i
OPTIMIZATION OF SUGAR PRODUCTION FROM CASSAVA
RESIDUE USING RESPONSE SURFACE METHODOLOGY
MOHD SHAHRIL BIN MUSTAFA @ SULAIMAN
Universiti Malaysia Pahang
ii
UNIVERSITI MALAYSIA PAHANG
BORANG PENGESAHAN STATUS TESIS
JUDUL
: OPTIMIZATION OF SUGAR PRODUCTION FROM CASSAVA
RESIDUE USING RESPONSE SURFACE METHODOLOGY
SESI PENGAJIAN
2009/2010
:
MOHD SHAHRIL BIN MUSTAFA @ SULAIMAN
Saya
(HURUF BESAR)
mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah)* ini disimpan di Perpustakaan Universiti
Malaysia Pahang dengan syarat-syarat kegunaan seperti berikut :
1.
2.
3.
4.
Tesis adalah hakmilik Universiti Malaysia Pahang
Perpustakaan Universiti Malaysia Pahang dibenarkan membuat salinan untuk tujuan
pengajian sahaja.
Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi
pengajian tinggi.
**Sila tandakan ( √ )
SULIT
(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia seperti yang termaktub di dalam
AKTA RAHSIA RASMI 1972)
TERHAD
√
(Mengandungi maklumat TERHAD yang telah ditentukan
oleh organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh
(TANDATANGAN PENULIS)
Alamat Tetap:
No 1366 Blok B3,
Felda Adela,
81900 Kota Tinggi, Johor
Tarikh : 30 April 2010
CATATAN :
*
**

(TANDATANGAN PENYELIA)
PUAN ROHAIDA CHE MAN
Nama Penyelia
Tarikh: 30 April 2010
Potong yang tidak berkenaan.
Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak
berkuasa/organisasiberkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu
dikelaskan sebagai SULIT atau TERHAD.
Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara
penyelidikan, atau disertasi bagi pengajian secara kerja kursus dan penyelidikan,
atau Lapuran Projek Sarjana Muda (PSM).
iii
“I hereby declare that I have read this thesis and in my opinion this thesis
is sufficient in terms of scope and quality for the award of the degree of Bachelor of
Chemical Engineering (Biotechnology)”
Signature
: ……………………………..
Supervisor
: Puan Rohaida Binti Che Man
Date
: 30 April 2010
iv
OPTMIZATION OF SUGAR PRODUCTION FROM CASSAVA RESIDUE
USING RESPONSE SURFACE METHODOLOGY
MOHD SHAHRIL BIN MUSTAFA @ SULAIMAN
A thesis submitted in fulfillment of the requirements for the award of the degree
of Bachelor of Chemical Engineering (Biotechnology)
Faculty of Chemical Engineering and Natural Resource
Universiti Malaysia Pahang
MAY 2010
ii
I declare that this thesis entitled “Optimization of Sugar Production From Cassava
Residue using Response Surface Methodology” is the result of my own research
except as cited in references. The thesis has not been accepted for any degree and
not concurrently submitted in candidature of any other degree
Signature
: ………………………………………...
Supervisor
: Mohd Shahril Bin Mustafa @ Sulaiman
Date
: 30 April 2010
iii
This thesis is dedicated to all my love ones, especially to Mak &
Abah. Thank You for the endless love & support.
iv
ACKNOWLEDGEMENT
Alhamdulillah, to Allah S.W.T I thank the most on His utmost love and
blessings for giving me the guidance in accomplishing this study. The most courteous
thank againt Him who made all thing achievable.
First and foremost, I would like to acknowledge and extend my heartfelt
gratitude to my supervisor, Puan Rohaida Bt Che man, for her guidance, criticisms and
inspiration she extended. Thank you so much for her vital encouragement and support.
I wish to express my deepest thankfulness to Encik Zaki who had been helping,
supporting and motivating me throughout this study. Thank you to the faculty of
Chemical & Natural Resources Engineering, UMP for its permission to carry out this
study.
