast, spring wheat does not require vernalization to form heads. It is planted in
spring and harvested in late summer or fall. In North America, winter wheat is most
commonly planted in regions with severe winters (e.g. South Dakota and northward in
the Great Plains) while spring wheat is most commonly planted in regions with milder
winters (e.g. the Pacific Northwest and from Nebraska southwards in the Great
Plains). Spring wheat can be also planted in fall in some regions with mild winter
weather, such as southern Oregon, California, Australia, and South Asia (Curtis,
2002).
The requirement for vernalization in wheat is mainly controlled by three vernalization
(VRN1) genes mapped in the middle of the long arm of chromosomes 5A, 5B, and 5D
(Gooding, 2009; Law et al., 1976; Maystrenko, 1980). The presence of a dominate
allele in any of the three VRN1 loci results in spring wheat while winter wheat
contains recessive alleles in all three VRN1 loci (Gooding, 2009).
Based on the presence of the red pigments of the true seed coat (within the pericarp),
wheat can be divided into two groups: red and white (Atwell, 2001). The pigment
presence and intensity in the seed coat is controlled by the three red (R) genes located
on the end region of the long arms of chromosomes 3A, 3B, and 3D (McIntosh et al.,
1998). Red wheat contains at least one red (R, dominant) allele and the degree of
11
redness generally increases as the number of R allele increases while white wheat
carries only r (recessive) alleles (Flintham, 1993). These red pigments are polyphenol
compounds, phlobaphene, or proanthocyanidin known to be synthesized through the
flavonoid biosynthesis pathway (Himi and Noda, 2005).
Based on the above criteria, common wheat in the U.S.A. can be divided into five
market classes: hard red winter (HRW), hard red spring (HRS), soft red winter (SRW),
hard white (HW), and soft white (SW). HW and SW include both winter and spring
types.
Durum wheat is another important commercial wheat class. It has harder kernels than
common hard wheat and is unique characteristic in that it contains a much higher
concentration of yellow carotenoid pigments distributed throughout the entire
endosperm (Morris, 2002).
12
2.3 Wheat kernel anatomy
Figure 2.1: The structure of the wheat kernel (Dexter and Sarkar, 2004)
A wheat kernel is a complex structure made up of different individual constituents
(Figure 2.1) and can be divided into three major anatomical sections: bran, endosperm,
and germ (Anonymous, 2000b; Dobraszczyk, 2001; Posner and Hibbs, 2005). Bran
accounts for 13-17% of the kernel by weight and contains higher amounts of fiber
(mainly insoluble), and mineral (ash) than the other two sections. Bran is composed of
pericarp (fruit coat), testa (seed coat), and nucellar epidermis (hyaline layer)
surrounding the endosperm and the germ. The pericarp is made up from several layers
divided into outer and inner pericarps. The outer pericarp consists of epidermis and
hypodermis while the inner pericarp consists of cross cells, and tube cells. The
13
pericarp contains primarily nonstarch polysaccharides (about 90% of the total
pericarp) with small amount of protein (6%), ash (2%), and fat (0.5%) (Delcour and
Hoseney, 2010b). The testa, a structure which lies between the tube cells and the
nucellar epidermis, is composed of three layers: a thick outer cuticle, a layer
containing pigment, and a thin inner cuticle. The nucellar epidermis is a compressed
layer between the seed coat and the aleurone layer.
The endosperm makes up about 80-85% of the grain mass. It contains two anatomical
structures, the aleurone layer, and the starchy endosperm. The aleurone layer lies
between the bran and the endosperm. Although aleurone layer is only one cell layer
thick, it is moderately high in enzyme activity, ash, protein, total phosphorus, phytatebound phosphorus, and lipids. Excess aleurone admixed into refined flour leads to
undesirable flour properties and lowers flour acceptability (Atwell, 2001; Delcour and
Hoseney, 2010b). Millers therefore consider the aleurone layer to be part of the bran
and eliminate its presence in refined flours as far as possible during milling (Evers and
Bechtel, 1988).
The starchy endosperm consists of cells containing starch granules embedded in a
protein matrix. Both components are used for nutritional support of the germinating of
embryo prior to the start of photosynthesis. Three types of cell (peripheral, prismatic,
and central cells) make up the starchy endosperm and they are present in different in
14
size, shape, and location within the kernel (Delcour and Hoseney, 2010b; Evers and
Bechtel, 1988). Peripheral cells are located immediately inside the aleurone layer.
They are mostly round and approximately 60 m in diameter. The next adjacent layers
extend toward the middle of the dorsal side and are composed of the elongated
prismatic cells. These cells are about 128-200 m long and 40-60 m wide. The
central cells are located at the center of the dorsal side. Their shapes and sizes vary
significantly.
The germ (embryo) is located on the dorsal side and accounts for 2-4% of the kernel
weight. It has two major structures: embryo and scutellum. The scutellum is a
structure that transports nutrients from the endosperm to the embryo during
germination (Posner and Hibbs, 1997). The general chemical composition of the
whole grain and its parts is shown in Table 2.1. However, different wheat cultivars can
vary significantly in chemical composition.
15
Table 2.1: Chemical composition of the whole wheat grain and its parts (% dry
weight) (Anonymous, 2000b)
Chemical composition Whole grain
Protein
16.0
Fats
2.0
Carbohydrates
68.0
Dietary fibers
11.0
Minerals (ash)
1.8
Other components
1.2
Total
100
Bran Endosperm Germ
16.0
13.0
22.0
5.0
1.5
7.0
16.0
82.0
40.0
53.0
1.5
25.0
7.2
0.5
4.5
2.8
1.5
1.5
100
100
100
2.4 Kernel texture
As noted above wheat can be classified in part by kernel texture (hardness). The
relative hardness of kernels results from the degree of adhesion between the starch
granules and the protein matrix. In soft wheat, starch granules loosely adhere to
protein matrix. On the other hand, the starch-protein interactions in hard wheat are
stronger. The degree of adhesion between the starch granules and the protein matrix is
controlled by friabilin protein (Mikulikova, 2007). Friabilin is found in high
concentration on the surface of water-washed starch granules of soft wheat. In
contrast, friabilin occurs only at small concentrations in hard wheat and is absent in
durum wheat (Greenwell and Schofield, 1986). Friabilin consists of three
polypeptides: puroindolines a and b (pin-a and pin-b), and grain softness protein-1
(Gsp-1), however pin-a and pin-b are the major components (Gautier et al., 1994a;
Giroux and Morris, 1997; Mikulikova, 2007; Rahman et al., 1994). Pin-a and pin-b are
coded by two puroindoline genes (Pin-a and Pin-b) on the Hardness (Ha) locus
16
located on the short arm of chromosome 5D (Gautier et al., 1994b; Jolly et al., 1993;
Morris et al., 1994). Although homeologues of the Ha locus are also present on the 5A
and 5B chromosomes, they are not expressed (Giroux and Morris, 1998).
The presence of wild-type Pin-a and Pin-b genes results in soft kernel texture. An
absence or a mutation in one or both Pin genes leads to strong starch-protein adhesion
and hard grain (Nadolska-Orczyk et al., 2009). Igrejas et al. (2001) determined the
variation of puroindoline content and the heritability of puroindoline content in 40
wheat varieties grown in different locations and suggested that Pin-b might be
responsible for kernel hardness. The absence of the D genome in tetraploid wheat
species results in a lack of Pin genes and therefore puroindolines. Consequently, the
kernel texture of tetraploid wheat species (such as durum) is very hard (Bhave and
Morris, 2008).
2.5 Wheat flour milling
Modern wheat flour milling is a dry-milling process that consists of breaking the grain
open, removing the bran, aleurone and germ, then gradually reducing the size of the
endosperm particles into “flour” (Anonymous, 2000a; Bass, 1988). “Flour” is defined
in Code of Federal Regulations (FDA, 2011) as having greater than 98% of the
material passing a cloth having openings not larger than 212 µm. Prior to milling the
first step is to clean and remove foreign or hazardous materials which might lower
17
flour quality and damage the mill. After cleaning, the cleaned wheat is then tempered.
Tempering is a process of adding an appropriate amount of water to wheat in
relatively short time frame so as to create a moisture gradient within the grain.
Tempering toughens the bran which lessens its tendency to break into small pieces and
helps the separation between bran and endosperm during milling. Tempering also
softens the endosperm which will then be reduced to flour particle size more easily.
The amount of water added to wheat is highly dependent on the initial moisture
content of the grain and kernel texture. Generally, hard wheat requires more water
than soft wheat and the duration of tempering varies from 8 h (soft wheat) to 24 h
(hard wheat) (Anonymous, 2000a).
The next step after tempering is grinding. This stage generally consists of two
operations running in series and performed by the break system and reduction system
of the mill, in that order. The objective of the break system is to separate bran and
germ from endosperm. The break unit consists of several pairs of steel rollers called
break rolls. In each pair, the rolls spin in opposite directions and at different speeds
(Figure 2.2). As mill stocks (the ground material) passes from one pair of rollers to the
next, the gap between the rolls becomes smaller. Final products of the break system
are bran, germ, coarse endosperm (middlings), and break flour. The coarse endosperm
is then transferred to the reduction system to reduce the particle size to flour fineness.
The reduction unit also composes of several pairs of smooth steel rollers. Again, in
18
n in oppositee directions and at differrent speeds aand the gap
each pair, the rolls spin
between ro
ollers also deecreases as milling
m
stepss continue. E
End productss of the reduction
system aree shorts and reduction
r
flo
our. The bre ak and reducction flours are combineed to
obtain “strraight grade”” flour.
Figure 2.2: The milling
g of wheat (Anonymouss, 2000a)
o starch graanules are daamaged. Sofft wheat geneerally produces
During miilling, some of
less damag
ged starch th
han hard wheeat due to weeaker interacctions betweeen starch annd
protein as mentioned in
i Section 2.4. Soft wheaat flour conttains approxiimately 2-4%
% of
damaged starch
s
while hard wheat flour containns approxim
mately 6-12 % (Stauffer,
2007). Wh
hen soft wheat is milled, the separatiion occurs att the interfacce between thhe
starch gran
nules and thee protein maatrix yieldingg fewer dam
maged starch,, smaller flouur
particles, and
a fewer prrotein fragmeents on the sstarch granulle surface (B
Bechtel et al..,
2009). On the other haand, the sepaaration occurrs at the starcch granules for hard wheeat.
19
Consequently, hard wheat requires more energy to mill and yields larger flour particles
with a higher degree of damaged starch.
2.6 Wheat applications
The uses of wheat can be divided into four main categories: food, feed, seed, and
industrial (starch and gluten) (Curtis, 2002; Oleson, 1994; Orth and Shellenberger,
1988). Approximately two-thirds of wheat utilization worldwide is for food purposes
(Oleson, 1994). As food, wheat is consumed in various forms such as breads, cookies,
and noodles.
2.6.1 Bread
Bread was one of the first processed foods and has become a staple foodstuff in many
countries across the world (Zhou and Therdthai, 2006). Various types of bread require
different ingredients and processing methods which result in different in shapes, sizes,
textures, colors, and flavors. Based on the presence or absence of leavening agents,
bread can be divided into leavened and unleavened breads (Zhou and Therdthai,
2006). Leavened bread is made from dough containing leavening agents such yeast or
baking powders. The leavening agents provide carbon dioxide making the dough to
rise. Unleavened bread is made from dough without leavening agents. Consequently
the bread is dense and flat.
20
Yeast-leavened bread is popular in almost all across the world. The basic ingredients
for yeast-leavened bread are flour, water, yeast, and salt. Hard wheat is preferred for
bread making as it generally has higher protein content, stronger gluten, and more
damaged starch. Gluten proteins in wheat flour have significant impact on bread
quality. The proteins have unique viscoelastic properties which can form a protein
network and thus retain the gas produced by yeast. Flour with high protein content and
stronger gluten gives bread with high loaf volume, fine and uniform crumb grain, and
strong and more extensible bread crumb (Aamodt et al., 2004; Zghal et al., 2001). The
degree of damaged starch in flour also affects bread quality. Damaged starch granules
absorb up to twice as much water as their own weight while intact starch granules
absorb approximately 33-40% of their weight (Cauvain and Young, 2001; Manley,
2000). Damaged starch granules are also more susceptible to enzymatic digestion than
intact granules (Carson and Edwards, 2009). During bread making, damaged starch is
more readily hydrolyzed and releases more simple sugars which will be fermented into
CO2 and alcohol by yeast. Flour with an optimum degree of damaged starch is desired
and the optimum level is dependent upon flour usage, flour protein content, -amylase
activity, and the processing method (Dubat, 2004). The level of damaged starch should
be high enough to support yeast activity but not too high to cause problems such as
sticky dough, excess gas production, and an undesirable red crust color (Barrera et al.,
2007; Carson and Edwards, 2009; Dubat, 2004).
21
Water is the second largest ingredient in bread making. It helps in dispersion and
hydration of the ingredients. Hydration of gluten in flour is particularly important. As
the proteins hydrate during mixing and are mechanically worked in the mixing
process, a gluten network starts forming. The gluten network is the basis of a dough’s
strength and extensibility and gas holding properties. Water is also important for
gelatinization of starch granules during baking (Cauvain and Young, 2003).
Gelatinization of the wheat starch occurs at 60-70°C. (Swanson, 2004).
Salt (NaCl) is added in yeast-leavened bread for three major purposes: flavor, control
of fermentation, and strengthening of gluten (Swanson, 2004). The basic functions of
salt are to provide flavor to bread and to control the fermentation process by slowing
yeast activity. In addition, salt increases mixing time due to toughing of gluten. Salt
helps extend shelf-life of bread because of its antimicrobial property (Swanson, 2004).
A basic bread making process contains three major steps: mixing, fermentation (or
proofing), and baking (Dobraszczyk et al., 2006). Mixing is the first step of bread
making. All the ingredients are mixed to obtain a homogeneously hydrated matrix.
When the flour is hydrated and subject to the energy of mixing, gluten development
occurs. The gluten network traps air incorporated during mixing. Mixing is continued
until the dough reaches its optimum development, often described as the peak in the
Mixograph mixing curve. The dough is then allowed to sit in bulk for fermentation.
22
During fermentation, carbon dioxide produced by yeast expands the gas cells formed
during mixing, giving volume to the dough. Commonly the dough is then divided and
pre-shaped, allowed to rest again to allow the gluten to relax, and then shaped into its
final form. A further period of fermentation (“proofing”) then ensues, giving the shape
additional volume and further letting the dough relax so that it can rise in the oven.
The dough is transferred to an oven at the end of fermentation for baking. Baking
transforms the raw dough into bread. The dough expands rapidly at the early stage of
baking because of the heat that accelerates yeast and enzymatic activities and the gases
also expand as the system’s temperature increases. This is called oven rise or oven
spring (He and Hoseney, 1991). As the dough temperature increases further, yeast and
enzymes are inactivated. Other changes such as moisture evaporation, crust formation,
starch gelatinization, protein coagulation, and both caramelization and Maillard
reactions also occur during baking, the latter two on the crust only. After baking, bread
is cooled to let the steam and alcohol to escape. Bread is then ready for packing and
consumption.
2.6.2 Cookies
Cookies are sweet baked snacks that have high sugar and fat contents and a low
moisture content (Maga, 2000). Their main ingredients are wheat flour, sugar, fat, and
egg. Cookies are generally made from soft wheat flour that has low protein content,
weak gluten, and low levels of damaged starch, and low water absorption. High water
23
absorption is undesirable for cookies because it increases baking time and causes high
cookie dough stiffness and smaller cookie diameters (Donelson and Gaines, 1998;
Gaines et al., 1988). Sugar is generally added to the cookie for sweetness, flavor, and
color via caramelization and Maillard reactions. As a hardening agent, sugar also
affects cookie texture. It gives cookies a crisp and firm texture due to rearrangement of
the sucrose molecules from crystal form to amorphous form (Townsend, 2001). In
addition, sugar acts as a preservative. It reduces water activity of cookies and
consequently prolongs their shelf-life. Fat provides tenderness (shortness) by coating
the flour particles and preventing water from interacting with flour proteins (Lai and
Lin, 2006; Townsend, 2001). Consequently, gluten development does not fully occur
and other ingredients are not strongly bound together. The high content of fat and
sugar gives cookie dough plasticity and cohesiveness without forming a strong gluten
network (Lai and Lin, 2006; Pena, 2002). Egg acts as a binder, emulsifier, and
leavening agent (Townsend, 2001). It helps to hold other ingredients together. Egg
also helps to retain air during mixing which in turn gives volume to the cookies.
Basic cookie making consists of three major steps: mixing, forming, and baking. The
main objective of mixing is to incorporate and evenly distribute ingredients
(Townsend, 2001). Based on the amount of water required to make cookie dough,
there are two types of doughs: short and hard doughs. Each dough type requires a
different mixing process. Short doughs contain high fat and sugar contents but low
24
water content. Due to the low water level, short doughs require a two-stage mixing
process (Townsend, 2001). In the first stage, all ingredients except flour are mixed
together. In this stage, both sugar and water are in a small droplet form in the fat. This
allows most of the sugar to be dissolved and prevents competition for water among
sugar, starch, and flour protein when the flour is added in the second stage.
Consequently, gluten development does not occur. Examples of short dough cookies
are wire-cut cookies, extruded cookies, and deposited cookies (van Benschop and
Hille, 2010). Hard doughs or semi-sweet cookies generally have low fat and sugar but
higher water contents than short doughs. The amount of water used to make hard
doughs is sufficient to dissolve all the sugar. Hard doughs require gluten development
to occur during mixing to obtain tough and extensible characteristics which is essential
for further processing. Hard doughs therefore receive a single-stage mixing in which
all the ingredients are mixed together in one step (Townsend, 2001). Hard doughs are
mixed at a higher speed and a longer time. This also helps to dissolve sugar and to
promote a gluten network formation. Examples of hard dough cookies are Marie
biscuit and Petit beurre (van Benschop and Hille, 2010).
After mixing, the dough is then formed into pieces. A method used to form the dough
mainly depends on the type of dough. Very soft short doughs that contain high fat and
sugar levels are generally formed by an extruding machine. Stiffer short doughs are
better formed by a molding machine. Hard doughs which are extensible and elastic are
25
formed by first rolling the dough into a thin sheet and then cutting the dough into
shapes.
Next, the dough pieces are transferred into an oven for baking. At the beginning of
baking (~40C), fat starts to melt. As the cookie temperature is rising (~60C),
leavening agent in the cookie dough starts to release gas causing the dough to expand
(Townsend, 2001). Thereafter, starch gelatinization occurs and cookie structure is
formed. At temperature above 100C, moisture content in cookies is lowered.
Caramelization and Maillard reactions occur causing a dark color on the crust. After
baking, cookies are allowed to cool down and then ready for packing.
2.6.3 Asian noodles
Wheat-flour noodles are one of the staple foods of Asia. Asian-style noodles are
different from pasta (Western-style noodles). Asian noodles use common wheat flour
as the main ingredient to make noodle dough, which then is sheeted thinly and cut into
strips. In contrast, pasta is produced by extrusion of dough made from semolina
(coarse flour of durum wheat). Basic noodle processing includes mixing of
ingredients, sheeting of the dough, and slitting the dough into strands. There are
various types of Asian noodles that are different in size of noodle strands, appearance,
ingredients, chemical properties, and processing methods. According to Fu (2008),
26
Hou (2001), Hou and Kruk (1998), and Nip (2007), Asian noodles can be classified
into groups based on raw material, salt composition, size, and processing method.
Raw materials: Chinese-style wheat-flour noodles are generally made from hard wheat
flour with higher protein content. They have bright creamy white or bright yellow
color and firm texture. In contrast, Japanese-style wheat-flour noodles are made from
soft wheat flour with medium protein content. They have creamy white color and a
soft and elastic texture.
Salt composition: Depending on the type of salt used, noodles can be grouped into two
categories: white salted noodles or yellow alkaline noodles. White salted noodles are
basically made from flour, water, and table salt (NaCl). They have a soft elastic
texture and smooth surface with white to creamy white color (Nagao, 1991). White
salted noodles are largely consumed in China, Japan, and Korea but they only have a
small market in Southeast Asia (Fu, 2008). Most Chinese prefer firm texture noodles
while Japanese and Koreans prefer soft texture noodles (Crosbie and Ross, 2004b;
Huang, 1996). Yellow alkaline noodles are similar to white salted noodles but use
alkaline compounds instead of table salt. The most commonly used alkaline
compounds are sodium carbonate (Na2CO3), potassium carbonate (K2CO3), or a
mixture of the two. The use of alkaline compounds gives the noodles unique aroma
and flavor, a yellow color and a firm, elastic texture (Fu, 2008).From their origin in
27
the southern part of China, yellow alkaline noodles have been introduced and widely
consumed in Southeast Asia (Fu, 2008).
Size: Asian noodles can be classified into groups based on width and thickness of
noodle strands. Based on the width of the noodle strands, Japanese noodles can be
divided into four groups: So-men (0.7-1.2 mm), Hiya-mughi (1.3-1.7 mm), Udon (1.93.8 mm), and Hira-men (5.0-6.0 mm) (Hou and Kruk, 1998).
Processing methods: Based on the processing method, Asian noodles are categorized
into two groups: hand-made or machine-made. Hand-made noodles can be made by
stretching and pulling noodle dough repeatedly to produce long noodle strands with
the same thickness (Dexter, 2004). Hand-made noodles have a soft, smooth, elastic,
and chewy texture and are usually cooked immediately after preparation. Due to high
demand for noodles, noodle machines have been created. However, the similar
principle of noodle production (mixing, sheeting, and cutting into strands) is still used
in machine-made noodles. The fresh machine-made noodles can be sold immediately
or processed further to produce different kinds of noodles (dried, frozen, boiled or
steamed) (Hou and Kruk, 1998).
28
Optimum water addition to noodle doughs: Noodle quality is significantly influenced
by the amount of water used in a formula. High-quality noodles are generally prepared
within a narrow range of water addition (Oh et al., 1986). Adding an excessive or
inadequate amount of water can affect color, and wet and dry strengths of noodles, and
cause production problems (Fu, 2008; Oh et al., 1986; Park and Baik, 2002). The
amount of water required for noodle processing depends on flour properties (protein
content especially: (Park and Baik, 2004; Park and Baik, 2002)), type of product,
formula, processing equipment, and processing conditions (Oh et al., 1986).
The only method currently used to determine optimum water addition for noodle
making is hand-feel from an experienced noodle maker. There is no objective method
available to determine optimum water addition for noodle making. The Mixograph
and Farinograph are generally used to determined dough and gluten properties, and
optimum water absorption for bread making. Both methods have been proposed and
used to determine optimum water addition for noodle making (Kruger, 1996; Lee et
al., 1998; Oh et al., 1986; Ohm et al., 2006; Park et al., 2010). The Mixograph and
Farinograph are mainly affected by and positively correlated with protein content and
composition. Because noodle making requires significantly less water than bread
making, the gluten may not be developed properly (Park and Baik, 2002).Therefore,
both methods designed for the determination of water absorption for bread making
may not be suitable for determining water absorption for noodle making. In addition,
29
the Farinograph is a labor intensive and time consuming method (Hou, 2010). Azudin
(1998) and Hatcher et al. (2002) used a sieving test to determined optimum water
addition to noodle dough. To determine an optimum water addition, noodle crumbs
are placed on a 3-mm sieve and gently shaken for three min. The retained crumbs and
passing through crumbs are weighted. At optimum water addition, the weights of both
portions are equal.
Lubricated squeezing flow (LSF) is a variant of a stress relaxation tests developed by
Chatraei et al. (1981) to study the biaxial extension viscosity of molten polymers in
creep. The principle of LSF is to apply an uniaxial compression to a cylindrical sample
between two lubricated parallel plates (Steffe, 1996). LSF have been applied to
various types of wheat doughs such as gluten (Janssen et al., 1996b), bread (Janssen et
al., 1996a; Kokelaar et al., 1996; Rouille et al., 2005; Sliwinski et al., 2004), and
cookie (Lee and Inglett, 2006; Manohar and Rao, 1997; Manohar and Rao, 1999;
Manohar and Rao, 2002). Liao et al. (2007), Ong et al. (2010),and Ross and Ohm
(2006) also applied LSF to noodle dough. LSF could be an alternative method to
determine optimum water addition for noodle doughs.
30
2.7 Chemical composition of wheat
2.7.1 Carbohydrates
2.7.1.1 Starch
Starch is the most abundant carbohydrate found in wheat. It represents approximately
60-75% of wheat endosperm thus it is a predominant source of assimilable
carbohydrate in human diets (D’Appolonia and Rayas-Duarte, 1994; Saito et al.,
2010). As in other plant species wheat starch occurs naturally in small granules. There
are two types of wheat starch granules; large and small (Figure 2.3). The large
granules have a lens-like shape 20-35m long while the small ones are spherical and
2-10 m in diameter (Delcour and Hoseney, 2010b).