Exclusive thanks from me to the people that I love especially Abah, Mak,
Shafiq, Abanglong, Ahmad Termizi, Elyanes Maslisa, Raja Norazean, Hanifan Abd
Jalil, Kamarul Izhan and to all who made this project done. Thank you so much for the
everlasting love, upright companionship, honourable supports and exquisite inspirations
through out this period.
v
ABSTRACT
Cassava residue, starch processing waste from cassava starch plant, was used as a
raw material in sugar production. This is study aims to optimize the production of sugar
from cassava residue using response surface methodology. The effect of parameters
such as incubation time and enzyme concentration is used to study the concentration of
glucose produced. Three different enzymes are used to produce glucose namely
cellulase, glucoamylase and α-amylase. Cellulase was the best enzyme that yielded the
most glucose. The optimization of the production of sugar is carried out by using
response surface methodology (RSM) based on the central composite design
(CCD).The optimal set parameters for enzyme concentration are 1.32 g/mL and 4.47
hours of incubation time. Glucose concentration of 1.059 g/L was achieved by using
these optimal parameters.
vi
ABSTRAK
Sisa ubi kayu adalah sisa pemprosesan kanji daripada pokok ubi kayu, digunakan
sebagai bahan mentah dalam penghasilan gula.. Tujuan kajian ini dilakukan adalah
untuk mengoptimumkan penghasilan gula daripada ubi kayu menggunakan kaedah
gerak balas permukaan (RSM). Pengaruh parameter seperti masa inkubasi dan
kepekatan enzim digunakan untuk mengkaji kepekatan glukosa yang dihasilkan. Tiga
jenis enzim digunakan dalam menghasilkan glukosa iaitu cellulase, glucoamylase and
α-amylase. Enzim terbaik yang menghasilkan gula yang paling tinggi adalah cellulase.
Penghasilan gula dioptimumkan menggunakan kaedah gerak balas permukaaan
berdasarkan reka bentuk komposit pusat (CCD). Parameter yang optimum adalah
dengan menggunakan 1.32 g/mL kepekatan enzim pada 4.47 jam masa inkubasi.
Kepekatan glukosa yang dihasilkan adalah 1.059 g/mL dengan menggunakan parameter
yang optimum ini.
vii
TABLE OF CONTENT
CHAPTER
TITLE
DECLARATION
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
ABSTRAK
TABLE OF CONTENT
LIST OF TABLES
LIST OF FIGURES
LIST OF SYMBOLS/ABBREVIATIONS
LIST OF APPENDICES
1
ii
iii
iv
v
vi
viii
x
xi
xii
xiii
INTRODUCTION
1.1
Introduction
1
1.2
Problem Statement
2
1.3 Objective of Research
1.4
2
PAGE
2
Scopes of Research
LITERATURE REVIEW
2.1
Cassava Residue
4
2.2
Structure of Cassava Residue Biomass
5
2.2.1
Cellulose
5
2.2.2
Hemicelluloses
6
2.2.3
Starch
7
2.3 Enzymatic Hydrolysis
2.3.1
Overview of Enzymatic Hydrolysis
8
9
2.4 Optimization of time and enzyme concentration on
Production of sugar by Using Response Surface
Methodology (RSM)
2.5 Production of Sugar
2.5.1
10
13
Production of xylose from Palm Oil Empty Fruit
Bunch (POEFB) using enzymatic hydrolysis process
13
viii
2.5.2
Production of sugar from from pineapple cannery
waste using continuous fermentation
2.5.3
Production of sugar from from pineapple cannery
waste using continuous fermentation
3
14
METHODOLOGY
3.1
Overview of Research Methodology
15
Enzymatic hydrolysis
16
3.1
Screening of Enzymes
16
3.2
Effect of Parameters
16
3.3
Effect of Incubation Time
17
3.4
Effect of Enzyme Concentration
17
3.5
Concentration of Glucose
17
3.7.1 Preparation of DNS reagent
17
3.7.2 Concentration of Glucose Analyzed by DNS
17
Response Surface Methodology (RSM)
18
3.8
4
13
RESULT AND DISCUSSION
4.1
Screening of Enzymes
20
4.2
Effect of Parameters on Glucose Concentration
21
4.2.1 Effect of Incubation Time
21
4.2.2 Effect of Enzyme Concentration
23
4.3
Determination of the Optimum of Parameters on Sugar
Production Using Response Surface Methodology
(RSM)
4.4
Optimization of sugar production using Response Surface
Methodology (RSM)
5
25
29
CONCLUSION ANR RECOMMENDATIONS
5.1
Conclusion
30
5.2
Recommendations
31
ix
REFERENCES
APPENDIX A - B
32
39-44
x
LIST OF TABLES
TABLE NO.
TITLE
PAGE
1.1
Chemical composition in cassava residue
5
1.2
Low level and high level of cultural condition
25
1.3
Experiments designed by Design Expert Software
26
1.4
ANOVA for response surface quadratic model for the
production sugar
1.5
27
Summaries of the optimized cultural conditions for sugar
production
29
xi
LIST OF FIGURES
FIGURE NO.
TITLE
1.1
Cassava Starch Plant
1.2
Basics compound of cellulose is cellubiose, disaccharides
PAGE
1
of two β-1-4 bonded glucoses
6
1.3
Structure of xylan and enzymes cleavage sites
7
1.4
Structure of starch
7
1.5
Schematic for basic process of enzymatic hydrolysis
9
1.6
Research design for the production of sugar
15
1.7
Screening of different enzyme
21
1.8
Effects of incubation time
22
1.9
Effect of enzyme concentration
23
1.10
Response surface plot of sugar production: Incubation time
vs. enzyme concentration.
28
xii
LIST OF SYMBOL/ABBREVIATIONS
ANNOVA
-
Analysis if variance
CCD
-
Central composite design
g
-
Gram
mL
-
Milliliter
DNS
-
Dinitrosaliccyclic Acid
HPLC
-
High performance liquid chromatography
Hr
-
Hour
L
-
Liter
g/mL
-
Gram per milliliter
g/L
-
Gram per liter
min
-
Minute
M
-
Molar
Nm
-
Nanometer
OD
-
Optical density
OFAT
-
One factor at a time
RSM
-
Response Surface Methodology
w/v
-
Weight per volume
°C
-
Degree celcius
%
-
Percentage
xiii
LIST OF APPENDICES
APPENDIX
A1
TITLE
PAGE
Preparation mixture of 0.1 M Sodium Hydroxide (NaOH),
(2000 mL) and 0.1 M Citric Acid (1000 mL) as Buffer
35
A2
Preparation of DNS Reagent (1 L)
36
A3.1
Preparation of 1 mg/mL of Glucose Standard Solution (10 mL)
37
A3.2
Standard Curve of Glucose
37
A4
Enzyme Dilution
38
B1
ANNOVA Results for Optimization of Parameters
39
1
CHAPTER 1
INTRODUCTION
1.1
Introduction
Cassava is a shrubby, tropical, perennial plant that is not well known in the
temperate zone. For the most people, cassava is most commonly associated with
tapioca. The plant grows tall, sometimes reaching fifteen feet, with leaves varying in
shape and size. The edible parts are the tuberous root and leaves. The tuber (root) is
somewhat dark brown in color and grows up to two feet long. Ironically, any
processed raw materials will end up producing waste. Due to the mass production of
which originated from cassava starch plant, encourage a mass production of its
waste, cassava residue which also called as biomass.
Recently, utilization of biomass resources has been the subject of the various
studies. Cassava residues is one of the biomass materials, which is the by product
from the cassava starch plant industry. Cassava residue has the potential to be utilized
to produce sugar due to its containing cellulose, hemi-cellulose and starch at levels of
24.99, 6.67 and 61% (w/w), respectively. Starch components in cassava residue were
converting to reducing sugar with different kind of enzymes (Teerapatr et al., 2006).
Figure 1.1 shows the cassava starch plant.
Figure 1.1: Cassava starch plan
2
1.2
Problem Statement
Annually, there is approximately many ton of cassava residues produced from
the starch factories in Malaysia. The cassava residue left over from the production
processes are abundant and still contain a high amount of starch content. Use of
cassava residue as raw material in sugar production not only reduces waste material
created from the cassava starch industry, but also lowers the cost of sugar production
(La-aied et al., 2006). In addition by using the cassava residue as raw material to sugar
production is to prevent from the environmental issues.
Fortunately, the main component of cassava residue consists of cellulose 24.99
% (w/w), hemi-cellulose 6.67 % (w/w) and starch 61 % (w/w) (Teerapatr et al., 2006).
Therefore, it can be consumed to produce sugar which has a major field of applications
in industries and can very useful to the environment and society at large.
1.3
Objective of Research
The main objective of this research is to optimize the production of sugar from
cassava residue using response surface methodology.