Figure 2.3: Scanning electron photomicrograph of wheat starch granules (Delcour and
Hoseney, 2010b)
31
Starch polymers are built from a single type of molecule, D-glucose (C6H12O6). The
glucose units are linked together through -1,4 and -1,6 glycosidic linkages creating
two types of polymers: amylopectin and amylose (Figure 2.4). Normal wheat starch
contains approximately 75% amylopectin and 25% amylose. Amylopectin is a highly
branched polymer consisting of 300,000-3,000,000 glucose units per molecule
connected through -(14) and -(16) linkages (Atwell, 2001). In contrast,
amylose is considered to be a relatively linear polymer containing approximately 500200,000 glucose units per molecule linked together via -(14) linkages (Edwards,
2007). Each individual amylose and amylopectin molecule contains only one reducing
end where the first carbon atom (C1) of glucose is not bound to another glucose
(Atwell, 2001; Delcour and Hoseney, 2010b).
Figure 2.4: Molecular structure of (a) amylose; and (b) amylopectin (Pérez et al.,
2009)
Starch composition influences the quality of end products made from wheat flour. For
example, the quality of Japanese udon noodles is negatively correlated to the amylose
32
content of flour (Oda et al., 1980). The amylose content in wheat flour is genetically
controlled by three waxy (Wx) genes, Wx-A1, Wx-B1, and Wx-D1, located on
chromosomes 7A, 4A, and 7D, respectively (Saito et al., 2010; Yasui, 2006). These
genes regulate production of Wx-A1, Wx-B1, and Wx-D1proteins also known as
granule-bound starch synthase I (GBSSI). GBSSI is an important enzyme in starch
synthesis and responsible for the synthesis of amylose (Echt and Schwartz, 1981;
Merida et al., 1999).The absence of Wx protein(s) results in lower amylose content in
starch (Nakamura et al., 1993; Yamamori et al., 1994). Waxy starch is amylose free
due to the lack of all three Wx proteins. Partially waxy starch is starch that has
amylose content between normal and waxy starch.
Amylose can take up double or single helical conformations. The double helix with
another amylose molecule is a crystalline form (also possible with amylopectin
terminal chains >~ 25 glucose units). In the single helix form there is a central
hydrophobic core that allows amylose to form complexes with mono- and
diglycerides, and free fatty acids and their salts. These complexes can slow down the
retrogradation rate of starch (Gunaratne and Corke, 2004; Lineback and Rasper, 1988;
Tainter, 2000) by limiting its ability to form double helix structures. When starch is
stained with iodine solution, a blue color develops due to poly-iodide ions (> 1 iodine
ion in the molecule) in the center of the amylose helical structure (Delcour and
Hoseney, 2010b).
33
Starch granules are semicrystalline. The clustered branches of amylopectin molecules
are in the crystalline form while the amylose molecules and amylopectin branched
regions are in an amorphous form (D’Appolonia and Rayas-Duarte, 1994). Normal
starch has a degree of crystallinity about 27-36% while waxy starch has a higher
degree of crystallinity, 37-45% (Fujita et al., 1998). Starch granules have a high
degree of molecular order consequently they are able to bend plane polarized light and
reveal birefringence or an extinction cross (Delcour and Hoseney, 2010b).
Intact starch granules are generally insoluble in cold water. They slightly and
reversibly absorb water and swell but remain as granules (BeMiller and Whistler,
1996; Vaclavik and Christian, 2008).When starch granules in excess water
(water:starch ratio > 1.5) are heated, they will undergo an irreversible process called
gelatinization in which the granules lose their internal order (BeMiller and Whistler,
1996; Buleon and Colonna, 2007). Several changes in granule properties occur in this
process including diffusion of water, swelling of granules, loss of birefringence,
melting of crystallites, and leaching of soluble components (mainly amylose)
(D’Appolonia and Rayas-Duarte, 1994; Dias, 1999; Gunaratne and Corke, 2004).
Starch gelatinization occurs over a broad temperature range. The initial gelatinization
temperature (first loss in birefringence), and the temperature range that gelatinization
occurs depend on the starch:water ratio, granule type, homogeneity of the granules,
and presence of other components such as sugar and lipid, which increase
34
gelatinization temperatures (BeMiller and Whistler, 1996; Eliasson and Tatham,
2001).
Further heating of starch granules results in more release of starch components and
additional swelling of granules until they collapse, particularly with application of
shear force (Atwell et al., 1988). This phenomenon is called pasting which produces a
viscous mass (starch paste) due to granule swelling and disruption. Pasting does not
occur until the temperature is beyond the gelatinization temperature, therefore pasting
is a consequence of gelatinization (Batey, 2007).
Upon cooling and storage, unordered starch polysaccharides (amylose and
amylopectin) may re-associate into an ordered structure via crystalline junction zones
(zones where two chains are joining together via hydrogen bonds) (Atwell et al., 1988;
Tainter, 2000). This process is called retrogradation which generally is undesirable
and more likely to occur in high amylose starch. The junction zones will continue to
grow and may form a three-dimensional structure which leads to gelation or
precipitation depending on several factors such as starch concentration, and cooling
rate (Lineback and Rasper, 1988; Tainter, 2000). As the junction zones continue to
grow, water entrapped in the system will be released. This process is called syneresis.
35
2.7.1.2 Nonstarch polysaccharides
Nonstarch polysaccharides are a group of carbohydrates found in a plant cell wall
which include cellulose, hemicelluloses [e.g. arabinoxylans (AX) and β-glucans],
lignin, and pectin (Zhang and Hamaker, 2010). These carbohydrates are indigestible
by enzymes in the human gastrointestinal tract so they are considered to be dietary
fiber (Lineback and Rasper, 1988). The major NSP present in wheat grains are AX, glucan, cellulose and, arabinogalactan-peptides (AGP) (Atwell, 2001; Lineback and
Rasper, 1988; Stone and Morell, 2009; Van Der Borght et al., 2005).
2.7.1.2.1 Arabinoxylans
Arabinoxylans (AX) are the major NSP found in the cell walls of wheat kernels and
make up approximately 4-8% of the dry matter (dm), depending on cultivar, and
seasonal and environmental conditions (Finnie et al., 2006; Gutierrez-Alamo et al.,
2008; Hong et al., 1989; Saulnier et al., 2007). AX mainly consist of the pentoses
(five-carbon sugars), arabinose and xylose, and for this reason AX are often called
pentosans (Saulnier et al., 2007). The basic structure of AX consist of backbone chains
of D-xylopyranose units connected by -(14) glycosidic linkages with some -Larabinose side chains (Figure 2.5). The arabinose units attach randomly at the O-2 or
O-3 position of the xylan backbones. However, some other side chains such as acetic
acid, ferulic acid, p-coumaric acid, galactose are also present, the exact substituents
36
depending on the tissues of origin (Atwell, 2001; Saulnier et al., 2007; Stone and
Morell, 2009).
Figure 2.5: Chemical structure of unsubstituted xylan residue (a), xylan residue
substituted at O-2 with an arabinose residue (b), xylan residue substituted at O-3 with
an arabinose residue (c), xylan residue substituted at O-2 and O-3 with arabinose
residues. Structure c shows the link of ferulic acid to C-5 of an arabinose residue
(Delcour and Hoseney, 2010a).
Substituted xylan molecules are more extended, stiffer, and more water soluble than
unsubstituted xylan molecules (Pitkanen et al., 2009; Sternemalm et al., 2008). It was
reported that debranching AX with mild acid or -L-arabinosidase would increase
chain-chain interactions between AX molecules causing aggregation and precipitation
of the xylose backbones (Andrewartha et al., 1979; Courtin and Delcour, 2002; Dea et
al., 1973; Lineback and Rasper, 1988). The arabinosyl side chains prevent aggregation
37
of the unsubstituted xylan molecules consequently they are more water soluble
(Shelton and Lee, 2000). The arabinose to xylose (A/X) ratio is used to determine the
shape and solubility of AX (Saulnier et al., 2007; Wang et al., 2006). However, other
factors such as molecular weight and interactions with other cell wall macromolecules
such as proteins, cellulose, and lignin also influence water solubility of the AX
(Saulnier et al., 2007).
Based on their water solubility, AX can be divided into two groups: water-extractable
AX (WE-AX) and water-unextractable AX (WU-AX).WE-AX are present smaller
amounts while WU-AX make up the majority of AX found in wheat (Maes and
Delcour, 2001; Saulnier et al., 2007). WE-AX are extractable with cold water while
WU-AX are extractable with alkaline solutions (Liukkonen et al., 2006). As noted
above that the number of arabinosyl side chains promotes water solubility of AX,
therefore WE-AX would have had higher A/X ratio than WU-AX. Composition
analysis however revealed that WU-AX had higher A/X ratio than WE-AX
(Izydorczyk and Biliaderis, 1995; Philippe et al., 2006). Even though WU-AX have
higher A/X ratio than WE-AX, WU-AX are water insoluble due to diferulic bridges
formed by ferulic acid residues at the at O-5 position of arabinose units between
chains (Fausch et al., 1963; Saulnier et al., 2007; Shelton and Lee, 2000).
38
Both bran and endosperm are rich in AX. However, bran contains significantly higher
levels of AX (12-25%) than endosperm (~ 2 %) (D’Appolonia and Macarthur, 1976;
Dornez et al., 2006). Within the bran, pericarp contains approximately 38% of the total
wheat bran AX while nucellar epidermis, aleurone, and seed coat contain 25, 25, and
2%, respectively (Benamrouche et al., 2002). The A/X ratio of the bran AX extracted
from different bran layers can vary from 0.1-1.2; pericarp AX (1.1-1.2), nucellar
epidermis AX (0.1), and aleurone AX (0.3-0.5) (Antoine et al., 2003; Barron et al.,
2007).
In wheat flour, the average content of AX is 2% from which 75% is WU-AX and 25%
is WE-AX. WU-AX are tightly bound to the inside of wheat endosperm cell walls
while WE-AX are loosely bound to the outside (Barron et al., 2006; Courtin and
Delcour, 2002; Guttieri et al., 2008; Mares and Stone, 1973; Philippe et al., 2006).
AX are hydrophilic and tend to compete for water with other constituents in food
system via hydrogen-bonding (Finnie et al., 2006). WU-AX and WE-AX are able to
retain water 7-10 and 4-6 times their weight, respectively (Courtin and Delcour, 2002;
Finnie et al., 2006). AX containing ferulic acid can form gels that hold water up to 100
g/g of AX polymer upon the addition of oxidizing agents (Courtin and Delcour, 2002;
Izydorczyk et al., 1990). These 3-dimensional gel networks result from cross-linking
between aromatic rings of ferulic acid units of adjacent AX polymers (Figueroa-
39
Espinoza and Rouau, 1998; Finnie et al., 2006; Geissmann and Neukom, 1973;
Saulnier et al., 2007).
AX have a significant impact on wheat processing and qualities of wheat products. In
breads, WE-AX have positive effects while WU-AX have negative effects (Courtin et
al., 1999). Adding WE-AX into semolina flour resulted in increased water addition
and decreased dough strength (Michniewicz et al., 1992; Turner et al., 2008).
Development time and viscosity of dough were increased with increasing AX content
(Biliaderis et al., 1995; Jelaca and Hlynka, 1971; Wang et al., 2003a). Starch
retrogradation rate and specific loaf volume was higher in AX-supplemented breads
(Biliaderis et al., 1995; Denli and Ercan, 2001; Michniewicz et al., 1992). Turner et al.
(2008) observed a decrease in stickiness of cooked AX-enriched pasta.
Kongraksawech et al. (2010) and Ong (2007) showed that AX is co-extracted with
glutenin macropolymer (GMP) and that AX in GMP extracted from alkaline noodle
doughs was significantly higher in concentration than AX in GMP extracted from the
salted noodle doughs.
2.7.1.2.2 glucan
-glucan is a linear, unbranched polysaccharide found in the cell walls of many cereal
grains such as barley, oats, and wheat (Chawla and Patil, 2010). -glucan is present in
40
large quantities in barley (5-11% w/w) and oats (2-9%) while only small quantities are
present in wheat (< 1%) (Beresford and Stone, 1983; Lineback and Rasper, 1988;
Tiwari and Cummins, 2009; Welch et al., 2000). In wheat, -glucan is mainly located
in aleurone layer (29%) and starchy endosperm (20%) (Bacic and Stone, 1981a; Bacic
and Stone, 1981b).Wheat flour contains less than 0.5% -glucan (Lineback and
Rasper, 1988).
-glucanis composed entirely of D-glucose units primarily linked together with one (13) linkage for every three or four -(14) linkages (Figure 2.6) (BeMiller and
Whistler, 1996). -glucan is considered to be a soluble dietary fiber due to the
presence of -(13) linkages that prevent -glucan molecules from packing closely
together (Chawla and Patil, 2010; Grimm et al., 1995). -glucan has a long, cylindrical
shape consisting of about 250,000 glucose units with average molecular weight about
487 kDa (Chawla and Patil, 2010; Li et al., 2006). -glucan is capable of forming
highly viscous solutions at relatively low concentration (> 0.3%) and the viscosity
increases as molecular weight and concentration of -glucan are increased (Anttila et
al., 2004; Doublier and Wood, 1995).
41
Figure 2.6: The primarry structure of the -gluccan (Chawlaa and Patil, 22010)
2.7.1.2.3 Cellulosee
Cellulose is
i a long, lin
near polymerr consisting oof thousandss of D-glucoose moleculees per
chain linkeed together via
v -(14)) glycosidic bbonds (Figurre 2.7) (BeM
Miller and
Whistler, 1996;
1
Shelto
on and Lee, 2000).
2
Due tto the absencce of side chhains, celluloose
molecules self-associaate and arran
nge in flat, ribbbon-like, riigid structures maintaineed by
hydrogen bonds
b
and van der Waalls interactionns (Stone andd Morell, 20009).
Consequen
ntly, cellulosse is water in
nsoluble. Ceellulose is paartially crystaalline because it
is composeed of both hiighly ordereed and non-oordered strucctures (Sheltoon and Lee,
2000).
Figure 2.7: Chemical structure
s
of cellulose
c
(Beelitz et al., 22008)
42
Cellulose is a structural component present in walls of all cells of kernel tissues but it
is primarily found (up to 30% w/w) in the pericarp (Ring and Selvendran, 1980; Stone
and Morell, 2009). Total cellulose content in a whole grain is about 2% (dm) while
less than 0.3% is found in endosperm (Fraser and Holmes, 1959; Stone and Morell,
2009). Cellulose content in wheat flour increases as extraction rate increases.
Therefore patent flour, flour composed of the innermost part of the endosperm but free
of bran and germ, contains little cellulose (Lineback and Rasper, 1988).
2.7.1.2.4 Arabinogalactan-peptides
Arabinogalactan-peptides (AGP) are water-extractable polysaccharides composed of
polysaccharide molecules (92-94%) covalently linked to a peptide backbone (6-8%)
(Van Der Borght et al., 2005). The peptide backbone consists of 15-20 amino acids,
particularly hydroxyproline, alanine, and glutamic acid/glutamine (Van den Bulck et
al., 2002). The AGP molecules have -L-arabinose to D-galactose ratio vary between
0.66 and 0.73 (Loosveld et al., 1998a). D-galactose units form backbone chains via β(16) glycosidic linkages with arabinose and galactose side chains at the 3-position
(Van den Bulck et al., 2002). The structure of AGP slightly varies from different
wheat varieties or within variety (Loosveld and Delcour, 2000).
43
Wheat flour contains approximately 0.27-0.38% (dm) of AGP (Loosveld et al.,
1998a). A study on effects of AGP in breadmaking found that substitution flour (12%) with AGP decreased baking absorption, dough extensibility, and loaf volume
(Loosveld and Delcour, 2000). However, there was no change in structure or
extractability of AGP during breadmaking (Loosveld et al., 1998b). Unlike WE-AX,
adding peroxidase or hydrogen peroxide does not affect the viscosity of AGP solutions
(Stone and Morell, 2009).
2.7.2 Proteins
Wheat flour generally contains 6-20% protein depending on the variety and
environmental conditions where wheat is grown (Dobraszczyk, 2001; Finney and
Barmore, 1948). Wheat kernel proteins can be classified into two groups: non-gluten
and gluten-forming proteins. Non-gluten proteins refer to proteins that are soluble in
water and dilute (0.5-1.0 M) NaCl solutions (Mills et al., 2003; Osborne, 1907). This
group of proteins includes albumins and globulins which comprise only 10-15% of the
flour proteins (Swanson, 2004). Albumins are soluble in water. Globulins are soluble
dilute NaCl solutions. Both albumins and globulins are considered to be “monomeric”
proteins (i.e. non-aggregating) with molecular weight range of 20-30 kDa (MacRitchie
and Lafiandra, 1997).
44
The gluten-forming proteins account for about 80-85% of total protein in wheat flour
(Swanson, 2004; Veraverbeke and Delcour, 2002). They are sometimes called
prolamins due to their high contents of glutamine and proline. The gluten-forming
proteins consist of two major types varying in size, molecular weight and solubility:
gliadin and glutenin (Dobraszczyk, 2001). When hydrated and mechanically worked
the gluten proteins provide unique viscoelastic properties which are not found in the
proteins of other cereals. Gliadins are the main contributors to gluten viscosity (flow).
Glutenins are the main contributors to gluten elasticity (Figure 2.8) (Ciaffi et al., 1996;
Mills et al., 1990).
Figure 2.8: Rheological behavior of gliadin and glutenin (Wrigley et al., 2006)
2.7.2.1 Gliadins
Gliadins make up about 40-50% of the total protein content of wheat (Qi et al., 2006).
They are soluble in 70% (v/v) ethanol (Thewissen et al., 2011; Weegels et al., 1996a).
45
Gliadins are also considered to be monomeric proteins and have a molecular weight
range of 30-80 kDa (MacRitchie and Lafiandra, 1997). Based on their mobility in acid
gel electrophoresis, gliadins can be classified into four groups: -, -, - and gliadins (MacRitchie and Lafiandra, 1997; Woychik et al., 1961). In gel
electrophoresis, -, - and -gliadins overlap each other because their molecular
weights are relatively close while -gliadins are well distinguished due to their high
molecular weight (70-80 kDa) (MacRitchie and Lafiandra, 1997). Due to the similarity
of - and -gliadins, they are sometimes grouped into one class, -gliadins (Kuktaitė,
2004). -, - and -gliadins are comparable to each other in amino acid composition
and are categorized as sulfur-rich prolamins because they contain a high number of
cysteine residues. On the other hand, -gliadins are considered sulfur-poor prolamins
because they contain no cysteine residues and at most one methionine (Tatham and
Shewry, 1995). Consequently, there are no disulfide bonds in -gliadins (MacRitchie
and Lafiandra, 1997; Shewry et al., 1986). Although the -, - and -gliadins contain
sulfhydryl groups, the disulfide links exist mainly intra-chain which make them
monomeric proteins.
The production of - and many -gliadins is regulated by Gli-A1, Gli-B1, and Gli-D1
genes on the short arm of group 1chromosomes (Branlard et al., 2001). While the
synthesis of -, - and some -gliadins are controlled Gli-A2, Gli-B2, and Gli-D2,
46
respectively which are located on the short arm of group 6 chromosomes (Branlard et
al., 2001).
2.7.2.2 Glutenins
Glutenins account for 30-45% of the total protein in wheat (Cornell, 2003). They are
soluble in dilute acid or alkali solutions (Osborne, 1907). Glutenins are considered to
be “polymeric” proteins as a result of their ability to aggregate, primarily via intermolecular disulfide bonding (see below). When isolated into individual subunits by
chemical reduction to break disulfide bonds the glutenin subunits can be separated into
two fractions: low molecular weight glutenin subunits (LMW-GS) and high molecular
weight glutenin subunits (HMW-GS). LMW-GS have molecular weights range from
30-55 kDa. HMW-GS have molecular weights range from 80-160 kDa (MacRitchie
and Lafiandra, 1997; Payne et al., 1980). The LMW-GS are closely related to gliadins (Tatham et al., 1990). LMW-GS and the HMW-GS can be linked via both
inter- and intra-molecular disulfide bonds forming very large polymeric proteins
which could have molecular weights up to several million Da (MacRitchie and
Lafiandra, 1997).
Glutenin polymeric proteins are studied extensively because they are crucial in
determining wheat flour quality, baking quality, and dough properties. Regarding the
extraction methods, glutenin polymers can be obtained in a semi-solid form as
glutenin macropolymer (GMP) or a solution form as unextractable polymeric proteins
47
(UPP). GMP is a fraction of aggregated glutenin proteins which is insoluble in 1.5%
(w/v) sodium dodecyl sulfate (SDS) solution and formed as a gel layer after
ultracentrifugation of a wheat flour in SDS solution (solid:liquid ratio = 1:14.5) (Don
et al., 2003a). UPP are polymeric glutenin proteins which are insoluble in anonreduced mixture of flour and 0.5% (w/v) SDS solution (solid:liquid ratio = 1:100) and
are subsequently solubilized by sonication.
Higher glutenin and GMP contents in wheat flour are associated with increased dough
firmness and mixing time (Weegels et al., 1996b). It is known that the gluten network
is formed gradually during mixing. However, there was also a decrease in the amount
of GMP during mixing due to the loss of HMW-GS (Don et al., 2003b; Skerritt et al.,
1999a; Weegels et al., 1997). The decrease GMP content was faster for dough made
from weak gluten flour compared to dough made from strong gluten flour (Jood et al.,
2001). The amount of UPP in wheat flour can be used to predict dough properties by
calculating in terms of percentage unextractable polymeric protein (%UPP) in total
polymeric protein (Field et al., 1983; Gupta et al., 1992). High %UPP values indicate a
large portion of high-molecular-weight insoluble glutenin polymer in SDS
(MacRitchie and Singh, 2004). Dough with high %UPP value will likely have better
elasticity and require a longer mixing time than dough with low %UPP value (Gupta
et al., 1993). Since the amount of UPP is dependent on the genetic composition of
48
flour proteins, different wheat varieties contain different quantities of UPP (Kuktaite
et al., 2004).
Genes coding for HMW-GS are located at Glu-A1, Glu-B1, and Glu-D1 loci on the
long arms of the group 1 chromosomes (Bekes et al., 2004). These protein subunits are
designated with numbers (e.g. 1, 2, and 2*) and regularly occur in pairs (e.g. 2+12,
5+10, and 17+18). The HMW-GS of l, 2* and 5+10 are associated with stronger
dough, while 2+12 are associated with weaker dough (Radovanovic et al. 2002).
Genes coding for LMW-GS are located at Glu-A3, Glu-B3, and Glu-D3 loci on the
short arms of the group 1 chromosomes (Singh and Shepherd, 1985).
2.7.3 Lipids
Lipids are minor constituents distributed throughout the whole grain (Table 2.2) in the
form of oil droplets or spherosomes (membrane-bound oil droplets) (Cornell, 2003).
The wheat grain contains lipid about 2-4% (w/w, dm) of whole grain weight and
approximately one-third of the wheat total lipid content is in the germ section which is
the smallest fraction in wheat (Chung and Ohm, 2000; Morrison, 1978b; Wrigley et
al., 2009). Endosperm, the largest section in wheat kernel, contains 1-2.5% lipid by
weight (Morrison, 1978b; Wrigley et al., 2009).
49
Table 2.2: Crude fat content in wheat kernel (Morrison, 1978a)
Tissue
Proportion of whole kernel (%) Crude fat (%)
Whole grain
100
2.1-3.8
Bran
N/A
5.1-5.8
Pericarp
5.0-8.9
0.7-1.0
Testa, hyaline
0.2-1.1
0.2-0.5
Aleurone
4.6-8.9
6.0-9.9
Endosperm
74.9-86.5
0.78-2.2
Scutellum
1.1-2.0
12.6-32.1
Embryonic axis
1.0-1.6
10.0-16.3
According to their structure and polarity, lipids can be classed as nonpolar and polar.
Wheat nonpolar lipids mainly consist of glycerides, free fatty acids, and sterol esters
(Wrigley et al., 2009). Glycerides are esters of glycerol with one, two, or three fatty
acids and termed mono-, di- and triglycerides, respectively. Nonpolar lipids are
dominated by triglycerides accounting for 50% of the total nonpolar lipids (Wrigley et
al., 2009). Fatty acids, the simplest form of lipids, are made up of carboxylic acids (COOH) and long chain hydrocarbon groups. The common fatty acids in wheat are
palmitic (16:0), stearic (18:0), oleic (18:1, n-9), linoleic (18:2, n-6), -linolenic (18:3,
n-3) (Table 2.3). About 75-80% of fatty acids in wheat are unsaturated with high level
of the unsaturated linoleic acid (18:2, n-6) (Chung et al., 2009; Morrison, 1988;
Morrison, 1994).
50
Table 2.3: Distributions of fatty acids in wheat (Morrison, 1994)
Fatty acid (wt. %)
16:0 18:0 18:1 18:2 18:3
Whole grain, total 17-24 1-2 18-21 55-60 3-5
Germ, total
18-24
2
8-17 54-57 4-9
Endosperm
18
1
19
56
3
Flour
Nonstarch, total
16-21 <2 12-13 60-66 4-5
a
Nonstarch, NL
17-19 1-2 14-15 60-62 4-5
a
12-14 1-2
8-9 71-74 4-5
Nonstarch, GL
a
Nonstarch, PL
23-27
1
9-10 59-63 2-4
Starch lipid
35-44 <2
1-14 44-52 1-4
Lipid source
a
NL, GL, and PL are nonpolar lipids, glycolipids, and phospholipids, respectively
The major constituents of polar lipids are glycolipids (lipids containing sugar groups,
in wheat mainly galactose and glucose) and phospholipids (lipids containing
phosphate groups) (Delcour and Hoseney, 2010b; Ito et al., 1983; McDonnell et al.,
1982; Wrigley et al., 2009). The majority of wheat glycolipids are galactolipids
(Chung et al., 2009). The principle galactolipids are di- and monogalactosyl
diglycerides (Chung et al., 2009). Phospholipids are fat derivatives containing a
phosphate group and one or more fatty acid residues. Phosphatidylcholine and
phosphatidylethanolamine are the major components of wheat phospholipids (Wrigley
et al., 2009).