1.4
Scopes of Research
In order to achieve the objective, scope of study was divided into three as
the following:
i)
To determine the best enzyme that can produce the most yield of glucose
using enzymatic hydrolysis by feeding three types of commercial enzymes
into the treated cassava residue, which are: (a) Cellulase, (b) Glucoamylase
and (c) α-amylase.
ii)
To study the effect of parameters of the best chosen from (i) such as incubation
time and enzyme concentration on glucose concentration.
3
iii)
To optimize the parameters of the enzyme chosen by using response surface
methodology (RSM) on sugar production.
4
CHAPTER 2
LITERATURE REVIEW
2.1
Cassava Residue
Cassava residue, which is the starch-processing waste from cassava starch
plant, was used as a raw material in sugar production (Cholada et al., 2004). The
cassava residue left over from these production processes are abundant and still
contain a high amount of starch content. Most cassava residue can be used as animal
feed due to its high content of protein and other nutrients which are necessary for
animal growth. In addition, cassava residue can be used to produce sugar. By using the
cassava residue as raw material in sugar production not only reduces waste material
created from the cassava starch industry, but also lowers the cost of sugar production
(Lerdluk et al., 2006).
The main component of most cassava residue containing cellulose at level
24.99%, hemicelluloses at level 6.67% and starch at level 68.34% ( Suthkamol et al.,
2006).Annually there is approximately one million of cassava residues produced from
the starch factories. The application of using these cassava residues especially for the
sugar production would be advantage for the country economy (Bongotrat et al.,
2004). Table 2.1 shows the chemical composition in cassava residue.
5
Table 2.1: The chemical composition in cassava residue
Composition
Cassava
residue
(Sample 1)
78.16
1.82
0.09
1.61
10.61
69.90
Moisture
protein
Fat
Ash
Fiber
Starch
2.2
Cassava
residue
(Sample 2)
79.50
2.03
0.20
2.38
14.35
61.84
Cassava residue
(Sample 3)
82.74
2.31
0.16
2.05
14.56
64.36
Structure of Cassava Residue Biomass
Main components of most cassava residue are cellulose, hemi-cellulose and
starch. Each of this part plays a role in protecting each other and strengthens the plant
structure (La-aied et al., 2006).
2.2.1 Cellulose
Cellulose consists of linear macromolecular chains of glucose, linked by β-1,4glucosidic bonds between the number one and number four carbon atoms of the
adjacent glucose units. Native crystalline cellulose is insoluble and occurs as fibers of
densely packed, hydrogen-bonded and anhydroglucose chains of 15 to 10, 000 glucose
units (Howard et al., 2003).
Theoretically, complete hydrolysis of cellulose into glucose require three main
enzymes namely endo-β-glucanase, exo-β-glucanase and β-glucosidase. Endo-βglucanase
randomly
hydrolyses
the
cellulose
polymers
yielding
shorter
oligosaccharides and also a few cellubiose molecules meanwhile, exo-β-glucanase acts
at the end of cellulose chains. β-glucosidase primarily hydrolyses cellubiose into
glucose. Figure 1.2 shows the basic compound of cellulose.
6
Figure 1.2: The basics compound of cellulose is cellubiose, disaccharides of two β1-4 bonded glucoses
2.2.2
Hemicelluloses
Hemicelluloses are heterogeneous polymers of pentoses (xylose, arabinose),
hexoses (mannose, glucose, galactose) and sugar acids. In hardwood hemicelluloses
mostly
contains
xylan,
whereas
softwood
hemicelluloses
contains
mostly
glucomannans. Xylan of many plant materials are heteropolysaccharides with
homopolymeric backbone chains of 1,4-linked β-D-xylopyranose units. The
monosaccharides released upon hemicellulose hydrolysis include a large fraction of
pentoses (Gunda et al., 1970).
In several plants the majority of hemicelluloses is xylan which can be
hydrolyzed into xylose. Particularly the hemicellulose of hardwood is rich in xylan.
Consequently it is possible to obtain xylan and xylose as by-products from cellulose
industry using hardwood (Gunda et al., 1970).
Figure 1.3 shows the structure of xylan and enzymes cleavage site. The
complex structure of xylan needs different enzymes to complete its hydrolysis. The
main enzyme that randomly hydrolysis the main chain of xylan to produce mixture
xylooligosaccharides named as endoxylanase and β-xylosidase will liberate xylose
from short oligosaccharides. Moreover, the sides groups presents in xylan are liberated
7
by α-L-arabinofuranosidase, α-D-glucuronidase and acetyl xylan esterase. Figure 2
shows the structure of xylan.
Figure 1.3: Structure of xylan and enzymes cleavage sites
2.2.3
Starch
Starch is a polymer composed of glucose units primarily linked by a (1-4)
glucosidic bonds, with some additional a (1-6) linkages. Figure 1.4 shows the structure
of starch.
Figure 1.4: The structure of starch
It consists of a mixture of two types of polymers: amylose and amylopectin.
The mass fraction of amylose normally lies around 20-30 % for amylose. Starch
varieties with deviating compositions also exist (nearly 100 % pure amylopectin potato
8
and maize starch). Amylopectin is a branched polymer. Linear chains a (1-4)-linked
D-glucose residues are connected by (1-6)-D-glucosidic linkages. Amylose is a much
more linear polymer since the frequency of a (1-6) linkages (0.2 - 0.7 %) is much
smaller than in amylopectin (4 - 5 %) (Richard et al., 2004).
The enzymes that break down or hydrolyze starch into the constituent sugars
are known as amylases. Alpha-amylases are found in plants and in animals. Human
saliva is rich in amylase, and the pancreas also secretes the enzyme. Individuals from
populations with a high-starch diet tend to have more amylase genes than those with
low-starch diets; chimpanzees have very few amylase genes. It is possible that turning
to a high-starch diet was a significant event in human evolution (Steve, 2004).
Beta-amylase cuts starch in maltose units. This process is important for the
digestion of starch and also used in brewing, where the amylase from the skin of the
seed grains is responsible for converting starch to maltose.
2.3
Enzymatic Hydrolysis
There are two main approaches to convert cellulose, hemicelluloses and starch
into simple sugars such as glucose and xylose are acid hydrolysis and enzymatic
hydrolysis (Teerapatr et.al., 2004). Acid hydrolysis is relatively inexpensive and
simple but there are several major drawbacks associated with this process including:
degradation of sugars due to the high temperature, formation of fermentation
inhibitors, such as furfural, and expensive construction materials in equipment to
handle the acids. The enzymatic hydrolysis of cellulose, hemicelluloses and starch
can be more attractive because of its specificity and absence of competitive
degradation, which results in higher yields of sugars. However, enzyme costs can be
prohibitively high and it is important to reduce the costs of enzyme production
and/or to reduce the amount of enzyme used. Figure 1.5 shows schematic for basic
process of enzymatic hydrolysis.
9
Substrate
Glucose
Hydrolysis
Cellulase
Hemicelluloses
Starch
Different Enzymes
Mixture
α-amylase,
Glucoamylase,
Cellulase
Figure 1.5: Schematic for basic process of enzymatic hydrolysis
2.3.1
Overview Process of Enzymatic Hydrolysis
There is a process that simultaneously hydrolyzes cellulose, hemicelluloses and
starch to sugars by addition of enzymes. Cellulose, hemicelluloses and starch is
degraded by different enzymes that are able to hydrolyse to the sugar glucose. The
cellulose molecules are composed of long chains of sugar molecules. One major
process to Cellulose hydrolysis (cellulolysis) is an enzymatic reaction. In the
hydrolysis process, these chains are broken down to free the sugar, before it is
fermented for alcohol production.
Hemicelluloses are xylan which can be hydrolyzed into xylose. The complex
structure of xylan needs different enzymes to complete its hydrolysis. The main
enzyme that randomly hydrolysis the main chain of xylan to produce mixture of
xylooligosaccharides named as endoxylanase and β-xylosidase will liberate xylose
from short oligosaccharides. Moreover, the sides groups presents in xylan are liberated
by α-L-arabinofuranosidase, α-D-glucuronidase and acetyl xylan esterase (Badal,
2003).
10
2.4
Optimization of Time and Enzyme Concentration on Production of Sugar
by Using Response Surface Methodology (RSM)
During the preliminary investigations into this research it was realized that the
classical, “one-time-a-time” factorial design experiments, where a single factor is
varied while others are kept constant, are often expensive and time consuming and do
not take into account the possible interaction of various independent factors that would
skew the results. For these reasons, statistical methods have been developed to reduce
the cost and duration of experiments that also allow for the observation of any
interacting factors in the final process response. One such highly successful method is
“Response Surface Methodology” (RSM), which is defined as a statistical method that
uses quantitative data from appropriate experimental designs to determine and
simultaneously solve multivariate equations that specify the optimum product for a
specified set of factors through mathematical models. It involves four important steps:
(1) identification of critical factors for the product or process, (2) determination of the
range of factors levels, (3) selection of specific test samples by the experimental
design, (4) analysis of the data by RSM and data interpretation (Mutanda et al., 2008).