Wheat grain lipids are composed of approximately 70% nonpolar lipids, 20%
glycolipids, and 10% phospholipids (Delcour and Hoseney, 2010b). Germ lipids
mainly contain about 77-85% nonpolar and 13-17% polar lipids, in which triglycerides
51
and phospholipids are the major components, respectively (Delcour and Hoseney,
2010b; Morrison, 1988). The major bran lipids are nonpolar; diglycerides and free
fatty acids. Phospholipids are the principle polar lipids in bran. For endosperm lipids,
they can be classed into nonstarch and starch lipids (Delcour and Hoseney, 2010b).
The nonstarch lipids are lipids located in anywhere in endosperm but starch granules.
Nonstarch lipids are the major lipids in the endosperm (65%) and contain
approximately 60% nonpolar lipids, 25% glycolipids, and 15% phospholipids
(Delcour and Hoseney, 2010b; Wrigley et al., 2009). On the other hand, starch lipids
are minor constituents (35%) and consist of 9% nonpolar lipids, 5% glycolipids, and
86% phospholipids.
Even though lipids are present in small quantities in wheat, they have significant
impacts on quality and property of wheat products due to their amphipathic
characteristic (possessing both hydrophilic and hydrophobic properties) (Cornell,
2003). Lipids can interact with both proteins and starch and form inclusion complexes.
Lipids associate with gluten proteins and help stabilize the gas cell structure in dough
resulting in higher loaf volume and better texture.
2.8 Health benefits of wheat consumption
Among cereals, wheat is known as an important source of essential nutrients. It
provides energy, protein, carbohydrate, lipids, vitamins, and minerals. As an energy
52
source, wheat provides approximately 1220-1450 kJ/100 g (Rosell, 2003). Most of the
energy comes from carbohydrates in form of starch, which is 60-70% of the mass of
the whole grain (Shewry, 2009). Sugars (e.g. glucose and sucrose) and dietary fiber
(e.g. arabinoxylans, -glucan, and cellulose) are other types of carbohydrate present as
minor components in wheat. Whole-wheat is rich in insoluble fiber which is helpful in
prevention in colon cancer and other intestinal tract disorders (Dewettinck et al.,
2008). Soluble fiber, although present only small amounts, helps reduce blood
cholesterol and sugar levels which in turn useful in the management of diabetes as
well as heart diseases (Plaami, 1997; Ranhotra, 1994).
Wheat is considered a good source of protein among cereals. It contains approximately
6-20% protein (Dobraszczyk, 2001; Shewry et al., 2009). However, the nutritional
quality of wheat protein is lower than milk and meat proteins due to its deficiency in
lysine (an essential amino acid) (Ranhotra, 1994; Shewry, 2009). The lysine quantity
is sufficient to meet the adults’ requirements but not childrens’ (Dewettinck et al.,
2008).
Wheat contains only small amount of lipids mainly located in the germ. The majority
of the fatty acids are unsaturated which helps lower blood cholesterol level. In
addition phytosterols, another type of lipid in wheat, can also reduce blood cholesterol
levels, decrease the risk of cancer, and increase immune modulation (Awad and Fink,
53
2000; Bouic, 2001; Ostlund, 2004). Lipids are also helpful in absorption of fat-soluble
vitamins (vitamins A, D, E, and K).
Whole-wheat is a good source of B vitamins, particularly thiamine (B1), riboflavin
(B2), niacin (B3), pyridoxine (B6), and folate (B9) (Bock, 2000; Piironen et al., 2009).
Wheat also contains a moderate amount of vitamin E. It has been reported the roles of
vitamin E in prevention or reduction of many diseases such as heart disease, cancer,
Alzheimer’s disease, and Parkinsons’ disease (Packer and Obermuller-Jevic, 2002).
Wheat also contains significant amount of minerals, particularly phosphorus, calcium,
magnesium, and potassium (Bock, 2000; Dewettinck et al., 2008; Rosell, 2003). In
contrast, iron, copper, zinc, sodium, and manganese are present in low quantities
(Bock, 2000; Piironen et al., 2009). Even though phosphorus is present in high
concentration in wheat, it is in a phytate form which humans have limited abilities to
hydrolyze and absorb. Phytate will also form insoluble complexes with mineral
cations, especially zinc, calcium, magnesium, and iron in the gastrointestinal tract
(Bohn et al., 2008; Davies and Nightingale, 1975). Phytate is therefore considered as
an anti-nutritional substance (Rosell, 2003). However, the impact of phytate on
bioavailability of those minerals is small compared to the amount of minerals present
in whole-wheat grain (Ranhotra, 1994). Selenium, an essential micronutrient for
54
humans with antioxidant, anti-cancer and anti-viral effects, is also found in wheat
(Lyons et al., 2005).
Significant amounts of phytochemicals and antioxidants such as carotenoids,
tocopherols, lignans, and phenolic acids are also found in wheat. Many studies have
pointed out that these phytochemicals and antioxidants may decrease the risk of
chronic diseases such as cardiovascular disease, diabetes, and cancer (Fung et al.,
2002; Knekt et al., 2002; Liu, 2004). Adom and Liu (2002) reported mean total
phenolic content (TPC) value for wheat was 1.90 µmol gallic acid equivalents
(GAE)/g sample. Adom et al. (2003) determined phytochemicals and antioxidant
activity of 11 wheat varieties and reported that TPC ranged from 709.8 to 860.0 µmol
GAE/100 g sample, total antioxidant activity (TAA) ranged from 37.6 46.4 µmol of
vitamin C/g, and total flavonoid content ranged from 105.8 to 141.8 µmol of catechin
equivalent/ 100 g sample. Liyana-Pathlrana and Shahidi (2007) reported TAA and
TPC of whole grain and wheat fractions of Canadian Western Amber Durum (CWAD)
and Canadian Western hard red spring (CWRS) using DPPH radical scavenging assay
and Folin-Ciocalteu’s reagent. Mean TAA values for CWAD and CWRS whole grain
were 4.24 and 4.99 µmole TEQ/g defatted sample, respectively. Mean TPC for
CWAD and CWRS whole grain were 769 and 1291 µg ferulic acid equivalents
(FAE)/g defatted sample, respectively. Yu et al. (2002) determined TAA and TPC of
Akron (HRW), Trego (HWW), and Platte (HWW)using radical cation ABTS+
55
scavenging assay and Folin-Ciocalteu’s reagent, respectively. Mean TAA for Akron,
Trego, and Platte were 1.31, 1.08, and 1.91 µmol TEQ/g sample, respectively. Mean
TPC for Akron, Trego, and Platte were 927.8, 642.2, and 487.9 mg GAE/g sample,
respectively. They found no significant correlation (p > 0.05) between TAA and TPC.
Zhou et al. (2004) reported TAA and TPC for Swiss red wheat grain were 14.67 µmol
TEQ/g sample and 1.8 mg of GAE/g sample, respectively. TPC for eight soft red
winter wheats grown in Maryland ranged from 0.4-0.8 mg of GAE/g sample (Moore et
al., 2005).
Adom and Liu (2002) found that bound phytochemicals were the main contributors to
total phenolics, flavonoids, and total antioxidant activity in wheat. The bran and germ
fractions generally have greater phytochemical and antioxidant levels than the
endosperm fraction (Adom et al., 2005; Liyana-Pathlrana and Shahidi, 2007).
Most wheat products are generally made from flour which has undergone refinement.
The bran, aleurone, and germ removed from the original wheat grain are dense in
nutrients. Consequently, more than half of the vitamin B1, B2, B3, E, calcium,
phosphorus, folic acid, copper, zinc, iron, and fiber are eliminated (Murray et al.,
2006). Even though enriched wheat flour is available, it does not equal what has been
lost. The simplest and cost-effective way to improve the nutrition quality of wheat
56
products is to increase whole grain consumption or incorporate more whole wheat
flour into products.
2.9 Marker assisted selection
Wheat breeding has been performed for centuries since wheat was first domesticated.
Previously, wheat improvement relied on a trial-and-error approach. Large numbers of
crosses were made and progeny were evaluated for traits of interest (disease resistant,
grain yield and grain quality). Selection has been based on phenotype. This approach
however can be inefficient, unpredictable, and not cost-effective. In recent years,
genotypic selection has been increasingly employed due to progress in genetics and
genomics. Marker assisted selection (MAS) is a process of using DNA markers
tightly-linked to specific genes of interest to help selection. MAS can be an accurate
and cost-effective tool to complement phenotypic screening because it is possible to
identify and select genes controlling desirable and undesirable traits at an earlier stage
of breeding.
Generally, traits can be divided into two groups: qualitative and quantitative.
Qualitative traits are traits usually controlled by one or small numbers of genes and
not significantly influenced by the environment (Baenziger and Depauw, 2009). The
traits are either present or absent, for example wheat kernel hardness which is
classified as either hard or soft. In contrast, quantitative traits are traits typically
57
controlled by a fairly large number of genes and highly influenced by environmental
factors (Baenziger and Depauw, 2009). The traits show continuous distributions.
Examples of quantitative traits are grain yield, and end-use quality.
Prior to employing MAS, it is important to know where genes or chromosomal regions
controlling traits of interest are located. However, it is more complicated for
quantitative traits because there are many genes involved, and the level of influence
and the function of each gene are different. Quantitative trait loci (QTL) analysis is a
process of identifying chromosomal locations that influence the trait of interest. In
QTL analysis, a statistical method is used to put together phenotypic and genotypic
(marker) data and tries to explain which genomic regions create the variation of these
phenotypes. QTL analysis is practiced with a genetic linkage map containing known
locations of genes or genetic markers of an experiment population. The efficiency of
the QTL analysis depends on the selection of parents, type and size of the mapping
population, density of markers, quality and accuracy of phenotypic and genotypic
data, and statistical method used.
Kernel hardness of common wheat has been studied intensely because it has a large
influence on end-use properties. As note in Section 2.4 the Ha locus on the short arm
of chromosome 5D is the major gene that controls kernel texture. Several QTLs that
affects kernel hardness have been identified. Groos et al. (2004) determined kernel
58
hardness of 194 recombinant inbred lines (RILs) derived from the cross between two
hard wheat cultivars (Renan and Recital) using the Single Kernel Characterization
System. They identified11 QTLs for kernel hardness, located on chromosomes 1B,
2B, 2D, 3A, 3B, 4A, 5A, 5B, 6B and 6D, and at the unlinked marker Xgwm130.
Arbelbide and Bernardo (2006) reported two markers related to kernel hardness at the
loci Xcfa2153 and Xgwm190 on chromosomes 1A and 5D, respectively. In an F5:6derived RILs of a cross between soft x extra-soft wheat cross, five QTLs were
detected on chromosomes 4BS, 4DS, 5DL and 7DS (Wang, 2010). Campbell et al.
(1999) who determined kernel hardness of 78 F2:5-derived RILs from a cross between
soft x hard wheat and identified four QTLs on chromosomes 2A, 2D, 5D, and 6B.
Twenty-two QTLs associated with kernel hardness were identified on chromosomes
1A, 1D, 2A, 2B, 3A, 3B, 5A, 5D, 6B, 6D, 7A, and 7B (Bordes et al., 2011).
59
Chapter 3: Materials and Methods
3.1 Materials
3.1.1 Effect of carbonate on co-extraction of arabinoxylans and
glutenin macropolymer (Chapter 4)
Twenty-three straight-grade flour samples representing 21 genotypes were obtained
from wheat harvested in the U.S. Pacific Northwest region in 2007. Samples were
obtained courtesy of the participants in the U.S. Pacific Northwest Wheat Quality
Council (PNW-WQC) and consisted of three hard red winter (HRW), six hard spring
(both red and white) (HS), four soft white spring (SWS), and 10 soft white winter
(SWW) cultivars. The SWW and SWS sets each had one cultivar with two entries.
Sample protein, absorption, and dough characteristics are presented in Table 4.1. Even
though there were both red and white HS samples, no distinction was made based on
seed coat color. Grain was milled into flour using a Miag Multomat pilot-scale flour
mill at the USDA-ARS Western Wheat Quality Laboratory (WWQL) as previously
described (Martin et al., 2007). Briefly, wheat (54 kg) was initially tempered (14% and
16% moisture for soft and hard wheats, respectively) approximately 18-24 h before
milling. A second temper was performed 10 min before milling by adding 0.5% of
water based on dry wheat weight. Tempered wheat was milled at a feeding rate of
approximately 900-1000 g/min. The Miagmill is composed of three break and five
reduction rolls producing ten flour streams and four feed streams. All ten flour streams
were blended to obtain straight grade flour.
60
3.1.2 Optimization of water addition to noodle doughs (Chapter 5)
Four straight-grade wheat flour samples from the U.S. PNW-WQC were used: Louise,
OR2050910, Promontory and WA7954. Straight-grade flour was prepared according
to the method described by Martin et al. (2007) and detailed in Section 3.1.1.
3.1.3 Determination of wheat quality and quantitative trait locus
analysis (Chapter 6 and 7)
Grain samples (approximately 1 kg) from 265 recombinant inbred lines (RILs) were
provided by the OSU wheat breeding program. The RILs were derived from a cross
between Tubbs, a soft white winter wheat variety widely grown in Oregon, and NSA
98-0995, an experimental hard red winter wheat line from the Limagrain/Nickerson
Limited’s French breeding program. The samples were grown in Corvallis, OR and
harvested in 2009 and 2010. Samples were sealed in lidded plastic containers and
stored at 4°C. Samples were equilibrated at room temperature before analysis.
3.2 Methods
3.2.1 Effect of carbonate on co-extraction of arabinoxylans and
glutenin macropolymer
For glutenin macropolymer (GMP) isolation, flour was defatted using the procedure
described by Peighambardoust et al. (2005). Briefly, each flour sample (50 ± 0.5 g)
61
was mixed with petroleum ether (750 mL) in a 1-L erlenmeyer flask. The mixture was
shaken constantly for 15min. The mixture was transferred into 250-ml centrifuge
bottles and centrifuged (Sorvall RC-5B, DuPont Medical Instrument, Hoffman
Estates, IL) at 4,200g for 10 min at ambient temperature. The supernatant was
discarded while the pellet was evaporated overnight in a fume hood. The yellow fat
residue from the top layer was removed and the defatted flour was obtained.
GMP was isolated from the defatted flour using the method described by Don et al.
(2003a) with modification. Briefly, defatted flour (1.5 ± 0.005 g) was suspended in
9.125 mL of deionized water in a 50-ml high speed centrifuge tubes. The mixture was
vortexed for 15-30 s. A 12% (w/v) ultra-pure SDS solution (3.875 mL) was added to
the mixture and vortexed for another 15 s. Deionized water (9 mL) was then added to
the mixture and vortexed for another 15 s. The final SDS concentration now was 2.1%
(w/v). For alkaline extractions the final flour slurry was 0.55% (w/v) with respect to
Na2CO3. The mixture was then centrifuged at 18,500g for 60 min at 20°C. The
supernatant was discarded while the wet GMP gel-like layer on top layer of the starch
sediment was scraped out and weighed.
AX content was determined on wet GMP using a colorimetric assay (Bettge and
Morris, 2000; Douglas, 1981). Briefly, xylose standard solutions were prepared by
first dissolving 0.1 ± 0.001 g of D-(+)-xylose in a 100-mL volumetric flask containing
62
deionized water and adjusted the volume with deionized water to 100 mL (solution A).
The solution A (10 mL) was then transferred to another 100-mL volumetric flask and
the volume adjusted with deionized water to 100 mL (solution B). Finally, 0, 0.5, 1.0,
1.5, and 2.0 mL of solution B were made up to 2.0 mL with deionized water to obtain
the final concentrations of 0, 25, 50, 75, 100 μg/mL, respectively.
Wet GMP (0.05 ± 0.002 g) was mixed with 2 mL of deionized water in a test tube and
the mixture was vortexed for 5 s. An extraction solution (10 mL), prepared as
described by Douglas (1981) was added into sample, blank, and standard tubes. Tubes
were incubated in a boiling water bath for 25 min and then immediately cooled in an
ice bath for 5 min. Absorbance at 510 and 552 nm of the solutions were determined
using a VERSAmax Tunable microplate reader (Molecular Devices, Inc., Sunnyvale,
CA). A xylose standard curve was created by plotting the A552-A510 values of xylose
standard solutions against their concentrations.
AX content was expressed as a percentage of wet GMP weight. Each determination
was made in duplicate. Flour protein concentration, carbonate, water, sucrose and
lactic acid solvent retention capacities, Farinograph, sugar snap cookie, and bread
baking tests were performed by the USDA-ARS WWQL using the appropriate
AACC-international approved methods (39-11, 56-11, 54-10, 10-52, and 10-10B,
respectively ) (AACC International, 2000). Japanese sponge cake baking was
performed using the method of Nagao et al. (1976).
63
3.2.2 Optimization of water addition to noodle doughs
3.2.2.1 Damaged starch
Damaged starch was determined using a starch damage assay kit (Megazyme, Ireland)
according to AACC approved method 76-31 (AACC International, 2000). Reagent
solutions were prepared as described in the kit manual. Refined flour samples (100 ±
10 mg) were weighed accurately into glass centrifuge tubes. The tubes and fungal amylase were incubated in a 40°C water bath for 5 min. Fungal -amylase (1.0 mL)
was added to each tube. The tubes were vortexed and placed in the 40°C water bath
for exactly 10 min from time of addition of the enzyme. After 10 min, sulphuric acid
solution (0.2 % v/v, 8 mL) was added to each tube to terminate the reaction and the
tubes were vortexed for 5 s. The tubes were centrifuged using a Beckman GS-15R
centrifuge (Beckman Coulter, Inc., Brea, CA) at 3,000 rpm for 5 min. Aliquots of 0.1
mL supernatant were carefully transfer to the bottom of two test tubes.
Amyloglucosidase solution (0.1 mL) was added to each tube. The tubes were vortexed
for 5 s and incubated at 40°C for 10 min. GOPOD reagent solution (4.0 mL) was
added to each tube including glucose standards and reagent blank tubes. The tubes
were incubated at 40°C for another 20 min. The absorbance of all solutions at 510 nm
was measured against a reagent blank. Each sample was analyzed in triplicate. Starch
damage level was calculated as follows:
64
%
∆ 8.1
E = Absorbance read against the reagent blank
F=
W = Weight in milligrams of the flour analyzed
Kernel hardness index, flour protein concentration and flour moisture were provided
by the USDA-ARS WWQL and determined using appropriate approved methods
(AACC International, 2000)
3.2.2.2 Noodle dough making
Noodle dough was prepared according to the method of Ohm et al.(2006). A 200-g pin
mixer (National Mfg, Lincoln NE) was used to prepare the dough mixture. Salt (NaCl)
and alkaline (NaCO3) solutions were prepared by dissolving salt and alkali in
deionized water to obtain a final concentration of 1.2 and 1 % flour weight basis,
respectively. Flour (100 g) was placed in a the mixing bowl and mixed dry for 30 s.
Salt or alkaline solution (for salted and alkaline noodles, respectively) was then added
to the flour in a consistent stream over a period of 30 s and the resulting crumbly
dough was mixed for a further 1 min. The mixer was stopped and adhering dough was
scraped from the pins and the bowl. Mixing was continued for another 2.5 min to
obtain a crumbly dough, which was then used for the sieving method or processed
further for the LSF method. For the LSF method, the crumbly mixture was rest for 30
65
min and then compounded using an Ohtake noodle machine (Ohtake Noodle Machine
Mfg. co. Ltd., Tokyo, Japan) with a 4.0-mm roller gap. The compounding process was
repeated 3 times with lengthways folding of the dough (halving its length and
doubling its thickness) each time. The compounded dough was cut immediately into
round “coin” pieces using a 25.4-mm inside-diameter punch (No. 149, C.S. Osborne
and Co., Harrison, NJ). Dough pieces were kept in a closed Ziploc bag at ambient
temperature until use.
3.2.2.3 Determination of optimum water addition
Three methods, the Mixograph method of Oh et al. (1986), the sieving method of
Hatcher et al. (2002), and the lubricated squeezing flow (LSF) method of Ross and
Ohm (2006) were investigated as ways of determining optimum water addition to
noodle doughs.
3.2.2.3.1 Mixograph method
A 10-g Mixograph (National Manufacturing, Lincoln, NE) with the spring bar set at
slot #6 was used to determine preliminary optimum water addition of all four flours.
The procedure was followed as described by Oh et al. (1986) with a modification.
Flour (10 g) was put into the bowl and the mixer was started. Deionized water was
added to the flour every 1 min in the following order: two aliquots of 1.0 mL, two
66
aliquots of 0.5 mL, and several aliquots of 0.2 or 0.1 mL until the dough became
cohesive. The amount of water added for each flour sample was converted to be used
for 100-g flour in noodle dough making. The water addition ranges tested for salted
and alkaline doughs were -6 to +8 and -6 to +10 %, respectively, from the optimum
water absorption estimated using the Mixograph method. Addition of alkaline salts
may increase the potential water absorption in noodle dough (Fu, 2008), therefore an
additional increment of water addition above the optimum (+10%) was included for
the alkaline doughs.
3.2.2.3.2 Sieving method
A series of aperture sieves (6.5, 3.3, 2.0 and 1.0 mm, W.S. Tyler, Mentor, OH, and
Fisher Scientific, Pittsburgh, PA) and a RX-29shaker (W.S. Tyler, Mentor, OH) were
utilized. The crumbly dough prepared in section 3.2.2.1 was placed on the first sieve
and shaken for 30 s. The weight of the dough crumbs on each sieve was recorded.
3.2.2.3.3 Lubricated squeezing flow method
The biaxial compression behavior of the doughs was investigated using a texture
analyzer (TAXTPlus, Texture Technologies, Scarsdale, NY) with a 25.4-mm diameter
cylindrical probe and a 25.4-mm diameter fixed plate. The procedure was followed as
described by Ross and Ohm (2006). Thin coats (approximately 0.5 mm) of teflon
67
based grease (Finish Line Technologies, Bay Shore, NY) was applied on the samplecontact surfaces of the probe and the base plate. The pre-cut doughs prepared in
section 3.2.2.1 were compressed to 75% strain at a compression rate of 1 mm/s and
held for 30 s. Maximum stress (maximum force) at 75% strain, residual force at 25 s
after peak, relaxation time (time at maximum strain for stress to decline to 1/e of the
maximum value), and % residual force (ResF/maximum force*100) were reported.
3.2.3 Determination of wheat quality
3.2.3.1 Kernel characteristics
One hundred kernels from each sample were prepared by removing broken and nonuniform kernels, and foreign matter. The kernels were tested individually for hardness
index (HI), weight, and moisture content using a Perten 4100 Single Kernel
Characterization System (Perten Instruments, Inc., Springfield, IL) according to the
AACC approved method 55-31 (AACC International, 2000).
3.2.3.2 Polyphenol oxidase activity
Polyphenol oxidase (PPO) activity was determined according to Morris et al. (1998)
and Anderson and Morris (2001). Five undamaged kernels of wheat from a given RIL
were loaded into a 2 ml siliconized tube. A 1.5mL aliquot of 10 mM L-DOPA in 50
mM MOPS solution, pH 6.5, was added to the tube. The tube was capped tightly and
68
vortexed for 30 min. After 30 min, the tube was put into an ice bath immediately to
stop the PPO reaction. Absorbance of the solution was measured at 492 nm. Samples
of the wheat varieties Winsome (low PPO) and Klassic (high PPO) were included in
every run as controls. Determinations were carried out in triplicate.
3.2.3.3 Milling performance test
Milling performance testing was done according to a modified Quadrumat milling
method established by the USDA-ARS WWQL. Twelve to 18 hours before milling
wheat grain (200 g) was tempered with deionized water. Grain was tempered to 14.0%
and 15.0% moisture for soft and hard wheats, respectively. Tempered wheat was
milled using a Brabender Quadrumat Senior experimental mill (GmbH & Co. KG,
Germany). The total weight of tempered wheat was recorded. On the break roll unit
grain was milled at a rate of 150 g/min. Once all the grain passed through the break
rolls, the unit was cleaned by reversing and forwarding the rolls for 3 s each. This
cleaning process was repeated three times. Ground wheat was sifted through 500-µm
and 150-µm sieves (Fisher Scientific, Pittsburgh, PA) for 1 min using a mechanical
shaking sifter (Great Western Manufacturing, Leavenworth, KS). Bran retained on the
500-µm sieve was weighed and discarded. The remaining stock was sifted for another
2 min. Break flour was weighed and stored in a ziplock bag. Middlings retained on the
150-µm sieve were re-milled using the mill’s reduction roll unit. Middlings were fed
into the reduction rolls at a rate of 50g/min using a vibratory feeder. Once all stock
69
passed through the reduction unit, the reduction unit was cleaned three times. The
output from reduction unit was sifted for 3 min through a 150-µm sieve. “Shorts” (fine
bran) retained on the 150-µm sieve were weighed and discarded. Reduction flour in
the pan was weighed and combined with the break flour. Flour yield (%) was
calculated as follows:
Flouryield %
100
Totalweightoftemperedwheat
Break flour yield (%) was calculated as follows:
Breakflouryield %
100
Totalweightoftemperedwheat
3.2.3.4 Grain and flour protein contents
Grain and flour protein contents were determined using near infrared spectroscopy
(Infratec 1241, FOSS NIR Systems Inc., Denmark) according to the AACC approved
method 39-11 (AACC International).