Basically, Response Surface Methodology (RSM) is a systematic approach that
can be obtained by using an inverse process of first, specifying the criteria and then
computing the ‘best’ design according to a formulation. Response Surface
Methodology (RSM) is a collection of statistical and mathematical techniques useful
for developing, improving and optimizing the design process. RSM encompasses a
point selection method (also referred to as Design of Experiments, Approximations
methods and Design Optimization) to determine optimal settings of the design
dimensions. It has important applications in the design, development and formulation
of new products as well as in the improvement of existing product designs. RSM
which includes factorial design and regression analysis can build models to evaluate
the effective factors and study their interaction and select the optimum conditions in
limited number of experiments (Chauhan and Gupta, 2004).
A successive response surface method is an iterative method which consists of
a scheme to assure the convergence of an optimization process. The scheme
determines the location and size of each successive region of interest in the Design
11
Space, builds a response surface in this region, conducts an design optimization and
will check the tolerances on the response and design variables for termination. This
RSM method has been widely used to evaluate and understand the interaction between
different physiological and nutritional parameters (Ambati and Ayyanna, 2001;
Hounjg et al., 1989).
There have been a few reports on optimization lipase production by
Pseudomonas sp. whereas Liu et., al (2006) have reported 5-fold increase in lipase
yield after optimization. Production of an alkaline lipase from Burkholderia
multivorans increased by 12-fold by a combination of Response Surface Methodology
(RSM) and scale-up attempt using 14 L bioreactor (Gupta et al., 2008). Thus the level
of lipase production by the strain is significantly high and can be cost effective for its
applications.
Optimization of ethanol production from hot-water extracts of sugar maple
chips was conduct by Jian et al., 2009. Hot-water extracts from sugar maple chips
prior to papermaking was employed in this study to produce ethanol by Pichia stipitis
58784. The effects of several factors such as seed culture age, fermentation time,
inoculum quantity, agitation rate, percent extract, concentration of inorganic nitrogen
source (NH4)2SO4 and pH value on ethanol production were investigated by
orthogonal experiments. Orthogonal analysis shows that the optimal fermentation was
obtained in the condition of 48 hour seed culture, 120 hour fermentation, 16%
inoculum, 180 rpm, containing 30 % extracts, 8 % ammonium sulphate supplement
and pH 5. This optimal condition was verified at 800 mL level in a 1.3 L fermentor.
The ethanol yield reached 82.27 % of the theoretical (20.57 g/L) after 120 hour (Jian
Xu and Shijie Liu, 2009).
Optimization of temperature, sugar concentration and inoculum size to
maximize ethanol production without significant decrease in yeast cell viability was
done by Cecilia et al., 2009. Aiming to obtain rapid fermentations with high ethanol
yields and retention of high final viabilities (responses), a 23 full-factorial central
composite design combined with response surface methodology was employed using
inoculum size, sucrose concentration and temperature as independent variables. From
this statistical treatment, two well-fitted regression equations having coefficients
12
significant at the 5 % level were obtained to predict the viability and ethanol
production responses. Three dimensional response surfaces showed that increasing
temperatures had greater negative effects on viability than on ethanol production.
Increasing sucrose concentrations improved both ethanol production and viability
(Karen et al., 2009).
The interactions between the inoculum size and the sucrose concentrations had
no significant effect on viability (Meline et al., 2009). Thus, the lowering of the
process temperature is recommended in order to minimize cell mortality and maintain
high levels of ethanol production when the temperature is on the increase in the
industrial reactor. Optimized conditions (200 g/L initial sucrose, 40 g/L of dry cell
mass, 30 °C) were experimentally confirmed and the optimal responses are 80.8±2.0
g/L of maximal ethanol plus a viability retention of 99.0±3.0% for a 4 hour
fermentation period. During consecutive fermentations with cell reuse, the yeast cell
viability has to be kept at a high level in order to prevent the collapse of the process.