3.2.3.5 Damaged starch
Method was detailed in Section 3.2.2.2
70
3.2.3.6 Arabinoxylan
Whole wheat flour was assayed for total arabinoxylan (TOAX) and water-extractable
arabinoxylan (WEAX) according to the method detailed in Section 3.2.1. Flour
samples were prepared for TOAX and WEAX determinations as follows. For TOAX,
flour (2-3 ± 0.5 mg) was suspended in 2 mL of deionized water. The mixture was
ready for analysis. For WEAX, flour (0.15 ± 0.0005 g) was weighed into a 15-mL
centrifuge tube with a screw cab. Deionized water (10 mL) was added to each tube.
The mixture was shaken vigorously for 5 s and incubated at room temperature for 30
min with vortexing at 10-min intervals. The mixture was centrifuged at 3,000g for 10
min and the supernatant (2 mL) was recovered for analysis.
TOAX and WEAX contents (µg xylose equivalent (XEQ)/mL) were determined based
on the linear equation from the standard curve. AX content value of the original
sample in g XEQ/100 g sample was calculated as follows:
100 100
For WEAX: mLsolvent = 10 mL and gsample = 0.15 g
For total AX: mLsolvent = 2 mL and gsample = 2-3 mg
Final results were expressed as % XEQ, dry weight basis (dwb). WUAX content was
obtained from the subtraction of WEAX content from total AX content.
71
%
100
%
100
3.2.3.7 Solvent Retention Capacity test
The solvent retention capacity (SRC) test investigates the ability of flour to retain a set
of solvents (water, 50% w/v sucrose, 5% w/v sodium carbonate, and 5% v/v lactic
acid). The test is used to predict baking performance of flour (Duyvejonck et al., 2011;
Kweon et al., 2011). Generally, lactic acid SRC is associated with gluten
characteristics, sodium carbonate SRC is associated with levels of damaged starch,
sucrose SRC is associated with pentosan and gliadin contents, and water SRC is
influenced by all flour constituents (Duyvejonck et al., 2011; Gaines, 2000; Kweon et
al., 2011).
The water and sucrose SRCs were conducted in whole and refined wheat flours
according to the AACC approved method 56-11 (AACC International, 2000) modified
to use 1 g of flour instead of 5 g. 15-mL centrifuge tubes with a screw cab were
weighed and recorded. Flour (1.000±0.050 g) was then weighed into each tube. An
appropriate solvent (5 mL) was added to each tube. The mixture was vortexed
vigorously for 5 s to suspend the flour. The mixture was then allowed to set and
hydrate for 20 min with vortexing at 5-min intervals. The tubes were immediately
transferred to a Beckman GS-15R centrifuge (Beckman Coulter, Inc., Brea, CA) and
72
centrifuged at 1,000g for 15 min. The supernatant was decanted and the tubes were
drained at a 90° angle for 10 min on a paper towel. Total weight of tube, cap, and
pellet was measured. Weight of pellet was calculated by subtracting total weight of
tube and cap from total weight of tube, cap, and gel. The test was done in triplicate.
SRC (%) value was calculated as follows (Haynes et al., 2009):
%
,
,
,
1
100
86
100
3.2.3.8 Polymeric proteins
Polymeric proteins are groups of chain polymers consisting of individual glutenin
subunits linked together by intermolecular disulfide bonds (Lemelin et al., 2005;
Southan and MacRitchie, 1999). The amount and size distribution of polymeric
proteins were analyzed using a modified two-step extraction method from Gupta et al.
(1993). Whole wheat flour (0.01 g) was suspended in 1 mL of 0.5% SDS, 0.05 M
sodium phosphate buffer (pH 6.9, extracting solution) and vortexed for 5 s. The
mixture was then centrifuged for 40 min at 8,800g using an Eppendorf 5413 centrifuge
(Eppendorf, Hauppauge, NY). The supernatant (SDS-extractable protein) was
recovered and filtered through a 0.45µm filter paper (Pall Corporation, Ann Arbor,
MI). The pellet was resuspended in 1 mL of extracting solution, vortexed for 5 s, and
sonicated (Model 100 Sonic Dismembrator, Fisher Scientific, Pittsburgh, PA) for 30s
at 5 W setting to solubilize protein not extractable in SDS alone. The mixture was
73
centrifuged for 40 min at 8,800g and the supernatant (SDS-unextractable protein) was
filtered. Both filtered SDS-extractable and -unextractable fractions were analyzed
using a SE-HPLC (Water 2695, Waters Corporation, Milford, MA) with a
PhenomenexBioSep SEC-S 4000 column (Phenomenex, Torrance, CA) and a guard
column (KJ0-4280, Phenomenex, Torrance, CA). Sample (10 μL) was injected into an
eluent of 50% (v/v) acetonitrile and water contain 0.1% (v/v) TFA at flow rate of 0.45
mL/min. Separation was achieved in 12 min. Proteins were detected at 214 nm using a
Waters 2996 photodiode array detector (Waters Corporation, Milford, MA). HPLC
chromatogram was integrated at time 5.60 and 6.75 min which divided the
chromatogram into 3 main sections: large polymeric proteins (LPP, A1), small
polymeric proteins (SPP, A2), monomeric proteins (MP, A3) (Figure 3.1). The test
was carried out in triplicate.
Figure 3.1: A typical HPLC chromatogram of polymeric protein method
74
The percentage of large unextractable polymeric protein (%LUPP) was calculated as
follows:
%
1
1
100
1
The percentage of total unextractable polymeric protein (% TUPP) was calculated as
follows:
%
1
1
2
2
1
2
100
3.2.3.9 Sugar-snap cookie
Sugar snap cookies were prepared according to the AACC approved method 10-52
(AACC International 2000) with modifications. The ingredients are listed in Table 3.1.
75
Table 3.1: Ingredients sugar-snap cookie baking trials for a pair of cookies
Ingredients
Flour (14% mb)
Sucrose
Nonfat dry milk
NaCl (in Soln B2)
NaHCO3
NaHCO3(in Soln A1)
NH4Cl
Shortening
Additional Water
Total Water
Calculated TS3
Calculated % S4
1
Amount
40 g
24 g
1.2 g
0.18 g
0.4 g
0.32 g (in 4 mL)
0.20 g (in 2 mL)
12 g
2.7 g
8.7 g
81.8 g
73.4 g
Solution A was prepared by dissolving 79.8 g of sodium bicarbonate distilled water and made to 1 L.
2
Solution B was prepared by dissolving 101.6 g of ammonium chloride and 88.8 g of sodium chloride in distilled
water and made to 1 L.
3
Total Solvent (TS) was calculated as the sum of sugar weight and total formula water weight, based on 100 g of
flour.
4
Solvent (S) % was calculated as sugar weight divided by the total solvent weight, based on 100 g of flour.
The dry mix consisting of sucrose, nonfat dry milk and sodium bicarbonate was
combined for 1 min with a hand whisk. Shortening was added to the dry mix and all
the ingredients were mixed until creamy in a Hobart N50 5 quart mixer (Hobart Corp.,
Troy, OH) using the paddle attachment. A portion of the creamed mass (37.6 g) was
transferred to the cookie dough mixing bowl of a 35 g pin mixer (National
Manufacturing, Lincoln, NE) followed by the appropriate amount of water, and
solutions A and B as described in Table 3.1. The mixture was mixed for 3 min with
scraping down of the mixture if it is stuck on side of bowl. Flour was then added to the
76
bowl and the mixing was continued for another 25 s. The dough was gently scraped
out of the bowl in a single dough mass. The dough was then divided into two equal
portions using a spatula. Both dough pieces were transferred to a room-temperature
baking sheet with gauge strips. A rolling pin was used to flatten the dough pieces to
the required thickness as determined by the gauge strips. The flattened dough pieces
were cut using a 6-cm cookie cutter and dough scraps were discarded. Weight of the
cookie sheet and dough was recorded before baking. The cookies were immediately
baked in a 196°C oven for 10 min. The weight of cookie sheet and cookies was
recorded immediately after baking. The cookies were removed from the sheet after
cooling for 5 min. Average cookie height and diameter were measured after the
cookies reached room temperature.
3.2.3.10 Total antioxidant activity and total phenolic content
Total antioxidant activity of whole wheat flour was determined according to the
methods of Beta et al. (2005), and Yu (2008). Flour (0.1 0.001 g) was mixed with
methanol (1 mL). The mixture was shaken constantly for 2 hand centrifuged at 3,000
rpm for 10 min on an Eppendorf 5417C centrifuge (Eppendorf, Hauppauge, NY). The
supernatant was recovered for analysis. To determine total antioxidant activity,
methanol (200 µL) was added to blank wells of a 96-microplate. Control (methanol),
6-hydroxy-2,5,7,8-tetramethylchroman-2-Carboxylic Acid (Trolox) standards (0-25
mg/mL in methanol), and sample extracts (100 µL each solution) were added to
77
appropriate wells. 2,2-diphenyl-l-picrylhydrazyl (DPPH) solution (0.2 mM, 100 L)
was added to the wells containing control, standards, and sample extracts. The plate
was gently shaken for 5 s and absorbance of solutions was measured with a
VERSAmax Tunable microplate reader (Molecular Devices, Inc., Sunnyvale, CA) at
515 nm at time0 and 30 min, respectively. The test was carried out in triplicate. DPPH
radical scavenging activity (%) of Trolox standards and samples was calculated as
follows:
%
radicalscavengingactivity
1
100
A standard curve was created (Trolox concentration vs. %DPPH radical scavenging
activity). Based on the linear equation from the standard curve, total antioxidant
activity in ug Trolox equivalents (TEQ)/mL was calculated. The final results were
reported in µmole TEQ/100 g sample, which was calculated as follows:
100 1
250
100
mLsolvent = the mL of solvent used for the sample extraction
gsample = the grams of sample used for the sample extraction
Total phenolic content (TPC) was determined according to the methods of Gao et al.
(2002) and Singleton and Rossi (1965). Whole wheat flour (1.0 ± 0.0050 g) was
extracted at room temperature with 4 mL of acidified methanol [HCl/methanol/water,
1:80:10 (v/v)] for 2 h on a New Brunswick Scientific G-33 shaker (New Brunswick
78
Scientific, Edison, NJ). The mixture was then centrifuged (Beckman GS-15R
centrifuge, Beckman Coulter, Inc., Brea, CA) at 3,000 rpm for 10 min. The
supernatant was recovered for the determination of total phenolics.
To determine the TPC, an extract (0.2 mL) was mixed with freshly diluted 10-fold
Folin-Ciocalteau reagent (1.5 mL) in a tube. The mixture was incubated for 5 min and
6% (w/v) sodium carbonate solution (1.5 mL) was added to the mixture. The mixture
was incubated at room temperature for 90 min. The absorbance of the mixture was
then measured at 765 nm using a VERSAmax Tunable microplate reader (Molecular
Devices, Inc., Sunnyvale, CA). Acidified methanol was used as the blank and gallic
acid solutions (0-1 mg/mL) were used as the standards. The results were expressed as
gallic acid equivalents (GAE). The experiment was carried out in triplicate.
A standard curve was created by plotting the absorbance values of gallic acid standard
solutions against their concentrations. A linear regression equation from the standard
curve was used to calculate TPC (mg GAE/mL). The final results were expressed in
µg GAE/g sample.
mLsolvent = the mL of solvent used for the sample extraction = 4 mL
gsample = the grams of sample used for the sample extraction = 1 g
1000
79
3.2.4 Quantitative trait locus analysis (Chapter 7)
The genetic map for this population contained 460 molecular markers, comprised of
simple sequence repeat (SSR) and diversity arrays technology (DArT) markers,
covering 883 cM, 1048 cM and 609 cM for the A, B, and D genomes, respectively.
The map contained 38 linkage groups distributed throughout all 21 wheat
chromosomes.
Quantitative trait loci (QTL) analysis by composite interval mapping (CIM) was
conducted using WinQTL Cartographer v.2.5 (Wang et al., 2007). The genome was
scanned at a speed of 2 cM and the window size was set at 10 cM. Significance
likelihood (LR) was obtained and converted to likelihood-odds score (LOD, LOD =
0.217 x LR). A QTL was declared at the LOD score ≥ 2.5.
3.2.5 Statistical analyses
Statistical analyses were performed using StatGraphics Centurion XVI (Statpoint
Technologies, Inc., Warrenton, VA). One-way and multifactor analysis of variance
were performed to determine the significance of each main effect and possible
interactions. Tukey's test was used to compare the difference of the means of each
effect. Correlation coefficient was run to examine the relationship between effects.
80
Chapter 4: Results and discussion: Effect of carbonate on coextraction of arabinoxylans with glutenin macropolymer
Based on the published article: T. Kongraksawech, A. S. Ross, and Y. L. Ong. 2010.
Effect of carbonate on co-extraction of arabinoxylans with glutenin macropolymer.
Cereal Chemistry. 87: 86-88.
As noted in Chapter 1 it was suggested that the aqueous phase of dough under alkaline
processing conditions might be enriched in AX. Increased levels of AX in the dough
aqueous phase could increase interactions between AX and gluten potentially leading
to altered gluten aggregation properties. We derived these hypotheses: under alkaline
processing conditions: 1) we would find AX to be associated with GMP and it would
be at higher concentrations than found at lower pH; and2) GMP de-polymerization
would be faster during mixing and sheeting and re-polymerization slower on dough
resting (Ong and Ross, 2008).
This study addressed the first hypothesis with these aims: to determine if 1) AX was
co-extracted with GMP, and 2) if so, did it increase in concentration in GMP extracted
from alkaline-carbonate flour slurries.
81
Results and discussion:
A preliminary study by other workers in our research team used flour from 1 SWW
and 3 HRW cultivars. They showed large increases in the concentration of AX coextracted with the GMP from alkaline noodle doughs made with Na2CO3 (“alkalinecarbonate doughs”) in comparison to the amount of AX co-extracted with GMP from
salted doughs (data not presented). Intriguingly the preliminary work showed a larger
proportional increase in the so-called GMP associated AX (GMP-AX) when GMP was
extracted from alkaline-carbonate dough made from the SWW cultivar (Stephens)
when compared to the increase observed in alkaline-carbonate GMP-AX in GMP
extracted from wheats of the other classes. These preliminary results led to the current
survey of a larger number of samples.
4.1 GMP-wet weight
ANOVA performed on the 23 straight-grade flour samples representing the 21
genotypes used in this study (Section 3.1.1) showed significant differences in the wet
weight of GMP across cultivars (F-ratio = 83.3; P ≤ 0.001) and across the two
extraction variants (alkaline and salted: F-ratio = 2564; p ≤ 0.001). The two-way
interaction term was also significant (F-ratio = 11.1; p ≤ 0.001) but the extraction
variants had the most profound effect (Figure 4.1). The GMP wet weights from both
water and alkali were significantly correlated (r = 0.92; p ≤ 0.001; n = 46). In general,
if GMP wet weight is an index of dough attributes e.g., Don et al. (2003a) then these
82
results made sense across the four wheat types displayed in Figure 4.1. The hard
spring (HS) wheats, with generally stronger dough attributes, had the highest GMP
wet weight, followed by the hard red winter (HRW), which overlapped with the
generally slightly stronger soft white spring (SWS) wheat. The soft white winter
(SWW) wheats had overall the lowest GMP wet weights. This is consistent with our
general understanding of the characteristics of these classes. Within the SWW we can
observe the higher GMP wet weight of Eltan, which is known to produce the strongest
doughs of current commercial SWW wheats, its backcrossed progeny ORCF103
which is also a stronger SWW wheat, and ORSS1757, which is among the weaker
(Figure 4.1). These inferences were supported by significant correlations between
GMP wet weight in water and farinograph peak time (r = 0.85; p ≤ 0.001; n = 23),
farinograph stability (r = 0.75; p ≤ 0.001; n = 23), and for the hard wheat flours, with
loaf volume (r = 0.79; p ≤ 0.001; n = 9).
83
Figure 4.1: Wet weight of glutenin macropolymer extracted from flour-water (solid
line), and flour-water-sodium carbonate (dashed line) slurries of 23 flour samples.
HRW, hard red winter; HS, hard spring; SWS, soft white spring; SWW, soft white
winter. Error bars represent ± least significant difference between water and alkaline
extractions at P = 0.05.
Table 4.1: Identification, market class, flour protein, absorption, and dough and end product characteristics of flour samples
Variety
Boundary
UT9325-55
Promontory
BZ903-445WP
BZ904-336WP
Cabernet
Snowcrest
WA7954
(KELSE)
WB926
Alturas
Louise
Louise1
WA8039
11.4
12.3
11.6
12.6
12.9
12.5
12.4
Solvent retention capacity
Carb W
Suc
Lac
(%) (%)
(%)
(%)
80
61.7 107.4 123.1
76.9 62.8 100.1 125.1
89.9 71.6 113.9 141.9
86.5
67 122.5 148.8
82.3
66 111.9 153
74.7 63.6 104.3 141.6
78.5 61.7 112.6 152.5
ABS
(%)
61.1
60.9
61.6
60.8
64.2
62.1
58.9
HS
14.5
90.9
66.2
123
168
63.5
8.3
HS
SWS
SWS
SWS
SWS
13.6
9.5
10.2
8
9.1
91.2
72.1
74.5
71.1
73.6
68.2
53.5
53.5
54.8
55.1
118.8
105
102.6
93.4
103.6
169.8
124.3
135
106
137.6
60.5
50.6
53.2
51.3
52.8
7.3
3.5
3.7
1.3
1.9
Growth
location
Class
FRC
(%)
Aberdeen, ID
Bluecreek, UT
Bluecreek, UT
Moses lake, WA
Moses lake, WA
Palouse, WA
Bozeman, MT
HRW
HRW
HRW
HS
HS
HS
HS
Pullman, WA
Pullman, WA
Aberdeen, ID
Pullman, WA
Various
Pullman, WA
Farinograph
PEAK STAB
(min) (min)
7.8
20.4
7.7
12.2
9.2
26.6
9
25.3
10.8
26.1
6.3
9.4
11.8
25.9
CODI
(min)
CAVOL
(ml)
LVOL
(ml)
--------
--------
920
990
955
1010
1015
1045
1015
15.2
--
--
1090
10
4.6
5.8
3.8
5
-9.43
9.56
9.63
9.34
-1143
1308
1335
1320
1020
-----
a
FPC, flour protein concentration (14% moisture basis); Carb-SRC, W-SRC, Suc-SRC, and Lac-SRC, carbonate, water, sucrose, and lactic acid solvent retention
capacities, respectively; F-ABS, F-PEAK, and F-STAB, farinograph water absorption, peak time, and stability time, respectively; CODI, sugar snap cookie
diameter; CAVOL, sponge cake volume; LVOL, AACC pup loaf volume.
84
Table 4.1: Identification, market class, flour protein, absorption, and dough and end product characteristics of flour
samples (Continued)
Variety
Bitterroot
Bitterroot1
Eltan
ID93-64901A
Masami
OR2050910
ORCF102
ORCF103
ORSS1757
Stephens
Growth location
Moscow, Tensed,
and Deary, ID
Various
Pendleton, OR
Moscow, Tensed,
and Deary, ID
Various
Pendleton, OR
Various
Pendleton, OR
Various
Pendleton, OR
Solvent retention capacity
Carb W
Suc
Lac
(%) (%)
(%)
(%)
ABS
(%)
9.4
71.5
54.3
100.1
108.6
51.3
2.5
SWW
SWW
8.2
8.6
67.8
71.9
55.1
55.6
95.3
108.2
92.3
122
51.5
53.1
SWW
8.5
75.2
55.6
100.1
112.4
SWW
SWW
SWW
SWW
SWW
SWW
8.6
6.8
8.7
7.2
6.9
7.2
69.2
72.3
73.5
72.2
73.8
70.7
54.6
53.9
59.6
54.9
54.5
55.2
96.8
96.4
100.9
99.6
96
96.7
102
90.1
84.8
100.4
80.4
86.8
Class
FRC
(%)
SWW
Farinograph
PEAK STAB
(min) (min)
CODI
(min)
CAVOL
(ml)
LVOL
(ml)
3
9.54
1275
--
2.2
5
3.2
6.6
9.6
9.57
1315
1303
---
52.5
2.2
2.7
9.81
1265
--
52.8
51.2
54.1
53.6
51.2
52.3
1.4
1
1.8
1.5
1.2
1
1.7
1
1.6
6.6
1
1.1
9.25
9.7
9.18
9.5
9.64
9.66
1270
1308
1215
1313
1305
1258
-------
85
86
4.2 GMP-AX
ANOVA also showed significant differences in the concentration of GMP-AX across
cultivars (F-ratio = 11.3; p ≤ 0.001) and across the two extraction variants (F-ratio =
198.1; p ≤ 0.001). The significant interaction term (F-ratio = 9.8; p ≤ 0.001) most
likely reflected the large variation of increase in GMP-AX across cultivars (Figure
4.2). However, there was always a higher concentration of GMP-AX under alkaline
extraction conditions, except for cultivars UT9325-55 and Kelse. Figure 4.2 shows
that the concentration of AX in GMP extracted from 23 samples systematically
differed between hard and soft wheats. Soft wheat generally showed a greater increase
in GMP-AX concentration when the GMP was extracted from the alkaline flour
slurries. For example, in the SWS group, all four samples had significantly increased
GMP-AX concentration compared to increases that were barely significant or not
significant for seven of the nine hard wheat samples (Figure 4.2). Within the soft
wheat classes, it was clear that there were further systematic subdivisions. The SWW
group taken as a whole had significantly higher GMP-AX concentration than the SWS
group when GMP was extracted from the alkaline flour slurries (Figure 4.2). Within
the SWW wheats, there were two further groups. The first was a group with smaller
increases (Eltan, OR2050910, ORCF103, and Stephens). This group had increases in
GMP-AX concentration similar to the levels of increase seen for the SWS samples.
The second group had significantly larger increases in GMP-AX concentration in
alkali (the 2 Bitterroot samples, ID93-64901, ORCF102, and ORSS1757).
Interestingly, there is a suggestion of a genetic component. Two of the low-increase
87
SWW cultivars were Eltan and ORCF103. These are highly related; ORCF103 is an
Eltan backcross. The other two cultivars were Stephens and OR2050910; the latter has
multiple occurrences of Stephens in its pedigree. It was difficult to make other familial
connections through the pedigrees of the large increase SWW group. Accordingly, we
need to be careful not to draw too many inferences.
Figure 4.2: Arabinoxylan (as xylose equivalents) concentration in glutenin
macropolymer extracted from flour-water (solid line), and flour-water sodium
carbonate (dashed line) slurries of 23 flour samples. HRW, hard red winter; HS, hard
spring; SWS, soft white spring; SWW, soft white winter. Error bars represent ± least
significant difference between water and alkaline extractions at P = 0.05.
Attempts to find correlations with the increase in GMP-AX concentration when
extracted from the alkaline flour slurry with other flour attributes, including solvent
retention capacity (SRC) values, especially the carbonate SRC variant, proved
unsuccessful. There was a small but significant (r = 0.55; p ≤ 0.05; n =14) correlation
88
between sponge cake volume and the GMP-AX concentration from the soft wheat
flours when extracted from the flour-water, but not the flour-alkali slurries. It is not
possible to reach a conclusion about this finding from this limited data set. Finally,
this data does not rule out the possibility that the observed association between the
alkaline conditions brought about by the addition of sodium carbonate and the
increased levels of GMP-AX is simply an artifact of the isolation procedure. For
example, it could result from the inadvertent entanglement of alkali-solubilized AX
with glutenins during mixing or centrifugation, and it may not occur in dough.
89
Chapter 5: Results and discussion: Optimization of water addition to
noodle doughs
Water is an essential ingredient in noodle making and has profound effects on noodle
quality and appropriate amounts are required in dough for optimum processing and
end-product quality. Currently, the only method that is widely used to determine
whether a proper amount of water has been added during noodle making is the handfeel of an experienced noodle maker. However, this method is subjective and varies
from person to person. Various objective methods have been proposed for use in
determination of optimum water addition to noodles such as Mixograph (Lee et al.,
1998; Oh et al., 1986; Ohm et al., 2006), Farinograph (Kruger, 1996), and sieving tests
(Azudin, 1998; Hatcher et al., 2002). However, they have not been widely adopted.
Lubricated squeezing flow (LSF) is a technique for studying the rheological behavior
of semi-liquid foods including noodle dough (Ong et al., 2010; Ross and Ohm, 2006).
Although the fundamental rheological behavior of an optimally hydrated noodle
dough is not known precisely, LSF might adaptable as a tool to determine optimum
water addition to noodle doughs. The aim of this study was to see if the LSF technique
was viable for this purpose.
5.1 Flour characteristics
Flour characteristics are summarized in Table 5.1. As expected, the soft wheats
(Louise and OR2050910) had lower kernel hardness index (HI), protein content, and
damaged starch (DS) than the hard wheats (Promontory and WA7954).
90
Table 5.1: Identification, market class, hardness index, protein content, moisture, and
damaged starch of flour samples
Variety
OR2050910
Louise
Promontory
WA7954
Location
Pendleton
Pullman
Bluecreek
Pullman
Class
SWW
SWS
HRW
HRS
HI DS (%) Protein (%) Moisture (%)
13.2
3.6
6.8
12.7
22.2
3.7
10.2
13.1
68.2
6.9
11.6
13.0
72.9
5.3
14.5
11.7
5.2 Noodle dough optimum water addition determined by Mixograph
Optimum water addition for noodle doughs, as determined via Mixograph was 32.043.0% (Table 5.2). It is of value to reiterate that the Mixograph method uses only flour
and water, with no added salts. This factor distinguishes this established method
(Section 3.2.2.3.2) from the other methods used in this study and from the majority of
commercial and experimental Asian noodle formulations used worldwide (Crosbie and
Ross, 2004a; Ross and Hatcher, 2005). As determined by this method optimum water
addition decreased as protein content increased across three of the four flour samples,
excepting Promontory. OR2050910 had the lowest protein content and the highest
absorption. In contrast, WA7954 had the highest protein content and the lowest
absorption. Park and Baik (2002) suggested that flours with higher protein content
require less water to create the uniform protein network that results in a good noodle
sheet. The higher than expected optimum water addition for Promontory could be
attributed to its notably higher starch damage level (Table 5.1).
91
Table 5.2: Optimum water additions determined by Mixograph, sieving, and LSF
methods
Variety
Mixograph optimum
water (%)
OR2050910
Louise
Promontory
WA7954
43.0
38.5
40.3
32.0
Sieving optimum
water (%)
Salted Alkaline
43.0
43.0
38.5
38.5
42.3
42.3
38.0
38.0
LSF optimum
water (%)
Salted
Alkaline
43.0-45.0 43.0-45.0
38.5-40.5 38.5-40.5
40.3-44.3 42.3-44.3
38.0-40.0 38.0-40.0
5.3 Sieving test
In contrast to the Mixograph method, the sieving tests devised by Hatcher (2002) and
Azudin (1998) use standard formulation noodle doughs (flour, water, and appropriate
salts) (Fu, 2008; Hou and Kruk, 1998; Park and Baik, 2002) and could be adapted
easily to doughs with other additives, such as alternate starches and polyphosphates
that may affect optimum water addition.
5.3.1 Salted doughs
Salted noodle doughs were prepared with a range of water additions (-6 to +8%) from
the Mixograph optimum water (Table 5.3). The amounts of material retained and
passing through the sieves from the uncompressed crumble of the salted doughs
prepared with the various water additions are shown in Figure 5.1. Water additions to
noodle dough had a significant impact on the amounts of material retained and passing
through the sieves. As expected, when water addition increased the amount of dough
92
retained increased while the amount passing decreased, indicating the expected pattern
of increasing dough cohesiveness as dough moisture increased.
Hatcher et al. (2002) defined optimum water addition as determined by this sieving
test as the water addition where the weight of the retained dough crumbs was equal to
the weight of the crumbs passing through the 3-mm sieve. It was evident for the softwheat flour derived noodle doughs that the closest to a 50:50 split between material
retained and material passing the sieve (sieving optimum) was equivalent to the
optimum water addition as determined by the Mixograph method (Figure 5.1a, b;
Table 5.2). For the hard-wheat flour derived noodle doughs the optimum water
additions predicted by the sieving method were higher than that predicted by the
Mixograph method: 2% higher for Promontory and surprisingly 6% higher for
WA7954 (Figure 5.1c, d; Table 5.2). For this test the expected decrease in optimum
water addition as flour protein increased was not as large as observed with the
Mixograph method.
93
Table 5.3: Range of water additions used to prepare noodle doughs for the sieving and
LSF methods
Dough
condition
Salted
Alkaline
Deviation of
water addition
from optimum
(%)
-6
-4
-2
0
2
4
6
8
-6
-4
-2
0
2
4
6
8
10
Water addition (%)
Louise
OR2050910
Promontory
WA7954
32.5
34.5
36.5
38.5
40.5
42.5
44.5
46.5
32.5
34.5
36.5
38.5
40.5
42.5
44.5
46.5
48.5
37.0
39.0
41.0
43.0
45.0
47.0
49.0
51.0
37.0
39.0
41.0
43.0
45.0
47.0
49.0
51.0
53.0
34.3
36.3
38.3
40.3
42.3
44.3
46.3
48.3
34.3
36.3
38.3
40.3
42.3
44.3
46.3
48.3
50.3
26.0
28.0
30.0
32.0
34.0
36.0
38.0
40.0
26.0
28.0
30.0
32.0
34.0
36.0
38.0
40.0
42.0
Figure 5.1: The amounts of material retained and passing through the sieves from the uncompressed crumble of the
salted doughs prepared with the various water additions: OR2050910 (a), Louise (b), Promontory (c), and WA7954 (d)
94
95
5.3.2 Alkaline doughs
Alkaline noodle doughs were prepared with a range of water additions (-6 to +10%)
from the Mixograph optimum water (Table 5.3). The soft-wheat flour derived doughs
had a sieving optimum water addition the same as that predicted by the Mixograph
method (Figure 5.2a, b; Table 5.2). Once again the hard-wheat flour derived doughs
had sieving optima at +2% (Promontory) and +6% (WA7954) greater than the
optimum predicted by the Mixograph method (Figure 5.2c, d; Table 5.2). The higher
sieving optima for both salted and alkaline hard-wheat flour derived doughs was
probably because the Mixograph method used only flour and water. As mentioned in
Section 3.2.2.3.1 that the incorporation of alkali may increase the potential water
absorption in noodle dough, it was expected that alkaline doughs would require more
water addition to reach their optimum levels. The results however showed that using
this method there was no difference in sieving optima between salted and alkaline
doughs.
Figure 5.2: The amounts of material retained and passing through the sieves from the uncompressed crumble of the
alkaline doughs prepared with the various water additions: OR2050910 (a), Louise (b), Promontory (c), and WA7954
(d)
96
97
5.4 Lubricated squeezing flow rheology
Lubricated squeezing flow (LSF) is a variant of stress relaxation tests applied to many
materials including foods. The principle of stress relaxation is to apply a deformation
to a specimen between two parallel plates, hold the deformation constant, and observe
the stress decay (relaxation) (Steffe, 1996). A typical LSF force/time curve is shown in
Figure 5.3. The parameters maximum force (MaxF), residual force (ResF) at 25 s after
peak, and relaxation time (RT) are shown for clarity. RT is the time that force takes to
decrease to 1/e or 36.8% of its maximum value. No relaxation would be observed in
an ideal elastic material because stress would not dissipate when the deformation was
held constant. Consequently, an ideal elastic material would have a RT of infinity. In
contrast, an ideally viscous material (e.g. water) would have a theoretical RT of 0.
Therefore materials with longer RTs are considered more elastic.
The hypothesis on which this study is based is that an optimally hydrated noodle
dough would flow most easily i.e. be least resistant to sheeting while still retaining the
distinct and somewhat plastic characteristics of a noodle dough, but before attaining
the distinctly elastic characteristics of a dough at higher hydration e.g. a bread dough.
Therefore, our hypothesis was that an optimally hydrated noodle dough would have
the shortest RT and lowest % residual force (% ResF) calculated as ResF/MaxF *100
prior to an increase in elasticity (e.g. increased RT) as the dough became more like an
elastic bread dough as water content increased. As a result it is anticipated that the
LSF method could be used to determine optimum water addition for noodle doughs.
98
Figure 5.3: A typical LSF force/time curve
5.4.1 Salted doughs
5.4.1.1 LSF maximum and residual force values
Water additions of noodle dough had significant impact on MaxF and ResF. However,
the data showed ResF and %ResF to have little value in predicting optimum water
addition and therefore these parameters will not be discussed further. MaxF for doughs
across four flour samples decreased as water addition increased (Figure 5.4a; Table
5.4). A similar pattern was reported by Hatcher et al. (1999) who showed that as water
addition increased, the amount of work required to sheet noodle doughs decreased.
ANOVA indicated that MaxF was lowest (p ≤ 0.001) for OR2050910 dough and was
highest (p ≤ 0.001) for WA7954 dough. MaxF values for Louise and Promontory
99
doughs were not significantly different (p > 0.05). OR2050910 dough at +8% water
addition was too wet to handle, having begun to show the characteristics of a “bread”
dough. WA7954 dough at -6% water addition was too dry to be compounded into a
dough sheet. Consequently, both these doughs were not subjected to LSF testing.
Table 5.4: LSF maximum and residual force values for salted noodle doughs prepared with various water additions
Deviation of
MaxF
ResF
water
addition
from optimum Louise OR2050910 Promontory WA7954 Louise OR2050910 Promontory WA7954
(%)
Salted
-6
115.0a
74.4 a
96.8 a
22.1 a
7.7 a
19.2 a
b
a
b
a
b
a
b
-4
90.9
68.0
72.3
235.6
15.5
7.0
13.5
59.5a
-2
75.9c
45.7 b
59.7c
186.8b
12.2 c
5.7b
10.9c
41.5b
0
57.8d
32.9 c
42.8d
138.0c
8.9 d
4.6c
7.9d
27.3c
2
37.1e
19.1 d
32.2e
98.2d
5.9 de
3.0 d
5.9e
17.2d
4
24.9f
12.6 de
25.8f
72.8e
4.3 e
2.0e
5.0ef
12.1e
6
19.1fg
10.1 e
18.8g
55.7f
3.7ef
1.5 e
3.8fg
9.3ef
8
8.6g
14.0g
35.6g
1.2 f
2.3g
5.7f
Alkaline
-6
135.3 a
91.4 a
138.3 a
25.5a
12.4 a
29.7a
b
b
b
a
b
b
b
-4
116.9
82.3
112.9
287.0
21.8
10.7
23.3
78.4 a
-2
95.8 c
66.1 c
93.5 c
240.6b
17.2c
8.2 c
18.8c
60.0b
0
66.8 d
47.9 d
74.6 d
190.0c
11.5d
6.3 d
14.9d
40.5c
2
49.8 e
31.7 e
65.1 e
150.0d
8.9e
5.1 e
13.1d
30.9d
4
32.9 f
22.1 f
51.2 f
120.2e
6.6f
4.6 e
10.5e
23.5e
6
24.0 g
16.4 g
41.8 g
96.7f
5.4fg
3.3 f
8.5e
18.2e
8
19.6 g
12.1gh
27.8 h
54.3g
3.9gh
2.0 g
6.1f
10.4f
10
14.8 h
9.2 h
23.4 h
45.0g
2.9h
1.5 g
5.5 f
9.7f
Comparing means within doughs from the same flour sample, means with different superscript letters are significantly different (p ≤ 0.001).
100
101
Maximum force
285
Louise
OR2050910
Promontory
WA7954
235
Force (N)
185
135
85
35
‐15
‐6
‐4
‐2
0
2
4
6
Deviation of water addition from optimum (%)
8
Figure 5.4: LSF maximum force for salted noodle doughs prepared with various water
additions
102
5.4.1.2 LSF relaxation time values
RT data for salted doughs from four varieties are shown in Figure 5.5; Table 5.5.
ANOVA indicated that RT was longest (p ≤ 0.001) for WA7954 dough and was
shortest (p ≤ 0.001) for OR2050910 dough reflecting the stronger nature of the
WA7954 dough. However, RTs for Promontory and Louise doughs were not
significantly different (p > 0.05). From these results, it was not possible to make a
clear conclusion about the relationship between hard- and soft-wheat derived noodle
doughs and RT, although at optimal water addition the hard wheat doughs tended to
have longer RT values. Additionally, other data has shown Louise to be among the
stronger of commercial soft white wheat varieties (Pacific Northwest Wheat Quality
Council, 2010). Ong et al. (2010)however observed that hard-wheat derived noodle
doughs had longer RTs than soft-wheat flour derived noodle doughs reflecting the
generally stronger dough characteristics of doughs derived from hard-wheats. Rao et
al. (2000) also suggested that the RT was associated with molecular size of gluten, the
longer the RT, the larger the molecules.
Comparing RTs within doughs from the same flour sample at increasing water
additions, RT for the soft-wheat flour derived noodle doughs decreased to a minimum
value at water additions of +2% and +4% for Louise, and -2% and 0% for
OR2050910 as water addition increased. As water addition further increased beyond
those minimum points, RT started to increase and then decreased again. Based on the
103
working hypothesis it is this first minimum in RT that is considered indicative of
optimal hydration. ANOVA showed that RTs at +2, and +4, water additions were not
significantly different (p > 0.05) suggesting a relatively broad range of optimum water
addition. RT at 0% water addition was not significantly different (p > 0.05) from those
at +2 and +4% water additions. However, the dough at +4% water addition was too
wet. Therefore, the doughs at 0 and +2% water additions were considered optimum.
OR2050910 dough had the shortest RT at -2 and 0% water addition. However,
ANOVA showed that RTs from -4 to +2% water additions were not significantly
different (p > 0.05) again suggesting that “optimum” water addition covered a
relatively broad range .Since OR2050910 doughs from -4and -2% water additions
were too dry when processed, the doughs at 0 and +2% water additions were therefore
considered optimum.
Unlike the soft-wheat flour derived noodle doughs, RT for the hard-wheat flour
derived noodle doughs continuously decreased as water addition increased. Although
ANOVA showed that RTs for Promontory dough from 0 to +8% water additions were
not significantly different (p > 0.05). The dough at +6 and +8% water additions were
too wet to handle. Promontory dough at 0, +2, and +4% water additions were therefore
considered optimally hydrated, although a minimum value for RT had not been
observed.
104
For WA7954 dough, ANOVA indicated that RTs at +8 and +6 % water additions were
lowest and not significantly different (p > 0.05). RT at 0% water addition (optimum by
Mixograph method) was the third longest (p ≤ 0.05) over the range of water additions
used in this study. RT results and dough handling indicated that WA7954 doughs at
+6 and +8% water additions were optimally hydrated.
Table 5.5: LSF relaxation time values for salted and alkaline noodle doughs prepared
with various water additions
Salted
Alkaline
Deviation of water
RT
addition from optimum
Louise OR2050910 Promontory WA7954
(%)
-6
3.2 a 1.1 b 3.0 a -4
2.4 c 1.0bc 2.3 b 8.1 a -2
1.9bc 0.9 c 2.0bc 5.6 b 0
1.6 cd 0.9 c 1.9 cd 3.9 c 2
1.3 de 1.0bc 1.8 cd 2.7 d 4
1.3 de 1.3 a 1.8 cd 2.1 de 6
1.8 c 1.3 a 1.8 cd 1.9ef 8
1.0 e -
1.7 d 1.4 f -6
3.4 a 1.6ab 4.4 a -
-4
3.0 a 1.4 cd 3.6 b 10.6 a -2
2.5 b 1.1ef 3.2 c 8.3 b 0
1.9 d 1.0 f 2.7 cd 5.5 c 2
1.7 d 0.9 f 2.7 d 4.5 d 4
2.0 d 1.5bc 2.5 d 3.7 de 6
2.4bc 1.8 a 2.3 d 3.1ef 8
1.8 d 1.5bc 2.6 d 2.4ef 10
2.1 cd 1.3 de 2.8 cd 3.0 f Comparing means within doughs from the same flour sample, means with different superscript letters
are significantly different (p ≤ 0.05)
105
Relaxation time
4
Louise
OR2050910
3.5
3
Time (s)
2.5
2
1.5
1
0.5
0
‐6
‐4
a
‐2
0
2
4
6
Deviation of water addition from optimum (%)
8
Relaxation time
10
Promontory
WA7954
9
8
Time (s)
7
6
5
4
3
2
1
0
‐6
b
‐4
‐2
0
2
4
6
Deviation of water addition from optimum (%)
8
Figure 5.5: LSF relaxation time for salted noodle doughs made from soft (a) and hard
(b) at various water additions
106
5.4.2 Alkaline doughs
5.4.2.1 LSF maximum force values
MaxF decreased as water addition increased (Figure 5.6; Table 5.4). ANOVA
indicated that MaxF was lowest (p ≤ 0.001) for OR2050910 dough and was highest (p
≤ 0.001) for WA7954 dough. MaxF values for Promontory and Louise doughs were
not significantly different (p > 0.05). ANOVA indicated that alkaline doughs had
significantly higher (p ≤ 0.001) MaxF than salted doughs (overall means 82.1 and 58.9
N from -6 to +8 water additions respectively). Alkaline reagents increase the firmness
of raw noodle doughs and cooked noodles (Fu, 2008; Hou and Kruk, 1998; Ong,
2007; Shiau and Yeh, 2001; Wu et al., 2006). WA7954 dough at -6% water addition
was not subjected to the LSF test because it was too dry to be formed a dough sheet.
107
Maximum force
340
Louise
OR2050910
Promontory
WA7954
290
Force (N)
240
190
140
90
40
‐10
‐6
‐4
‐2
0
2
4
6
Deviation of water addition from optimum (%)
8
10
Figure 5.6: LSF maximum force for alkaline noodle doughs prepared with various
water additions
108
5.4.2.2 LSF relaxation time values
RT results for alkaline doughs are shown in Figure 5.7; Table 5.5. ANOVA showed
that RTs for alkaline doughs were significantly different (p ≤ 0.001) across the four
varieties. WA7954 dough had the longest RT followed by Promontory, Louise, and
OR2050910 doughs, respectively. The same trend was observed as seen for the salted
doughs, alkaline hard-wheat flour derived noodle doughs had longer RT than alkaline
soft-wheat flour derived noodle doughs. Alkaline doughs had significantly higher (p ≤
0.001) RT than salted doughs (overall means 2.4 and 1.7 s from -6 to +8 water
additions respectively) suggesting that alkaline doughs were more elastic than salted
doughs. Ong (2007), and Ross and Ohm (2006) also observed that raw and cooked
alkaline noodles had longer RT than salted noodles. Hou and Kruk (1998) also
suggested that alkaline salts increase elasticity of alkaline doughs.
For Louise dough, ANOVA showed that RTs at 0, +2, +4, +8, and +10% water
additions were not significantly different (p > 0.05). However, doughs at +4, +8, and
+10% water additions were too wet. Therefore, the doughs at 0 and +2% water
additions were considered optimally hydrated. Also, +2% water addition was the
numerical minimum for RT in Louise doughs. For OR2050910 dough, ANOVA
indicated that RTs at -2, 0, and +2% water additions were not significantly different (p
> 0.05). However, dough handling suggested that the dough at -2% water addition was
too dry. The doughs at 0 and +2% water additions were therefore considered at the
109
optimum level. Again the +2% water addition was the numerical minimum for RT as
observed for the Louise doughs.
Promontory dough had the lowest RT at +6% water addition. ANOVA showed that
RTs from 0 to +10% water additions were not significantly different (p > 0.05). Even
though RT results suggested that the doughs at 0 to +10% water addition were
optimally hydrated, Promontory dough at 0% water addition was slightly dry while
that at +6, 8 and 10% water additions were too wet. In this case dough handling
characteristics did not agree with the numerical minimum for RT (+6%) as the
optimum level. WA7954 dough had the lowest RT at +8% water addition. ANOVA
showed that RTs from +6 to +10% water additions were not significantly different (p
> 0.05). RT at 0% water addition (Mixograph optimum) was the third longest (p ≤
0.05) over the absorption range studied. However, the dough at +10% was too wet.
The doughs at +6 and +8% water addition were therefore optimally hydrated. There
was no different in LSF optima between salted and alkaline noodle doughs as
identified by the sieving test. Once again both salted and alkaline hard-wheat flour
derived doughs had LSF optima greater than the optimum predicted by the Mixograph
method.
Optimum water additions predicted by Mixograph, sieving, and LSF methods are
shown in Table 5.2. The optimum water additions determined by the LSF method
110
were somewhat equivalent or close to those determined by the Mixograph method
across three of the four dough samples excepting WA7954 dough for both types of
noodle doughs. The optimum water additions predicted by the LSF method for both
salted and alkaline WA7954 doughs were remarkably 6-8% higher than those
predicted by the Mixograph method. LSF optima were equivalent or close to sieving
optima for both salted and alkaline noodle doughs.
111
Relaxation time
4
Louise
OR2050910
3.5
3
Time (s)
2.5
2
1.5
1
0.5
0
‐6
‐4
‐2
0
2
4
6
8
10
12
Deviation of water addition from optimum (%)
a
Relaxation time
14
Promontory
WA7954
12
Time (s)
10
8
6
4
2
0
‐6
b
‐4
‐2
0
2
4
6
8
Deviation of water addition from optimum (%)
10
12
Figure 5.7: LSF relaxation time for alkaline noodle doughs made from soft (a) and
hard (b) at various water additions
112
Chapter 6: Results and discussion: Determination of wheat quality
The 265 recombinant inbred lines (RILs) used in this study were originally created to
map elements of agronomic performance and disease resistance. Accordingly the
parental lines were selected primarily for their contrasting traits related to
susceptibility and tolerance to biotic and abiotic stresses. The RIL population was not
created to address quality issues. Despite the at times close convergence of the parents
for some quality related phenotypes there appeared to be a broad, usable range of
quality-related phenotypic values within the population. Transgressive segregations
were observed for many of the studied traits.
6.1 Kernel characteristics
Mean hardness index (HI), kernel weight (KW), and grain moisture content (GMC)
results of 2009 and 2010 RILs and their parents determined using the single kernel
characterization system are reported in Table 6.1, and Figure 6.1 and 6.2. For clarity,
traits with an “H+S” subscript represent traits for both hard and soft progeny, while
traits with either an “H” or an “S” subscript represent traits for hard or soft progeny
only, respectively.
113
2009 harvested lines
Mean HI was 37.7 for Tubbs and 58.1 for NSA, while mean HIH+S for the RILs was
47.9 and ranged from 15.3 to 83.8. In this study, RILs with HI  50 were considered to
be soft-grained. RILs with HI > 50 were considered to be hard-grained. Of the total of
265 available lines, 138 had HI  50 and 127 had HI > 50. As expected, NSA was
harder than Tubbs. However, NSA is relatively soft for a hard wheat and Tubbs is
relatively hard for a soft wheat (Flowers et al., 2009). This was confirmed by the HI
results in this study (Table 6.1). Transgressive segregation was observed for HIH+S.
The frequency distribution for HIH+S was bimodal as was expected from a population
generated from a cross between a genetically hard and a genetically soft wheat.
Observed separately, the frequency distributions for HIH and HIS were normal. The
presence of the bimodal distribution for HIH+S confirmed that the RILs were not
anomalous with respect to HI. The bimodal distribution confirms for this population
the established concept that kernel hardness is under control of a single major locus
(Campbell et al., 1999; Jolly et al., 1996), the Ha locus on the short arm of the
chromosome 5D of common wheat.
Mean KW was 45.0 mg for Tubbs and 40.2 mg for NSA, while mean KWH+S was 41.5
mg and ranged from 27.3 to 53.2 mg. Mean GMC was 11.6% for Tubbs and 11.5% for
NSA, while mean GMCH+S was 11.5% and ranged from 10.9 to 12.0%. The frequency
distributions for KWH+S and GMCH+S both fit a normal distribution. There was a
114
negative correlation (p ≤ 0.001) between HIH+S and GMCH+S (Table 6.2) indicating,
unsurprisingly, that kernels with higher moisture were softer. HI was significantly
correlated with break flour yield, damaged starch, and solvent retention capacities
(Table 6.2), which will be discussed later in this chapter.
2010 harvested lines (soft only)
Only the 137 lines considered to be soft-grained in 2009 were tested in 2010. Overall
2010 harvested grain had higher mean HI than that harvested in 2009 (p ≤ 0.001) (data
not shown). This led to the circumstance that 14 lines considered to be soft in 2009
(HI ≤ 50), when harvested in 2010 had HI > 50. These were included in the RILs
tested from the 2010 harvest. Mean HI was 44.9 for Tubbs and 63.8 for NSA, while
mean HIS was 41.4 and ranged from 26.6 to 68.4. The frequency distribution for HIS
was not normal due to those 14 lines that had HI > 50 in the 2010 harvest year.
However, if only HI of soft grained progeny was observed, the frequency distribution
was normal. Considerable transgressive segregation occurred: 74% of 2010 RILs had
lower HI than Tubbs.
2010 soft progeny had significantly lower (p ≤ 0.001) KWS and greater (p ≤ 0.001)
GMCS than the 2009 soft progeny. Mean KW was 33.4 mg for Tubbs and 40.8 mg for
NSA. Mean KWS was 38.6 and ranged from 25.6 to 48.7 mg. Mean GMC was 12.1%
115
for Tubbs and 11.5% for NSA, while the mean GMCS was 11.9% and ranged from
10.7 to 13.3%. The frequency distributions for KWS and GMCS were both normal. HIS
and GMC was again negatively correlated (p ≤ 0.05) (Table 6.2).
Table 6.1: Values and range of 23 end-use quality traits in Tubbs, NSA, and their RILs
in 2009 and 2010
Trait
Year
n
RILs
Min. Max. Mean
Kernel
texture
Tubbs
NSA
37.7 ± 17
58.1 ± 17
15.3
83.8
47.9
63.8 ± 17
40.2 ± 10
15.3
26.6
27.3
49.9
68.4
53.2
32.7
41.4
41.5
40.8 ± 10
11.5 ± 0.3
HIH+S
2009 265
H&S
HIS
KH
KWH+S(mg)
2009 138
2010 137
2009 265
S
S
H&S
KWS
2009 138
KW
2010 137
GMCH+S(%) 2009 265
S
S
H&S
33.4 ± 13
11.6 ± 0.3
33.4
25.6
10.9
53.2
48.7
11.5
41.9
38.6
11.5
GMCS
S
S
H&S
S
S
S
H&S
S
S
S
H&S
H&S
11
12.1 ± 0.3 11.5 ± 0.5 10.7
0.30 ± 0.09 0.51 ± 0.07 0.09
43
42.1
35
39.5
40.5
27.8
66.6
67.6
57.3
9
10.3
8.8
12.2
10.6
8
8.6
10.2
8.5
13.2
14.5
12.6
45.6 ± 2
70.1 ± 2
44.1
41.6 ± 1
61.3 ± 2
43.2
12
13.3
0.81
49.3
51
71.8
14.6
14.8
13.9
14.8
70.5
61.8
11.6
11.9
0.45
43.3
43.8
66
10.8
11
10.5
13.5
57.8
53.4
PPO
BFY (%)
FY (%)
GPC (%)
FPC (%)
FMC (%)
LUPP (%)
TUPP (%)
2009
2010
2009
2009
2010
2009
2009
2010
2009
2009
2009
2009
138
137
88
137
136
137
265
137
136
136
88
88
44.9 ± 16
45.0 ± 11
HI: Hardness index; KW: Kernel weight; GMC: Grain moisture content; PPO: Polyphenol oxidase; BFY: Break
flour yield; FY: Flour yield; GPC: Grain protein content; FPC: Flour protein content; FMC: Flour moisture content;
LUPP: Large unextractable polymeric protein; TUPP: Total unextractable polymeric protein; DS: Damaged starch;
TOAX: Total arabinoxylan; WEAX: Water-extractable arabinoxylan; WUAX: Water-unextractable arabinoxylan;
WSRC-W: Water solvent retention capacity-Whole wheat flour; SSRC-W: Sucrose solvent retention capacityWhole wheat flour; WSRC-R: Water solvent retention capacity- Refined flour; SSRC-R: Sucrose solvent retention
capacity- Refined flour; CD: Cookie diameter; CH: Cookie height; TAA: Total antioxidant activity; TPC: Total
phenolic content
116
Table 6.1: Values and range of 23 end-use quality traits in Tubbs, NSA, and their RILs
in 2009 and 2010 (Continued)
Trait
DS (%)
TOAX
(% XEQ, dwb)
WEA
(% XEQ, dwb)
WUAX
(% XEQ, dwb)
WSRC-W (%)
SSRC-W (%)
WSRC-R (%)
SSRC-R (%)
CD (mm)
CH (mm)
TAA
(µmole TEQ/100 g)
TPC
(mg GAE/mL)
Kernel
texture
Tubbs
NSA
2009 137
S
4.0 ± 0.2
4.7 ± 0.2
2009 137
S
6.8 ± 0.4
6.8 ± 0.3
2009 137
S
2009 137
S
6.4
2009
2009
2009
2009
2009
2009
136
136
136
136
135
135
S
S
S
S
S
S
2009
88
2009
88
Year
n
RILs
Min. Max. Mean
2.5
4.8
3.5
5
8.2
6.5
0.26
0.53
0.37
6.3
4.6
7.7
6.1
76.8 ± 3
103.2 ± 3
60.6 ± 1
85.7 ± 1
76.3
10.9
75.5 ± 0
98.5 ± 3
60.8 ± 2
83.0 ± 1
76.5
9.2
66.6
94.6
53.9
80.1
75.8
6.7
84.4
115
68.1
95.5
86.5
10.5
75.3
103
59
87.2
80.8
8.6
H&S
59.6 ± 3
67.7 ± 2
47.8
73.8
62.8
H&S
1265 ± 23
1273 ± 4
1111 1663
1306
0.38 ± 0.03 0.50 ± 0.01
117
2009 RILs
Tubbs:: 37.7 ± 17
NSA: 5 8.1 ± 17
Number of samples
80
60
40
20
0
20
30
40
a
50
HI
Number of samples
2009 RILs
14
40
12
20
10
00
80
8
60
6
40
4
20
2
0
35
30
70
2009 RILs
100
80
90
Tubbs: 45.0 ± 11
NSA: 4 0.2 ± 10
40
45
KW
W (mg)
b
Number of samples
60
50
55
Tubbss: 11.6 ± 0.3
NSA: 111.5 ± 0.3
80
60
40
20
0
11
c
11.2
11.4
111.6 11.8
GM C (%)
12
12.2
ons for HIH+SS (a), KWH+SS (b), and GM
MCH+S (c) inn the 2009 R
RIL
Figure 6.1: Distributio
population
n
118
2010 RILs
Tubbs: 44.9 ± 16
NSA: 63.8 ± 17
Number of samples
50
40
30
20
10
0
30
35
40
a
50
55
60
65
70
HI
2010 RILs
60
Number of samples
45
Tubbs: 33.4 ± 13
NSA: 40.8 ± 10
50
40
30
20
10
0
30
b
35
40
KW (mg)
2010 RILs
Number of samples
60
50
45
50
Tubbs: 12.1 ± 0.3
NSA: 11.5 ± 0.5
40
30
20
10
0
11
c
11.5
12
12.5
GMC (%)
13
13.5
Figure 6.2: Distributions for HIS (a), KWS (b), and GMCS (c) in 2010 RIL population
119
Table 6.2: Correlations between wheat quality traits of 2009 RILs
HIH+S
HIH+S
HIS
KW
GMC
PPO
FY
BFY
GPC
FPC
FMC
LUPP
TUPP
DS
TOAX
WEAX
WUAX
WSRC-W
SSRC-W
WSRC-R
SSRC-R
CD
CH
TAA
TPC
1***
-0.12*
-0.49***
0
-0.08
-0.59***
0.11
0.13
-0.08
0.14
0.21*
0.73***
-0.1
0.11
-0.11
0.49***
0.19*
0.58***
0.27**
-0.54***
0.49***
0.61***
-0.43***
HIS
1***
0.19*
-0.26**
0
-0.08
-0.59***
0.18*
0.13
-0.08
-0.1
-0.11
0.73***
-0.1
0.11
-0.11
0.49***
0.19*
0.58***
0.27**
-0.54***
0.49***
0.50***
-0.38**
KW
-0.12*
0.19*
-0.04
0.30**
0.05
-0.32***
0.06
0.04
-0.03
0.11
0.12
0.18*
-0.08
0
-0.09
0.09
0.13
0.18*
0.13
-0.19*
0.24**
-0.01
0.1
GMC
-0.49***
-0.26**
-0.04
0.05
0.24**
0.39***
-0.30***
-0.34***
0.05
-0.14
-0.17
-0.13
-0.13
-0.1
-0.13
-0.15
-0.25**
-0.32***
-0.25**
0.33***
-0.27**
-0.48***
0.26*
PPO
0
0
0.30**
0.05
0.02
-0.04
-0.14
-0.18
-0.30*
0.21*
0.17
0.09
0.04
-0.08
0.05
-0.08
0.24
0.17
0.13
-0.09
0.01
0.23*
0.13
FY
-0.08
-0.08
0.05
0.24**
0.02
0.68***
-0.30***
-0.29***
-0.21*
0.15
0.17
-0.03
-0.37***
-0.25**
-0.35***
-0.23**
-0.38***
-0.29***
-0.43***
0.34***
-0.39***
0.25
-0.30*
HI: Hardness index; KW: Kernel weight; GMC: Grain moisture content; PPO: Polyphenol oxidase; FY: Flour
yield; BFY: Break flour yield; GPC: Grain protein content; FPC: Flour protein content; FMC: Flour moisture
content; LUPP: Large unextractable polymeric protein; TUPP: Total unextractable polymeric protein; DS:
Damaged starch; TOAX: Total arabinoxylan; WEAX: Water-extractable arabinoxylan; WUAX: Waterunextractable arabinoxylan; WSRC-W: Water solvent retention capacity-Whole wheat flour; SSRC-W: Sucrose
solvent retention capacity-Whole wheat flour; WSRC-R: Water solvent retention capacity- Refined flour; SSRC-R:
Sucrose solvent retention capacity- Refined flour; CD: Cookie diameter; CH: Cookie height; TAA: Total
antioxidant activity; TPC: Total phenolic content
*, **, *** significant at (α=0.05), (α=0.01), and (α=0.001) respectively
120
Table 6.2: Correlations between wheat quality traits of 2009 RILs (Continued)
HIH+S
HIS
KW
GMC
PPO
FY
BFY
GPC
FPC
FMC
LUPP
TUPP
DS
TOAX
WEAX
WUAX
WSRC-W
SSRC-W
WSRC-R
SSRC-R
CD
CH
TAA
TPC
BFY
-0.59***
-0.59***
-0.32***
0.39***
-0.04
0.68***
-0.57***
-0.51***
-0.07
0.09
0.1
-0.29***
-0.17
-0.23**
-0.15
-0.36***
-0.43***
-0.47***
-0.44***
0.56***
-0.58***
-0.23
-0.08
GPC
0.11
0.18*
0.06
-0.30***
-0.14
-0.30***
-0.57***
0.90***
0.20*
-0.12
-0.03
-0.19*
0.01
0.15
-0.01
0.07
0.22*
0.1
0.19*
-0.28**
0.31***
-0.03
-0.01
FPC
0.13
0.13
0.04
-0.34***
-0.18
-0.29***
-0.51***
0.90***
0.19*
0.04
0.05
-0.19*
0.09
0.14
0.08
0.06
0.20*
0.09
0.22*
-0.25**
0.26**
-0.09
0.15
FMC
-0.08
-0.08
-0.03
0.05
-0.30*
-0.21*
-0.07
0.20*
0.19*
-0.33*
-0.26
0.04
-0.08
-0.1
-0.07
-0.09
-0.25**
-0.18*
-0.17*
0
0.03
-0.33*
0.29
LUPP
0.14
-0.1
0.11
-0.14
0.21*
0.15
0.09
-0.12
0.04
-0.33*
0.93***
-0.01
-0.11
0.09
-0.12
0.25
0.1
0.13
0.34*
-0.06
-0.06
0.11
-0.04
TUPP
0.21*
-0.11
0.12
-0.17
0.17
0.17
0.1
-0.03
0.05
-0.26
0.93***
-0.03
-0.16
0.15
-0.17
0.25
0.11
0.12
0.35*
-0.16
0
0.1
-0.08
DS
0.73***
0.73***
0.18*
-0.13
0.09
-0.03
-0.29***
-0.19*
-0.19*
0.04
-0.01
-0.03
0.06
0.09
0.05
0.46***
0.19*
0.56***
0.28***
-0.51***
0.44***
0.36*
-0.32*
121
Table 6.2: Correlations between wheat quality traits of 2009 RILs (Continued)
HIH+S
HIS
KW
GMC
PPO
FY
BFY
GPC
FPC
FMC
LUPP
TUPP
DS
TOAX
WEAX
WUAX
WSRC-W
SSRC-W
WSRC-R
SSRC-R
CD
CH
TAA
TPC
TOAX
-0.1
-0.1
-0.08
-0.13
0.04
-0.37***
-0.17
0.01
0.09
-0.08
-0.11
-0.16
0.06
0.25**
1***
0.12
0.26**
0.23**
0.35***
-0.23**
0.18*
-0.01
0.28
WEAX
0.11
0.11
0
-0.1
-0.08
-0.25**
-0.23**
0.15
0.14
-0.1
0.09
0.15
0.09
0.25**
0.16
0.15
0.37***
0.45***
0.61***
-0.39***
0.40***
-0.07
-0.17
WUAX
-0.11
-0.11
-0.09
-0.13
0.05
-0.35***
-0.15
-0.01
0.08
-0.07
-0.12
-0.17
0.05
1***
0.16
0.11
0.23**
0.19*
0.30***
-0.20*
0.15
-0.01
0.29*
WSRC-W
0.49***
0.49***
0.09
-0.15
-0.08
-0.23**
-0.36***
0.07
0.06
-0.09
0.25
0.25
0.46***
0.12
0.15
0.11
0.37***
0.56***
0.42***
-0.39***
0.41***
0.35*
-0.25
SSRC-W
0.19*
0.19*
0.13
-0.25**
0.24
-0.38***
-0.43***
0.22*
0.20*
-0.25**
0.1
0.11
0.19*
0.26**
0.37***
0.23**
0.37***
0.51***
0.62***
-0.44***
0.48***
0.08
0.01
WSRC-R
0.58***
0.58***
0.18*
-0.32***
0.17
-0.29***
-0.47***
0.1
0.09
-0.18*
0.13
0.12
0.56***
0.23**
0.45***
0.19*
0.56***
0.51***
0.71***
-0.63***
0.63***
0.31*
-0.25
122
Table 6.2: Correlations between wheat quality traits of 2009 RILs (Continued)
SSRC-R
HIH+S
0.27**
HIS
0.27**
KW
0.13
GMC
-0.25**
PPO
0.13
FY
-0.43***
BFY
-0.44***
GPC
0.19*
FPC
0.22*
FMC
-0.17*
LUPP
0.34*
TUPP
0.35*
DS
0.28***
TOAX
0.35***
WEAX
0.61***
WUAX
0.30***
WSRC-W 0.42***
SSRC-W 0.62***
WSRC-R 0.71***
SSRC-R
CD
-0.64***
CH
0.65***
TAA
0.12
TPC
-0.16
CD
-0.54***
-0.54***
-0.19*
0.33***
-0.09
0.34***
0.56***
-0.28**
-0.25**
0
-0.06
-0.16
-0.51***
-0.23**
-0.39***
-0.20*
-0.39***
-0.44***
-0.63***
-0.64***
CH
0.49***
0.49***
0.24**
-0.27**
0.01
-0.39***
-0.58***
0.31***
0.26**
0.03
-0.06
0
0.44***
0.18*
0.40***
0.15
0.41***
0.48***
0.63***
0.65***
-0.78***
-0.78***
-0.13
0.12
0.24
-0.26
TAA
0.61***
0.50***
-0.01
-0.48***
0.23*
0.25
-0.23
-0.03
-0.09
-0.33*
0.11
0.1
0.36*
-0.01
-0.07
-0.01
0.35*
0.08
0.31*
0.12
-0.13
0.12
TPC
-0.43***
-0.38**
0.1
0.26*
0.13
-0.30*
-0.08
-0.01
0.15
0.29
-0.04
-0.08
-0.32*
0.28
-0.17
0.29*
-0.25
0.01
-0.25
-0.16
0.24
-0.26
-0.19
-0.19
Table 6.3: Correlations between wheat quality traits of 2010 RILs
HIS
HIS
KW
GMC
GPC
BFY
KW
-0.08
GMC
-0.20*
0.19*
GPC
0.21*
-0.42***
-0.33***
-0.08
-0.20*
0.19*
0.21*
-0.42*** -0.33***
-0.60*** 0.14
0.36*** -0.47***
*, **, *** significant at (α=0.05), (α=0.01), and (α=0.001) respectively
BFY
-0.60***
0.14
0.36***
-0.47
123
6.2 Polyphenol oxidase activity
Polyphenol oxidase (PPO) activity is an enzyme associated with undesirable
darkening in many wheat products, particularly Asian noodles. Mean PPO activity for
90 hard and soft lines randomly selected from the 2009 RILs, including their parents,
determined by the L-DOPA assay are reported in Table 6.1 and Figure 6.3. Mean PPO
activity was 0.30 for Tubbs and 0.51 for NSA. Mean PPO activity for the tested
progeny was 0.45 and ranged from 0.09 to 0.81.The frequency distribution for PPO
activity of the tested RILs was normal. Transgressive segregation for PPO activity of
2009 tested RILs was observed. Approximately 23% of the tested RILs had lower
mean PPO activity than Tubbs. In addition, there was one line (ID: 189) that had very
low PPO activity (0.09). ANOVA results (data not shown) indicated that there was no
significant difference (p > 0.05) in PPO activity between hard and soft progeny.
2009 RILs
Number of samples
25
Tubbs: 0.3 ± 0.09
NSA: 0.51 ± 0.07
20
15
10
5
0
0.1
0.2
0.3
0.4
0.5
0.6
PPO
Figure 6.3: Distribution for PPO activity in 2009 RILs
0.7
0.8
0.9
124
6.3 Milling performance test
Milling performance was tested on soft progeny only and from both harvest years.
2009 harvested lines
Break flour yield (BFY: i.e the yield of flour from the break side of the mill) is a good
predictor of overall soft wheat quality. Good soft wheat quality is generally associated
with high BFY (Faridi et al., 1994). The parents had very similar BFY values: 43.0%
for Tubbs and 42.1% for NSA Table 6.1. Mean BFY for the RILs was 43.3% and
ranged from 35.0 to 49.3%. The frequency distribution for BFY was normal (Figure
6.4a). Transgressive segregations were observed. Approximately 55% of the tested
RILs had greater BFY values than the soft parent, Tubbs.
Total flour yield (FY) from an experimental mill can be used to predict the straight
grade flour yield from commercial milling. Total FY is the sum of flour coming from
the break and reduction sides of the mill and is < 100% because of the removal of bran
and germ. Wheat that produces higher FY is more desirable. 2009 soft-grained RILs
were milled to obtain straight-grade flours. The parents had very similar FY values:
66.6% for Tubbs and 67.6% for NSA (Table 6.1).Mean FY for the RILs was 66.0%
and ranged from 57.3 to 71.8%. The frequency distribution for FY was skewed
towards higher values (Figure 6.4b). Transgressive segregation occurred as 36% of the
125
tested RILs had greater FY than Tubbs. In this study, correlation between HIS and FY
was not significant (p > 0.05) (Table 6.2). However, there were reports that kernel
hardness was negatively correlated with FY (Brevis et al., 2008; Kouřimská et al.,
2006; Martin et al., 2001). In contrast, Pasha et al. (2010) and Hruskova and Svec
(2009) reported a positive correlation between kernel hardness and FY in . FY could
be influenced by the amount of starchy endosperm in kernel, thickness of bran, size of
germ, and kernel hardness (Posner and Hibbs, 1997). In this study, FY was positively
correlated (p ≤ 0.001) with BFY, which was in accordance with the results reported by
Morris et al. (2004).
2010 harvested lines
Mean BFY was 39.5% for Tubbs and 40.5% for NSA. Mean BFY of the RILs was
43.8% and ranged from 27.8 to 51.0% (Table 6.1).The distributions for BFY for the
tested RILs were skewed towards the higher values (Figure 6.4c). Transgressive
segregation was also observed. Approximately, 88% of the test RILs had higher BFY
than Tubbs. The BFY values for parental lines harvested in 2009 and 2010 were
similar. ANOVA results (data not shown) indicated that the difference between BFY
values for 2009 and 2010 soft progeny were not significant (p >0.05). As expected,
HIS was negatively correlated with BFY for both 2009 and 2010 tested RILs (p ≤
0.001; Table 6.2). Consequently, softer progeny generated more break flour than
harder progeny in this sample set. Martin et al. (2001) and Brevis et al. (2008) also
126
observed a negative correlation between kernel hardness and BFY in hard red spring
wheat. However, Morris et al. (2005) found no correlation between kernel hardness
and BFY in soft wheat cultivars. However, the Morris et al. (2005) study included
both soft white common wheat and soft white club wheat. Club wheats (Triticum
aestivum ssp.compactum) have excellent milling performance despite a physically
harder kernel, possibly a result of their divergent kernel morphology. Consequently
the results of Morris et al. (2005) is may not reflect the commonly observed negative
relationship between kernel hardness and BFY in modern soft white common wheats.
127
2009 RILs
Tubbs: 43.0
NSA: 42.1
Number of samples
40
30
20
10
0
36
38
40
42
44
BFY (%)
a
46
2009 RILs
50
Tubbs: 66.6
NSA: 67.6
50
Number of samples
48
40
30
20
10
0
58
60
62
b
64
66
FY (%)
68
70
72
Number of samples
2010 RILs
c
35
30
25
20
15
10
5
0
Tubbs: 39.5
NSA: 40.5
28 30 32 34 36 38 40 42 44 46 48 50 52
BFY (%)
Figure 6.4: Distributions for FY and BFY in 2009 and 2010 RIL population
128
6.4 Grain and flour protein contents
Wheat protein content is one of the key factors affecting both nutritional value and the
end uses of wheat. Consequently, protein content is used to grade wheat. Generally,
wheat with higher protein content gets a better price than wheat with lower protein
content. It is therefore necessary to determine protein content in wheat grain and flour.
Although kernel protein content is largely determined by environmental conditions
there is an underlying genetic component, for example the presence of the GPC-B1
gene is associated with higher grain protein (Anonymous, 2012)
2009 harvested grain
Grain protein content (GPC) and flour protein content (FPC) for the all 2009 RILs and
their parents are reported in Table 6.1 and Figure 6.5a and b. Mean GPC was 9.0% for
Tubbs and 10.3% for NSA, while mean GPC for the RILs was 10.8% and ranged from
8.8 to 14.6%. Mean FPC was 8.6% for Tubbs and 10.2% for NSA, while mean FPC
for the RILs was 10.5% and ranged from 8.5 to 13.9%. Wheat flour had lower protein
content than wheat grain due to removal of bran, aleurone, and embryo during milling
(Posner, 2000). Straight-grade flour at 70% extraction has 0.9-1.9% less protein
content than wheat (Tian, 2006). In this study, flour however had 0.3% less protein
content than wheat. NSA had slightly higher GPC and FPC than Tubbs, conforming to
the generally held assumption that hard wheat has higher protein content than soft
wheat (Mikulikova, 2007), although there can be great overlap. For example ANOVA
129
results (data not shown) indicated that the difference in GPC and FPC between hard
and soft progeny was not significant (p > 0.05). The distributions for GPC and FPC of
the RILs were skewed towards lower values. Transgressive segregation was observed
for both GPC and FPC.GPC and FPC were negatively correlated with FY and BFY (p
≤ 0.001; Table 6.2). GPC was positively correlated with HIS (p ≤ 0.05) and FPC (p ≤
0.001) but the correlation between FPC and HIS was not significant (p >0.05).
Hruskova and Svec (2009) also observed positive correlations between kernel
hardness and GPC as in this study, but also with FPC, which was not observed here.
2010 harvested grain
Mean GPC was 12.2% for Tubbs and 10.6% for NSA, while mean GPC for the RILs
was 11.0% and ranged from 8.0 to 14.8% (Table 6.1). GPC for 2010 Tubbs was higher
than that of 2009 Tubbs but GPC for 2010 NSA was similar to that of 2009 NSA.
ANOVA results (data not shown) revealed that 2010 soft progeny had significantly
lower (p ≤ 0.001) GPC than 2009 soft progeny. It is known that protein content is
highly influenced by environment with some effect from genetic and little impact from
genetic x environment interaction (Panozzo and Eagles, 2000; Rao et al., 1993). Rate
and time of nitrogen application, nitrogen content in soil, water availability, and
temperature and light intensity are the major factors influence protein content in wheat
(Blanco et al., 2000; Rao et al., 1993). The frequency distribution for GPC of the RILs
was skewed towards lower values (Figure 6.5c). Transgressive segregation was
130
observed. GPC for the tested RILs was negatively correlated with KW (p ≤ 0.001)
supporting the general observation that smaller, less well filled, kernels tend to have
higher protein concentration. 2010 GPC was positively correlated with HIS (p ≤ 0.05)
as was also observed for the 2009 samples.
131
2009 RILs
Number of samples
60
Tubbs: 9.0
NSA: 10.3
50
GPC (%)
40
30
20
10
a
Number of samples
15.0
14.5
GPC (%)
2009 RILs
Tubbs: 8.6
NSA: 10.2
35
30
25
20
15
10
5
0
FPC (%)
8.5 9.0 9.5 10.010.511.011.512.012.513.013.514.0
FPC (%)
b
2010 RILs
35
Number of samples
14.0
13.5
13.0
12.5
12.0
11.5
11.0
10.5
10.0
9.5
9.0
0
Tubbs: 12.2
NSA: 10.6
30
25
20
15
10
5
c
15.0
14.5
14.0
13.5
13.0
12.5
12.0
11.5
11.0
10.5
10.0
9.5
9.0
8.5
0
GPC (%)
Figure 6.5: Distributions for grain and flour protein contents in 2009 and 2010 RIL
population
132
6.5 Polymeric proteins
Polymeric proteins are groups of glutenin subunits linked together by disulfide bonds
(Lemelin et al., 2005). Based on solubility in SDS-phosphate buffer, polymeric
proteins can be classified into two groups: extractable polymeric protein and
unextractable polymeric protein (UPP). UPP is important in determining gluten
strength and end-use quality, particularly breadmaking quality. Dough made from
wheat with greater amount of UPP is generally more elastic and requires longer
mixing time (Gupta et al., 1993).
Mean percentage of large unextractable polymeric protein (LUPP) and percentage of
total unextractable polymeric protein (TUPP) for 90 lines randomly selected from the
2009 RILs including their parents are reported in Table 6.1 and Figure 6.6. Mean
LUPP was 45.6% for Tubbs and 70.1% for NSA, reflecting the higher strength of the
HRW parent. Mean LUPP for the RILs was 57.8% and ranged from 44.1 to 70.5%.
Mean TUPP was 41.6% for Tubbs and 61.3% for NSA, while mean TUPP for the
RILs was 53.4% and ranged from 43.2 to 61.8%. The frequency distributions for
LUPP and TUPP were normal. No transgressive segregation was observed for both
traits in this population. ANOVA results (data not shown) indicated that there was no
difference (p > 0.05) in the amount of LUPP between soft and hard progeny.
However, hard progeny contained significantly greater (p ≤ 0.05) amount of TUPP
than soft progeny. Hard wheat flour has been reported to contain more UPP than soft
133
wheat flour (Butow et al., 2002). There was no correlation (p >0.05) between LUPP
and HIH+S or GPC in this population. However, there was a weak correlation (P ≤0.05)
between TUPP and HIH+S. LUPP is positively correlated with TUPP (p ≤ 0.001).
Kuktaiteet al. (2004) reported that strong flour (containing HMW-GS 5+10) had
higher percentages of LUPP and TUPP than weak flour (containing HMW-GS 2+12).
It is still unknown how the results was influenced by HMW-GS since identification of
HMW-GS was not been done in this study.
2009 RILs
Tubbs: 45.6 ± 2
NSA: 70.1 ± 2
Number of samples
50
40
30
20
10
0
45
50
a
55
60
LUPP (%)
65
70
75
2009 RILs
Number of samples
30
Tubbs: 41.6 ± 1
NSA: 61.3 ± 2
25
20
15
10
5
0
44
b
46
48
50
52 54
TUPP (%)
56
58
60
62
Figure 6.6: Distributions for LUPP (a) and TUPP (b) in 2009 RIL population
134
6.6 Damaged starch
Damaged starch (DS) is another indicator of soft wheat quality. Soft wheat produces
less DS because it is easy to break down and its endosperm fractures through cell
walls during milling. In contrast, hard wheat is more difficult to break down and its
endosperm fractures at cell walls giving more DS (Pasha et al., 2010). DS was tested
on the 2009 soft progeny (137 lines) and their parents. Mean DS was 4.0% for Tubbs
and 4.7% for NSA, while mean DS for the RILs was 3.5% and ranged from 2.5 to
4.8% (Table 6.1). As expected, Tubbs generated less DS than NSA although the
values were close reflecting the relatively hard nature of Tubbs as a soft wheat and the
relatively soft nature of NSA as a hard wheat. The frequency distribution for mean DS
of RILs was skewed towards the lower values (Figure 6.7). Transgressive segregation
was observed. Approximately, 90% of the soft-grained RILs had lower DS values than
Tubbs. There were only 2 soft-grained RILs that had higher DS value than NSA. DS
was positively correlated with HIS (p ≤ 0.001),while negatively correlated with BFY
(p ≤ 0.001; Table 6.2) indicating, unsurprisingly, that wheat with harder kernel texture
generated more DS but less BFY due to its strong adhesion between starch granules
and protein matrix.
135
2009 RILs
Number of samples
70
Tubbs: 4.0 ± 0.2
NSA: 4.7 ± 0.2
60
50
40
30
20
10
0
2.8
3.2
3.6
4
4.4
DS (%) 4.8
5.2
Figure 6.7: Distributions for DS in 2009 RIL population
6.7 Arabinoxylans
Arabinoxylans (AX) are the major nonstarch polysaccharides of wheat grain cell
walls. Based on water solubility, AX can be divided into two fractions: waterextractable arabinoxylan (WEAX), and water-unextractable arabinoxylan (WUAX).
WEAX contains ferulic acid residues which can interact with other ferulic acid
residues or protein side chains forming a large polymeric gel under oxidizing
conditions. The gel entraps water and generates higher viscosity (Bettge and Morris,
2007) even up to the point of creating a solid-like substance. WUAX can absorb water
up 10 times their own weight (Bettge and Morris, 2007; D’Appolonia and Kim, 1976;
Kulp, 1968). Both WEAX and WUAX have significant impacts on the end uses of soft
wheat flour. It is therefore important to determine the distribution of AX in wheat
flour.
136
Whole wheat flour from 2009 soft progeny (137 lines) and their parents were
determined for total arabinoxylan (TOAX), water-extractable arabinoxylan (WEAX),
and water-unextractable arabinoxylan (WUAX) contents. Both parents had similar
mean TOAX values: 6.8% xylose equivalent dry weight basis (XEQ dwb), while mean
TOAX of the RILs was 6.5% XEQ dwb and ranged from 5.0 to 8.2% XEQ dwb
(Table 6.1). The frequency distribution for TOAX of 2009 soft wheat RILs was
normal (Figure 6.8a). Transgressive segregation was observed as 68% of the softgrained RILs had lower TOAX values than their parents.
Mean WEAX was 0.38% XEQ dwb for Tubbs and 0.50% XEQ dwb for NSA, while
mean WEAX of the 2009 RILs was 0.37% XEQ dwb and ranged from 0.26 to 0.53%
XEQ dwb (Table 6.1). The frequency distribution for WEAX of the RILs was skewed
towards lower values (Figure 6.8b). Transgressive segregation was observed as 63%
of the tested RILs had lower WEAX values than NSA. Mean WUAX was 6.4% XEQ
dwb for Tubbs and 6.3% XEQ dwb for NSA, while mean WUAX of the 2009 softgrained RILs was 6.1% XEQ dwb and ranged from 4.6 to 7.7% XEQ dwb (Table 6.1).
The frequency distribution for WUAX of 2009 soft wheat RILs was normal (Figure
6.8c). Transgressive segregation was observed as 64% of soft-grained RILs had lower
WUAX values than Tubbs. Bettge and Morris (2000) reported a positive correlation
between TOAX and WEAX with HI which was not observed in this study. However,
137
TOAX and WUAX were negatively correlated with FY (p ≤ 0.001). WEAX was
negatively correlated with both BFY and FY (p ≤ 0.01).
138
2009 RILs
Number of samples
30
Tubbs: 6.8 ± 0.4
NSA: 6.8 ± 0.3
25
20
15
10
5
0
5.25 5.50 5.75 6.00 6.25 6.50 6.75 7.00 7.25 7.50 7.75 8.00 8.25
a
TOAX (% XEQ, dwb)
Number of samples
25
2009 RILs
Tubbs: 0.38 ± 0.03
NSA: 0.50 ± 0.01
20
15
10
5
0
0.260.280.300.320.340.360.380.400.420.440.460.480.500.520.54
b
WEAX (% XEQ, dwb)
Number of samples
30
2009 RILs
Tubbs: 6.4
NSA: 6.3
25
20
15
10
5
0
4.75 5.00 5.25 5.50 5.75 6.00 6.25 6.50 6.75 7.00 7.25 7.50 7.75
c
WUAX (% XEQ, dwb)
Figure 6.8: Distributions for TOAX (a), WEAX (b), and WUAX (c) in 2009 RIL
population
139
6.8 Solvent retention capacity test
Solvent retention capacity (SRC) test is a method for evaluating soft wheat flour
functionality as detailed in Section 3.2.3.7. In this study, water SRC (WSRC) and
sucrose SRC (SSRC) tests were employed to determine characteristic of whole wheat
and refined flours from the 2009 soft wheat RILs (136 lines) and their parents. In
general, lower SRC values are indicative superior soft wheat quality
Mean WSRC of whole wheat flour (WSRC-W) was 76.8% for Tubbs and 75.5% for
NSA, while mean WSRC-W for the RILs was 75.3% and ranged from 66.6 to 84.4%
(Table 6.1). Mean WSRC of refined flour (WSRC-R) was 60.6% for Tubbs and 60.8%
for NSA, while mean WSRC-R for the RILs was 59.0% and ranged from 53.9 to
68.1% (Table 6.1). It is unsurprising that mean WSRC-W was greater than mean
WSRC-R because whole-wheat contains bran and germ. These components have a
high solvent absorption capacity due to the high content of non-starchy
polysaccharides. Tubbs and NSA had similar WSRC-W and WSRC-R values. The
frequency distributions for WSRC-W and WSRC-R of the RILs were skewed towards
lower values (Figure 6.9). Considerable transgressive segregations were observed. For
whole-wheat flour, 75% of the tested RILs had lower WSRC values than Tubbs. For
refined flour, 82% of the tested RILs had lower WSRC values than Tubbs. Both
WSRC-W and WSRC-R were positively correlated with HIS and DS (p ≤ 0.001), and
negatively correlated with BFY (p ≤ 0.001; Table 6.2). As mentioned in Section 6.6,
140
harder wheat produces more DS than softer wheat during milling and DS absorbs
more water than undamaged starch. Consequently, flour with higher DS will have
higher WSRC value than flour with lower DS. WSRC-R was also positively correlated
with TOAX (p ≤ 0.01), WEAX (p ≤ 0.001), and WUAX (p ≤ 0.05). This is very likely
a result of the high water absorption properties of AX. In contrast, WSRC-W showed
no significant correlation with AX. The influence of the bran is most likely the source
of the poor relationship.
2009 RILs
Number of samples
40
Tubbs: 76.8 ± 3
NSA: 75.5 ± 0
30
20
10
0
67
69
71
a
75 77 79
WSRC‐W (%)
2009 RILs
70
Number of samples
73
81
83
85
Tubbs: 60.6 ± 1
NSA: 60.8 ± 2
60
50
40
30
20
10
0
54
b
56
58
60
62
64
66
68
70
WSRC‐R (%)
Figure 6.9: Distributions for WSRC-W (a) and WSRC-R (b) in 2009 RIL population
141
SSRC is reported to be associated with AX content (Kweon et al., 2011). Mean SSRC
of whole wheat flour (SSRC-W) was 103.2% for Tubbs and 98.5% for NSA, while
mean whole wheat flour SSRC of the RILs was 103.0% and ranged from 94.6 to
115.2% (Table 6.1). Mean SSRC of refined flour (SSRC-R) was 85.7% for Tubbs and
83.0% for NSA, while mean SSRC of the RILs was 87.2% and ranged from 80.1 to
95.5% (Table 6.1). SSRC-W was higher than SSRC-R. Tubbs had slightly greater
SSRC-W and SSRC-R values than NSA. The frequency distributions for whole wheat
and refined flour SSRCs were skewed to the lower values (Figure 6.10). Transgressive
segregations for SSRC-W and SSRC-R were also observed. For whole wheat flour,
57% of the tested RILs had lower SSRC values than Tubbs. For refined flour, 33% of
the tested RILs had lower SSRC values than Tubbs. Both SSRC-W and SSRC-R were
positively correlated with HIS (p ≤ 0.05 and 0.01, respectively) while negatively
correlated with BFY and FY (p ≤ 0.001; Table 6.2). As expected, both SSRC-W and
SSRC-R were positively correlated TOAX, WEAX, and WUAX (p ≤ 0.01 or 0.001).
WSRC-W and SSRC-W were positively correlated (p ≤ 0.001) with WSRC-R and
SSRC-R. Souza et al. (2011) also reported a positive correlation between whole-wheat
flour WSRC and SSRC.
142
2009 RILs
Number of samples
40
Tubbs: 103.2 ± 3
NSA: 98.5 ± 3
30
20
10
0
96 98 100 102 104 106 108 110 112 114 116
SSRC‐W (%)
a
2009 RILs
Number of samples
35
30
Tubbs: 85.7±1
NSA: 83.0±1
25
20
15
10
5
0
82
b
84
86
88
90
SSRC‐R (%)
92
94
96
Figure 6.10: Distributions for SSRC-W (a) and SSRC-R (b) in 2009 RIL population
6.9 Sugar-snap cookie
The sugar-snap cookie method is another method used to predict the general
performance of soft wheat flour. High quality soft wheat flour generally produces
cookies with large diameter and low thickness. In this study, diameter and height of
cookies made from the 2009 soft wheat RILs (135 lines) and their parents are reported
in Table 6.1 and Figure 6.11. Mean cookie diameter (CD) was 76.3 mm for Tubbs and
76.5 mm for NSA, while mean CD of the tested RILs was 80.8 mm and ranged from
143
75.8 to 86.5 mm. Mean cookie height (CH) was 10.9 mm for Tubbs and 9.2 mm for
NSA, while mean CH of the tested RILs was 8.6 mm and ranged from 6.7 to 10.5 mm.
The frequency distribution for the CD and CH of tested RILs were normal and
transgressive segregations were observed for both traits.
As expected, cookies with larger CD had shorter CH as indicated by the negative
correlation (p ≤ 0.001) between CD and CH (Table 6.2). CD was also negatively
correlated with HIS (p ≤ 0.001), GPC (p ≤ 0.01), FPC (p ≤ 0.01), DS (p ≤ 0.001),
TOAX (p ≤ 0.01), WEAX (p ≤ 0.001), and both WSRC and SSRC of both whole and
refined flours. In contrast, CD was positively correlated with BFY (p ≤ 0.001) and FY
(p ≤ 0.001). Gaines (2004) also observed positive correlations between CD and BFY
and FY, and negative correlations between CD and FPC and SSRC-R. Guttieri et al.
(2001) reported negative correlation between CD and FPC and SSRC-R but not
WSRC-R. Previous studies also showed negative correlations between CD and SSRCW and SSRC-R (Gaines, 2004; Moiraghi et al., 2011; Souza et al., 2011; Zhang et al.,
2008). Damaged starch, protein, and AX absorb high amounts of water. Consequently,
less water is available for sugar to interact and form syrup during baking resulting in
more viscous dough and lower diameter cookies (Donelson and Gaines, 1998; Slade
and Levine, 1994).
144
Surprisingly, contrary results were observed in this study. It is accepted that SSRC-R
is a good predictor of CD. Flour with low SSRC-R value is generally reported to
produce cookies with large diameters. In Section 6.8, only 33% of the tested RILs had
lower SSRC-R values than Tubbs. However, 96% of the tested RILs had higher CD
than Tubbs. This observation may reflect anecdotal evidence (Ross, 2012) that
germplasm from the OSU soft wheat breeding program often has higher CD values
than the SSRC-R would predict in independent testing by industrial partners.
2009 RILs
Number of samples
30
25
Tubbs: 76.3
NSA: 76.5
20
15
10
5
0
76 77 78 79 80 81 82 83 84 85 86 87
CD (mm)
a
2009 RILs
Number of samples
50
Tubbs: 10.9
NSA: 9.2
40
30
20
10
0
7
b
7.5
8
8.5 9 9.5
CH (mm)
10 10.5 11
Figure 6.11: Distributions for CD (a) and CH (b) in 2009 RIL population
145
6.10 Total antioxidant activity and total phenolic content
The DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assay was used to
determine total antioxidant activity (TAA). Mean TAA for 90 lines randomly selected
from the 2009 RILs including their parents determined are reported in Table 6.1 and
Figure 6.12a. Mean TAA was 59.6µmole trolox equivalents (TEQ)/100 g for Tubbs
and 67.7 µmole TEQ/100 g for NSA, while mean TAA of the RILs was 62.8 µmole
TEQ/100 g and ranged from 47.9 to 73.8 µmole TEQ/100 g. The frequency
distribution for TAA of the tested RILs was normal. Transgressive segregation was
observed. Approximately, 23% of 2009 tested RILs had greater TAA values than
NSA. There was a positive strong correlation between TAA and HIS+H (p ≤ 0.001;
Table 6.2).
Folin-Ciocalteu’s reagent was used to determine total phenolic content (TPC). Mean
TPC for the same90 lines randomly selected from the 2009 RILs including their
parents determined are reported in Table 6.1 and Figure 6.12b. The mean TPC was
1265µg gallic acid equivalents (GAE)/g for Tubbs and 1273 µg GAE/g for NSA,
while mean TPC of the RILs was 1301 µg GAE/g and ranged from 1111 to 1663 µg
GAE/g. The frequency distribution for TPC of the tested RILs was skewed to the
higher values. Transgressive segregation was observed. Approximately, 67% of the
tested RILs had greater TPC values than NSA. TPC is negatively correlated with
HIS+H (p ≤ 0.001; Table 6.2).
146
No correlation between TAA and TPC was found in this study. This finding was
supported by previous studies (Liyana-Pathirana and Shahidi, 2005; Qusti et al., 2010;
Zielinski and Kozlowska, 2000). However, several studies reported correlations
between TAA and TPC in some other plants (Adom and Liu, 2002; Emmons et al.,
1999; Qusti et al., 2010). Differences in extraction method and methods used to
determine TAA and TPC makes it difficult to compare the results between studies
(Liyana-Pathirana and Shahidi, 2005). TAA and TPC values between read and white
progeny were compared. ANOVA results (data not shown) indicated that red progeny
tended to have higher TAA and TPC values than white progeny but the differences
were not significant (p > 0.05). These results were supported by Mpofu et al. (2006),
Yu et al. (2002), and Beta et al. (2005) who found no significant difference in TAA or
TPC between red and white wheats in their studies.
147
2009 RILs
Number of samples
30
25
20
Tubbs: 59.6 ± 3
NSA: 67.7 ± 2
15
10
5
0
45
50
55
60
65
70
75
80
TAA (µmole TEQ/100 g)
Number of samples
30
2009 RILs
Tubbs: 1265 ± 23
NSA: 1273 ± 4
25
20
15
10
5
0
TPC (mg GAE/mL)
Figure 6.12: Distributions for TAA (a) and TPC (b) in 2009 RIL population
148
Chapter 7: Results and discussion: Quantitative trait locus analysis
The phenotypic data from Chapter 6 were utilized to perform quantitative trait locus
(QTL) analysis. Composite interval mapping (CIM) identified a total of significant 28
QTLs (LOD score ≥ 2.5) on 10 wheat chromosomes for 15 end-use quality and
nutrition traits in 2 harvest years (Table 7.1). These QTLs explained up to 40.9% of
the phenotypic variance.
7.1 Kernel characteristics
Kernel hardness
As noted in Section 6.1, traits with an “H+S” subscript represent traits for both hard
and soft progeny, while traits with either an “H” or an “S” subscript represent traits for
hard or soft progeny only, respectively. No QTL related to hardness index (HIH+S) was
detected for the 2009 RILs. However, when QTL analysis was performed on only the
2009 soft progeny (HI ≤ 50), one QTL (wPt2014-6A) associated with HIS for 2009
RILs was detected on chromosome 6AC with a LOD score of 2.6 (Table 7.1). This
QTL explained 7.0% of the phenotypic variance in the trait with a positive additive
effect indicating that the increase in HI was due to the NSA allele at this QTL.
It was mentioned in Section 6.1 that 14 RILs considered soft-grained (HI ≤ 50) in the
2009 harvest year had HI > 50 in the 2010 harvest year. When QTL analysis was
149
conducted on 2010 soft progeny along with those 14 lines, two QTLs (ORW5-7DL
and wPt1628cl5-7DL) associated with HI were detected on chromosome 7DL with
LOD scores of 3.6 and 2.9, respectively. These QTLs explained up to 10.1% of the
phenotypic variance in the trait with negative additive effects indicating that the
decrease in HI was due to the Tubbs alleles at these QTLs. However, two different
QTLs (wPt7785-7AS and tPt2203-5B) were identified when those 14 lines were
excluded from QTL analysis. wPt7785-7AS was found on chromosome 7AC with a
LOD score of 3.0. This QTL explained 8.5% of the phenotypic variance in the trait
with a positive additive effect indicating that the increase in HI was due to the NSA
allele at this QTL. tPt2203-5B was found on chromosome 5BS with a LOD score of
2.6. This QTL explained 7.3% of the phenotypic variance in the trait with a negative
additive effect indicating that the decrease in HI was due to the Tubbs allele at this
QTL.
As expected, NSA possessed alleles that increased kernel hardness, while Tubbs
possessed alleles that decreased kernel hardness. It is known that the difference in
kernel hardness between hard and soft wheats is mainly controlled by the Ha locus on
chromosome 5DS. However, no QTL was found on chromosome 5DS in this study.
This might have been expected, firstly as there was no reason to expect that the Ha
locus controls variance in softness amongst soft wheats, and secondly because of the
lack of markers at the region for these parents.
150
In summary, in this study QTLs for HI were found on chromosomes 6AC, 7DL, 7AC,
and 5BS. Previous studies also reported QTLs for kernel hardness on other wheat
chromosomes. Arbelbide and Bernardo (2006) reported two QTLs associated with
kernel hardness on chromosome 1A and 5D in 373 inbreds obtained from 158
different crosses. Groos et al. (2004) identified kernel hardness QTLs on
chromosomes 1A, 1B, 2A, 2B, 2D, 3A, 3B, 4A, 5A, 5B, 5D, 6A, 6B, and 6D in a hard
x hard cross. Sun et al. (2010) also reported QTLs for kernel hardness on chromosome
1D, 5B, 5D, and 7A in a soft x hard cross. Campbell et al.(2001) found QTLs related
to kernel hardness on chromosomes 2A, 2D, 5D, and 6B in a soft x hard cross.
Campbell et al.(2001) also reported that the puroindoline locus on 5DS explained
more than 60% of variation for kernel hardness. It is valuable to note here that the
study of Campbell et al (2001) made no attempt to discount the effect of the 5DS Ha
locus by only looking at the soft-grained progeny. Therefore as the Ha locus is the
dominant locus associated with the difference in kernel texture between hard and soft
wheats it is hardly surprising that the Ha locus accounted for more than 60% of
variation for kernel hardness. The choice to look only at the soft-grained progeny in
this thesis was a deliberate strategy designed to assess the genetic basis of variation in
kernel texture within soft-grained wheats.
Due to the dominant effect of the kernel hardness genes at the Ha locus, the wPt20146A marker was not identified when the QTL analysis for HIH+S in the 2009 progeny
151
was performed. However, when the 2009 hard progeny were excluded, and by
assumption the effect of the Ha locus, the wPt2014-6A marker was detected. Wang et
al. (2012) eliminated the variation of the kernel hardness genes at Ha locus by
crossing between soft and extra-soft wheat. They reported five QTLs associated with
kernel hardness on chromosomes 4BS, 4DS, 5DL, and 7DS.
Kernel weight (KW)
There was no QTL associated with KWH+S detected for the 2009 RILs. However, three
QTLs associated with KWS for the 2010 soft RILs, with those 14 lines with HI > 50,
were detected on three different chromosomes (Table 7.1). BARC356-3A was
identified on chromosome 3AS with a LOD score of 3.3. wPt7299-7A was found on
chromosome 7AC with a LOD score of 2.6. Both QTLs explained up to 9.3% of the
phenotypic variance in the trait with negative additive effects indicating that the
decrease in KW was due to the Tubbs alleles at these QTL. The last QTL,
wPt1726cl2-1BSat, was detected on chromosome 1B with a LOD score of 3.5,
explaining 9.9% of the phenotypic variance in the trait with a positive additive effect
indicating that the increase in KW was due to the NSA allele at this QTL.
In summary, in this study QTL for KW were found on chromosomes 1B, 3AS, and
7AC. The results of this study were supported by Sun et al. (2010) who also reported
152
QTLs for KW on chromosomes 1B, and 7A, as well as on chromosomes 4B, 5A, 6A,
in a soft x hard cross. Groos et al. (2003) reported QTLs for 1000-kernelweight on 3A
and 7A in the Renan x Récital cross. Wang et al. (2012) also found a QTL associated
with 1000-kernelweight on chromosome 7A in a soft x extra-soft cross.
Grain moisture content (GMC)
The QTL analysis for GMC was not performed because GMC was mainly influenced
by environment i.e. rain fall during wheat harvest and storage condition.
Table 7.1: QTLs detected by composite interval mapping in the T x N population
Trait
Year Chrom. QTL peak Closest marker to peak LOD 2-LOD Sup. L. R² (%) Add. E. Par. con.
HIS
2009 6AC
19.2
wPt2014-6A
2.6
6.8 - 21.2
7.0
2.0
NSA
HIS*
2010 7DL
7DL
2.0
9.7
ORW5-7DL
wPt1628cl5-7DL
3.6
2.9
0 - 4.5
8.7 - 10.4
10.1
7.8
-2.3
-2.0
Tubbs
Tubbs
HIS **
2010 7AC
5BS
2010 3AS
7AC
1B
2009 1B
2009 3B
3D
2009 1B
2009 5BS
3DC
0.0
102.7
101.3
46.7
72.5
148.3
191.7
30.6
61.4
37.5
47.6
wPt7785-7AS
tPt2203-5B
BARC356-3A
wPt7299-7A
wPt1726cl2-1BSat
wPt944cl4-1BL
wPt8206-3B
wPt4292cl2-3D
wPt5385cl2-1B
wPt1261-5B
wPt2013cl2-3D
3.0
2.6
3.3
2.6
3.5
2.7
2.5
2.7
2.8
5.4
2.8
0.0-2.0
88.8-114.7
95.3 - 104.9
46.5 - 47.1
63.1 - 78.4
135.2 - 154.5
173.3 - 207.9
29.5 - 47.8
48 - 73
20.6 - 43.8
45.9 - 48.0
8.5
7.3
9.3
6.9
9.9
8.8
16.2
7.2
7.9
40.9
4.1
1.5
-1.4
-1.5
-1.3
1.6
0.1
1.1
0.8
0.7
0.7
-0.2
NSA
Tubbs
Tubbs
Tubbs
NSA
NSA
Tubbs
Tubbs
NSA
NSA
Tubbs
2009 4AL
1B
2009 1B
57.1
102.3
102.3
wPt4620cl3-4A7A
wPt6690-1BL
wPt6690-1BL
2.9
2.7
3.3
43.0-59.1
101.8-120.5
101.8-109.6
13.3
6.7
8.3
0.5
-0.3
-0.3
NSA
Tubbs
Tubbs
KW*
PPO
BFY
FY
GPCH+S
GPCS
FPCS
Chrom. = Chromosome; 2-LOD Sup. L. = 2-LOD support limit; Add. E. = Additive effect; Par. Con. = Parental contributor
* = Included 14 lines that had HI > 50
** = Excluded 14 lines that had HI > 50
153
Table 7.1: QTLs detected by composite interval mapping in the T x N population (Continued)
Trait
Year Chrom. QTL peak Closest marker to peak LOD 2-LOD Sup. L. R² (%) Add. E. Par. con.
TUPP
2009 3AL
6.4
wPt2698cl2-3AL
3.0
0 - 17.3
9.3
-1.0
Tubbs
DS
7AC
2009 5AS
14.8
2.2
BARC127-7AS
wPt4131-5AS
3.3
2.7
2.1 - 25
0-6
15.1
8.3
-1.3
0.1
Tubbs
NSA
64.9
30.8
2.2
14.4
212.7
37.7
103.8
2.1
wPt4835cl2-7AS
wPt8418-7AS
wPt4131-5AS
GWM304-5AS
wPt280cl15-3B
wPt5064-3B
wPt6105cl2-5B
wPt1928-7AS
2.6
2.6
4.3
5.1
2.6
2.5
2.7
3.0
62.9 - 66.7
30.3 - 36.8
0 - 8.3
13.1 - 19.4
211.0 - 213.1
19.4 - 51.7
93.3 - 106.3
0 - 12.4
9.6
6.9
14.5
14.8
7.4
16.7
10.4
11.3
0.2
-1.5
1.9
-2.0
-0.3
-2.5
-2.0
-30.8
Tubbs
NSA
Tubbs
NSA
NSA
Tubbs
Tubbs
Tubbs
WUAX
2009 7AS
WSRC-W 2009 7AS
SSRC-W 2009 5AS
5AS
CH
2009 3B
TAA
2009 3B
5BS
TPC
2009 7AC
154
155
7.2 Polyphenol oxidase activity
One QTL (wPt944cl4-1BL) associated with polyphenol oxidase (PPO) activity was
detected on chromosome 1B with a LOD score of 2.7 (Table 7.1). This QTL explained
8.8% of the phenotypic variance in the trait with a positive additive effect indicating
that the increase in PPO was due to the NSA allele at this QTL.
It has been suggested that PPO gene(s) could be located on group 2 chromosomes,
particularly 2D as a major gene (Anderson and Morris, 2001; Anderson et al., 2006;
Jimenez and Dubcovsky, 1999). This hypothesis has been supported by many studies.
Demeke et al. (2001) identified QTLs significantly associated with wheat PPO on
chromosome 2D in the M6 x Opata-85 RILs, chromosomes 2A, 2B, 3D, and 6B in the
NY18 x CC RILs, and chromosome 3BS in ND2603 x Butte-86 RILs. He et al. (2007)
detected two QTLs on chromosome 2DL in a doubled haploid population obtained
from the Zhongyou 9507 x CA9632 RILs, and a set of nullisomic–tetrasomic lines and
ditelosomic line 2DS of Chinese Spring. Raman et al. (2005) identified a QTL for
PPO activity on chromosome 2AL in a doubled haploid population derived from
Chara x WW2449. However, no PPO QTL on chromosome 1B has previously been
reported. The wPt944cl4-1BL identified in this study could be a minor QTL for PPO.
156
7.3 Milling performance test
Break flour yield (BFY)
Two QTLs (wPt8206-3B and wPt4292cl2-3D) associated with BFY for the 2009 soft
RILs were identified on chromosomes 3B and 3D with LOD scores of 2.5 and 2.7,
respectively (Table 7.1). These QTLs explained up to 16.2% of the phenotypic
variance in the trait with positive additive effects indicating that the increase in BFY
due to the Tubbs alleles at these QTLs. No QTL associated with BFY for the 2010 soft
RILs was identified.
As expected, Tubbs possessed the alleles that increased BFY. The QTL for BFY on
chromosome 3B has been also reported in another study. Carter et al. (2011) found
BFY QTL on chromosomes 3B, 4D, 6B, and 6D in the Louise (soft) x Penawawa
(soft) cross. Other research groups have reported BFY QTL on other chromosomes.
Breseghello et al. (2005) reported QTL related to BFY on chromosome 1AS and L in
doubled-haploid lines generated from an AC Reed (soft) x Grandin (hard) cross.
Breseghello and Sorrells (2006) found QTL for BFY on chromosome 5B in a study of
95 cultivars of soft winter wheat. Wang et al. (2012) reported six QTLs for BFY on
chromosomes 1BS, 4BS, 5BS,7BL, 2DS, and 4DS in a soft x extra-soft cross.
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Flour yield (FY)
One QTL (wPt5385cl2-1B) associated with FY for the 2009 soft RILs were identified
on chromosomes 1B with a LOD score of 2.8 (Table 7.1). This QTL explained 7.9%
of the phenotypic variance in the trait with a positive additive effect indicating that the
increase in FY due to the NSA allele at this QTL.
Our result was supported by Smith et al.(2011) who found two FY QTLs on
chromosome 1B in the 25R26 x Foster cross. However, other studies reported QTL at
different locations. Campbell et al. (2001) identified QTLs related to FY on
chromosomes 3S, 5BS, and 5DS in the NY6432-18 (soft) x Clark’s Cream (hard)
RILs. Kunert et al. (2007) reported FY QTLs on chromosomes 2A, 6B, 7A, and 7B in
the Batis x Syn022 RILs and chromosome 5D in the Zentos x Syn086 RILs. Nelson et
al. (2006) found QTLs associated with FY on chromosomes 4A, 4D, and 5D in RILs
derived from the W7985 x Opata-85 cross. Carter et al.(2011) found FY QTLs on
chromosomes 3B, 4D, and 6D in the Louise x Penawawa cross.
7.4 Grain and flour protein contents
Grain protein content (GPC)
Two QTLs (wPt1261-5B and wPt2013cl2-3D) associated with GPCH+S for 2009 RILs
were identified. wPt1261-5B was found on chromosome 5BS with a LOD score of 5.4
158
(Table 7.1). This QTL explained 40.9% of the phenotypic variance in the trait with a
positive additive effect indicating that the increase in GPCH+S was due to the NSA
allele at this QTL. wPt2013cl2-3D was detected on chromosome 3DC with a LOD
score of 2.8. This QTL explained 4.1% of the phenotypic variance in the trait with a
negative additive effect indicating that the decrease in GPCH+S was due to the Tubbs
allele at this QTL.
When hard progeny were excluded from the 2009 RIL sample set, two different QTLs
for GPCS were identified. wPt4620cl3-4A7A was found on chromosome 4AL with a
LOD score of 2.9 (Table 7.1). This QTL explained 13.3% of the phenotypic variance
in the trait with a positive additive effect indicating that the increase in GPC was due
to the NSA allele at this QTL. wPt6690-1BL was detected on chromosome 1B with a
LOD score of 2.7. This QTL explained 6.7% of the phenotypic variance in the trait
with a negative additive effect indicating that the decrease in GPCS due to the Tubbs
allele at this QTL. No QTL associated with GPCS for the 2010 RILs was identified.
Flour protein content (FPC)
One QTL (wPt6690-1BL) associated with FPCS for 2009 RILs was detected on
chromosome 1BL with a LOD score of 3.3 (Table 7.1). This QTL explained 8.3% of
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the phenotypic variance in the trait with a negative additive effect indicating that the
decrease in FPCS due to the Tubbs allele at this QTL.
As expected, NSA possessed the alleles that increased protein content, while Tubbs
possessed the alleles that decreased protein content. All of GPCH+S and GPCS QTLs
identified in this study (on chromosomes 1B, 3DC, 4AL, and 5BS) appeared similar to
those reported previously. Groos et al. (2003) reported QTLs related to GPC on
chromosomes 4A and 5B in the Renan x Récital cross. Bordes et al. (2011) identified
two QTLs for GPC on chromosome 5B and 3D. Kunert et al. (2007) found GPC QTLs
on chromosomes 3A, 4A, 5D, 7B, and 7D in the Batis x Syn022 RILs and
chromosome 4B in the Zentos x Syn086 RILs.
There was a co-located QTL (wPt6690-1BL) on chromosome 1BL associated with
both GPCS and FPCS. Other researchers who determined QTLs for both GPC and FPC
also reported co-located QTL for GPC and FPC. However, those QTLs were not on
chromosome 1B. Huang et al. (2006) found two QTLs associated with both GPC and
FPC on chromosomes 4D and 7B in doubled haploid population obtained from a cross
between ACKarma and 87E03-S2B1. Zhao et al. (2010) reported three QTLs related
with both GPC and FPC on chromosomes 3A, 5D, and 6D in doubled haploid lines
derived from Huapei 3 x Yumai 57. McCartney et al. (2006) detected two QTLs
associated with both GPC and FPC on chromosomes 2B and 4D in the RL4452 x AC
160
Domain cross. Even though none of those researchers reported QTLs on chromosome
1B, several studies identified QTLs related to either GPC or FPC on chromosome 1B
(Bordes et al., 2011; McCartney et al., 2006; Zanetti et al., 2001).
7.5 Polymeric proteins
Two QTLs (wPt2698cl2-3AL and BARC127-7AS) associated with total unextractable
polymeric protein (TUPP) for 2009 RILs were identified on chromosomes 3AL and
7AS with LOD scores of 3.0 and 3.3, respectively (Table 7.1). These QTLs explained
up to 15.1% of the phenotypic variance in the trait with negative additive effects
indicating that the decrease in TUPP was due to the Tubbs alleles at these QTLs.
However, no QTL associated with large unextractable polymeric protein for 2009
RILs was detected.
UPP are strongly associated with HMW-GS. Several studies have reported QTLs
related to UPP at genes that encode HMW-GS on group 1 chromosomes. Mann et al.
(2009) identified QTLs for UPP at Glu-B1 and Glu-D1associated with HMW-GS,
Glu-B3 associated with LMW-GS, a non-glutenin associated QTL on chromosome 2B
in the Kukri x Janz RILs. Tsilo et al. (2011) reported QTLs associated with UPP on
1A, 1B, 4D, 5D, and 6D in the MN98550 x MN99394 RILs. This is the first study that
detected TUPP QTLs on chromosomes 3A and 7A.
161
7.6 Damaged starch
One QTL (wPt4131-5AS) associated with damaged starch (DS) was identified on
chromosome 5AS with a LOD score of 2.7 (Table 7.1). This QTL explained 8.3% of
the phenotypic variance in the trait with a positive additive effect indicating that the
increase in DS was due to the NSA allele at this QTL.
NSA possessed the alleles that increased DS as expected. The result from this study
was supported by Campbell et al. (2001) also found major QTLs correlated with DS
on chromosomes 4D, 5A, 5B, and 5D in NY6432-18 x Clark’s Cream RILs.
7.7 Arabinoxylans
One QTL (wPt4835cl2-7AS) associated with water-unextractable arabinoxylan
(WUAX) was identified on chromosome 7AS with a LOD score of 2.6 (Table 7.1).
This QTL explained 9.6% of the phenotypic variance in the trait with a positive
additive effect indicating that the increase in WUAX was due to the Tubbs allele at
this QTL. However, no QTL associated with total arabinoxylan and water-extractable
arabinoxylan was detected.
There is no information reported in the literature about QTL for WUAX. However,
several studies reported QTLs for TOAX and WEAX. Nguyen et al. (2011) found
162
QTLs for TOAX on chromosomes 1A, 2A, 3D, 4D, 6B, and 7A in the Berkut x
Krichauff RILs. Groos et al. (2004) identified QTLs for WE-AX on chromosomes 1B
and 7A in the Recital x Renan RILs. Martinant et al. (1998) also found QTL
associated with WE-AX on chromosome 1B in the Synthetic x Opata RILs and the
Courtot x Chinese Spring RILs. Charmet et al. (2009) reported QTLs related to
WEAX on chromosomes 1B and 6B in the Valoris x Isengrain RILs and RE0006 x
CF0007 RILs.
7.8 Solvent retention capacity
One QTL (wPt8418-7AS) associated with water solvent retention capacity for whole
wheat flour (WSRC-W) was found on chromosome 7AS with a LOD score of 2.6
(Table 7.1). This QTL explained 6.9% of the phenotypic variance in the trait with a
negative additive effect indicating that the decrease in WSRC-W due to the NSA allele
at this QTL.
Two QTLs (wPt4131-5AS and GWM304-5AS) associated with sucrose solvent
retention capacity for whole wheat flour (SSRC-W) were detected on chromosome
5AS with LOD scores of 4.3 and 5.1, respectively. wPt4131-5AS explained 14.5% of
the phenotypic variance in the trait with a positive additive effect indicating that the
increase in SSRC-W was due to the Tubbs allele at this QTL. GWM304-5AS
explained 14.8% of the phenotypic variance in the trait with a negative additive effect
163
indicating that the decrease in SSRC-W was due to the NSA allele at this QTL. No
QTL associated with WSRC and SSRC for refined flour were identified.
Prior to this study, there have been no reports of QTL related to WSRC or SSRC for
whole wheat flour. However, several studies have been reported QTLs associated with
WSRC or SSRC for refined flour. Carter et al.(2011) identified QTL for WSRC on
chromosomes 3B and 4D and for SSRC on chromosomes 1A, 3B, and 4D in the
Louise x Penawawa cross. Smith et al. (2011) reported QTL significantly related to
WSRC on chromosomes 1B, 2B, 2D, 3B and 7D, and SSRC on chromosomes 1B, 2B,
and 3B in the 25R26 x Foster RILs. Interestingly, the locus wPt4131-5AS, associated
with SSRC-W was also significantly associated with DS. This could explain part of
the correlation between those traits.
7.9 Sugar-snap cookie
One QTL (wPt280cl15-3B) associated with cookie height (CH) was found on
chromosome 3B with a LOD score of 2.6 (Table 7.1). This QTL explained 7.4% of the
phenotypic variance in the trait with a negative additive effect indicating that the
decrease in CH was due to the NSA allele at this QTL. However, no QTL associated
with cookie diameter (CD) was detected in this study.
164
This study is the first to report QTL for CH. Several groups identified QTLs for CD.
Campbell et al. (2001) reported QTLs for CD on chromosomes 5BS and 5DS in a
population of RILs derived from the NY6432-18 x Clark’s Cream cross. Breseghello
et al. (2005) found QTL significantly associated with CD on chromosome 6B in the
AC Reed (soft) x Grandin (hard) RILs. Nelson et al. (2006) identified QTLs related
for CD on chromosome 5D in RILs generated from the W7985 and Opata 85 cross.
Carter et al. (2011) reported QTLs related to CD on chromosomes 3B, 4A, and 4D in
the Louise x Penawawa cross.
7.10 Total antioxidant activity and total phenolic compound
In this study, two QTLs (wPt5064-3B and wPt6105cl2-5B) associated with total
antioxidant activity (TAA) were detected on chromosomes 3B and 5BS with LOD
scores of 2.5 and 2.7, respectively (Table 7.1). These QTLs explained up to 16.7% of
the phenotypic variance in the trait with negative additive effects indicating that the
decrease in TAA was due to the Tubbs allele at these QTLs.
One QTL (wPt1928-7AS) associated with total phenolic content (TPC) was found on
chromosome 7AC with a LOD score of 3.0 (Table 7.1). This QTL explained 11.3% of
the phenotypic variance in the trait with a negative additive effect indicating that the
decrease in TPC was due to the Tubbs allele at this QTL.
165
To our knowledge, no one has previously performed QTL mapping for TAA and TPC.
There is also not much information on how genetics, environment, and interactions
between genetics and environment affect wheat TAA and TPC. Moore et al. (2006)
determined antioxidant properties and total phenolics of bran samples from 20 wheat
varieties grown in two locations. Results showed that both genetic and environment
influenced antioxidant properties and total phenolics. Mpofu et al. (2006) measured
TPC and antioxidant activities of six red- and white-grained hard spring wheat grown
in four locations. They reported that TPC and antioxidant activities were largely
influenced by environmental effects. Both research groups agree that the effect of
interaction between genetics and environment was small.
166
Chapter 8: Conclusions
8.1 Effect of carbonate on co-extraction of arabinoxylans with
glutenin macropolymer (Chapter 4)
 Under the extraction conditions used in this study, AX material was coextracted with GMP. In addition, the AX concentration was higher in GMP
extracted under alkaline conditions compared to extractions done in water.
 There was a distinct difference between hard and soft wheats in the extent of
the increase in GMP-AX concentration, with soft wheats having a greater
amount of increase. Within the soft wheats, the largest increases in GMP-AX
concentration were in a subgroup of the SWW wheats (Figure 4.2).
 At this juncture, it is not clear what, if any, practical implications these
observations might have.
8.2 Optimization of water addition to noodle doughs (Chapter 5)
 For the sieving test the water addition had a significant impact on the amount
of dough retained and passing the sieve. A general decline in the amount of
dough passing the sieve was observed as water addition increased for both
salted and alkaline noodle doughs.
167
 MaxF of LSF also decreased as water addition increased for both salted and
alkaline noodle doughs.
 For both salted and alkaline soft-wheat flour derived noodle doughs, the
optimum water additions predicted by the Mixograph, sieving, and LSF
methods were equivalent or close. For both salted and alkaline hard-wheat
flour derived noodle doughs, the optimum water additions predicted by the
sieve and LSF methods were generally greater than those predicted by the
Mixograph, particularly for the WA7954 dough.
 The results of this study showed that the LSF method in its current form is not
yet optimized for determining optimum water addition in either both salted or
alkaline noodle doughs. RT was the most promising parameter. There was
evidence that RT was of some value in assessing optimum water addition for
the soft-wheat noodle doughs. As many noodle types are made with hardwheat flours, this suggests that the LSF method in its current form is not
adequate for all noodle types.
 Further work with a wider range of water additions, with an expanded number
of samples is required.
 In this study the optimum water additions predicted by the Mixograph, sieving
test, and LSF method for both salted and alkaline hard- and soft-wheat flour
derived doughs were higher than those generally used in noodle industry. This
168
was largely due to the differences in environments during noodle preparation
in which the commercial environments were well better controlled.
169
8.3 Determination of wheat quality (Chapter 6)
 HI was significantly correlated with many important traits (GMC (negative),
BFY (negative), DS (positive), WSRC (positive) and SSRC (positive) for both
whole wheat and refined flours, CD (negative) and CH (positive) for refined
flour only, and TAA (positive), and TPC (negative) for whole-wheat only)
(Table 6.2). The correlations between HI and GMC, BFY, DS, WSRC, SSRC,
CD, and CH support the “act like one trait” concept of Souza et al. (2009).
That is, these parameters are simply different expressions of a single
underlying trait all related to the absorption capacity of the flour.
 Among 88 RILs tested for PPO activity, one line (ID: 189; soft wheat) had
surprising low PPO activity (0.09). The low PPO and hence by inference low
darkening rate in noodle doughs, makes this line a good candidate as parent for
breeding wheat for udon noodle type applications by supplying a genetic
source of very low PPO.
 GPC for both harvest years was positively correlated with HIS and FPC (Table
6.2). However, FPC for the 2009 test RILs was not correlated with HIS.
ANOVA analysis revealed that there was no significant difference in GPC
between hard and soft progeny in the 2009 RILs (p > 0.05).
170
 ANOVA analysis showed no significant difference (p > 0.05) in the amount of
LUPP between hard and soft progeny in the 2009 RILs. However, the amount
of TUPP was significantly greater (p ≤ 0.05) in hard progeny than soft
progeny.
 Interestingly, 90% of the 2009 soft progeny had lower DS than Tubbs
indicating generally better soft wheat quality than the soft parent. As expected,
DS was negatively correlated with BFY (Table 6.2).
 WSRC-W and SSRC-W values were greater than WSRC-R and SSRC-R
values due to high content of non-starchy polysaccharides in whole wheat
flour.
 At least 75% of the 2009 soft progeny had lower WSRC-W and WSRC-R
values than Tubbs, again suggesting better overall soft wheat quality than the
soft parent. Both WSRC-W and WSRC-R were positively correlated with DS
but negatively correlated with BFY (Table 6.2). WSRC-R was also
significantly correlated with TOAX, WEAX, and WUAX further supporting
Souza’s “act like on trait” concept.
171
 At least 33% of the 2009 soft progeny had lower SSRC-W and SSRC-R values
than Tubbs. Both SSRC-W and SSRC-R were negatively correlated with BFY
and FY but positively correlated TOAX, WEAX, and WUAX (Table 6.2).
 SSRC-R is known to be the best predictor of cookie quality. However, contrary
results were observed in this study. Only 33% of the 2009 soft RILs had lower
SSRC-R values than Tubbs but 96% of the 2009 soft RILs had larger CD than
Tubbs. This finding is supported by the evidence that germplasm from the
OSU soft wheat breeding program often has higher CD values than the SSRCR would predict in independent testing by industrial partners.
 No correlation between TAA and TPC was observed in this study. It is because
most of phenolic compounds in wheat are in a bound form which does not
exhibit antioxidant activity. ANOVA analysis revealed that the differences in
TAA and TPC between the 2009 red and white progeny were not significantly
different (p > 0.05).
 Crossing between Tubbs, at the low end of the range of acceptable quality for
soft wheat, and NSA, a hard red winter wheat, appeared to result in new
genetic combinations that unexpectedly created superior soft-wheat quality.
The cross also produced progeny with potentially increased nutritional values.
172
8.4 Quantitative trait locus analysis (Chapter 7)
 A total of 28 significant QTLs associated with HI, KW, PPO, BFY, FY, GPC,
FPC, TUPP, DS, WUAX, WSRC-W, SSRC-W, CH, TAA, and TPC were
identified in the T x N population from two harvest years.
 Five QTLs related to HIS were detected in both 2009 and 2010 soft progeny.
For the 2009 soft RILs, one QTL was identified on chromosome 6AC. Two
QTLs on chromosome 7DL were found in the 2010 soft RILs including those
14 lines that were soft (HI ≤ 50) in the 2009 harvest year but had HI > 50 in the
2010 harvest year. However, when those 14 lines were excluded, two new
QTLs were discovered on chromosomes 7AC and 5BS. Three QTLs related to
KW were found on chromosomes 3AS, 7AC, and 1B in the 2010 RILs.
 One QTL for PPO activity was detected on chromosome 1B in the 88 RILs
randomly selected from the T x N population.
 Two QTLs associated with BFY were detected on chromosomes 3B and 3D,
while one QTL related to FY was detected on chromosome 1B in the 2009 soft
RILs.
 A total of five QTLs related to GPC or FPC were found in the 2009 RILs. Two
QTLs for GPCH+S were identified on chromosomes 5BS and 3DC in the soft
and hard progeny. However, when the hard progeny were excluded, two new
173
QTLs associated with GPCS were detected on chromosomes 4AL and 1B. QTL
analysis identified one QTL related to FPCS on chromosome 1B in the 2009
soft RILs. As expected, the QTL for FPCS was co-located with that for GPCS.
 Two minor QTL associated with TUPP were found on chromosomes 3AL and
7AC in the 88 RILs randomly selected from the T x N population. However,
no major genes encoding HMW-GS on group 1 chromosomes were detected in
this study.
 QTL analysis detected one QTL related to DS on chromosome 5AS, another
one for WUAX on chromosome 7AS, and another one for CH on chromosome
3B in the 2009 soft progeny.
 QTL analysis identified three QTLs associated with SRC on chromosomes
7AS and 5AS in the 2009 soft progeny. One QTL related to WSRC-W was
found on chromosome 7AS. Two QTLS related to SSRC-W were detected on
chromosome 5AS. Interestingly, one of the SSRC-W QTLs was co-located
with that for DS.
 It is for the first time that QTLs for TAA and TPC are identified. Two QTLs
for TAA were on chromosomes 3B and 5BS, while one QTL for TPC was on
chromosome 7AC. These preliminary results are the first genetic approach to
the control of TAA and TPC in wheat.
174
 The QTL mapping results in this study could be utilized in future pre-selection
in progeny derived from Tubbs or NSA to combine desired economic traits on
the base of a high probability of achieving quality appropriate to the soft-white
market-class.
175
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