Optimization study for sorbitol production by Zymomonas mobilis in sugar
cane molasses was conducted by Cazetta et al., 2005. Sorbitol production by
Zymomonas mobilis in sugar cane molasses was investigated by batch fermentation.
The effects of the mean sugar concentration, temperature, agitation and culture time
were studied simultaneously by factorial analysis. Maximum sorbitol production was
determined using a second-order central composite design and analyzed by the
superficial response method. Response surface methodology analysis showed that the
best conditions for sorbitol production were 300 g/L total reducing sugars (TRS) in the
culture medium, 36 hour at 35 °C fermentation time (Silva et al., 2004).
13
2.5
Production of Sugar
Many types of plant biomass can produce the sugar using some method. For
example palm oil empty fruit bunch (POEFB) by using enzymatic hydrolysis process
(Choudhury et al., 2007), production of sugar from maize starch by using enzymatic
hydrolysis process (Singh et al., 1999).
2.5.1
Production of Xylose From Palm Oil Empty Fruit Bunch (POEFB) Using
Enzymatic Hydrolysis Process
Xylose is found in the embryos of most edible plants. Xylan, the
hemicelluloses used as raw material in xylose production. Xylanase, the enzyme that is
used to hydrolysed the structure of xylan to produce xylose. This study aims to
improve the production of xylose from palm oil empty fruit bunch (POEFB) using
enzymatic hydrolysis process. The effect of cultural conditions such as enzyme
concentration and incubation time is used to study the production of xylose. Three
different xylanases enzyme are used to produce xylose namely Multifect Xylanase,
Multifect CX 12L and optimase CX 72L. Multifect CX 12L was the best enzyme that
yielded the most xylose. The optimization of the production of xylose is carried out by
using response surface methodology (RSM) based on the central composite design
(CCD). The optimal set cultural conditions for enzyme concentration are 50 U/g and
35.5 hours of incubation. Xylose production of 23.15 mg/mL was achieved by using
these optimal conditions (Rahman et al., 2007).
2.5.2
Production of Sugar From Maize Starch Using Enzymatic Hydrolysis
Process
Crude amylases were prepared from Bacillus subtilis ATCC 23350 and
Thermomyces lanuginosus ATCC 58160 under solid state fermentation. The effect of
various process variables was studied for maximum conversion efficiency of maize
starch to glucose using crude amylase preparations. Doses of pre-cooking α-amylase,
post-cooking α-amylase, glucoamylase and saccharification temperature were found to
14
produce maximum conversion efficiency and were these selected for optimization.
Full factorial composite experimental design and response surface methodology were
used in the design of experiments and analysis of results. The optimum values for the
tested variables for the maximum conversion efficiency were: pre-cooking α-amylase
dose 2.243 U/mg solids, post-cooking α-amylase dose 3.383 U/mg solids,
glucoamylase dose 0.073 U/mg solids at a saccharification temperature of 55.1 °C.
The maximum conversion efficiency of 96.25 % was achieved. This method was
efficient; only 28 experiments were necessary to assess these conditions, and model
adequacy was very satisfactory, as coefficient of determination was 0.9558
(Adinarayana et al., 2005).
2.5.3
Production of Sugar From Sorghum Straw Using Acid Hydrolysis Process.
Xylose is a hemicellulosic sugar that can be used as a carbon and energy
source for the growth of microorganisms. The main use of xylose is its bioconversion
to xylitol. Sorghum straw is a raw material for xylose production that has not yet been
studied. The objective of this work was to study xylose production by hydrolysis of
sorghum straw at 122 °C, using three concentrations of sulphuric acid (2 %, 4 % and 6
%). Kinetic parameters of mathematical models for predicting the concentration of
xylose, glucose, acetic acid and furfural were found and optimal conditions selected.
These were 2 % H2SO4 at 122 °C for 71 minutes, which yielded a solution with 18.17
g xylose/L, 6.73 g glucose/L, 0.9 g furfural/L and 1.51 g acetic acid/L (Ramirez et al.,
2001).
15
CHAPTER 3
MATERIAL AND METHODS
3.1
Overview of Research Methodology
Basically, there are three steps to be accomplished in producing the highest
yield of sugar. The first step is to performing an enzymatic hydrolysis method on the
cassava residue by (i) screening the best enzyme (ii) studying the effects of
parameters; incubation time and enzyme concentration. Finally, optimize the
parameters to maximize the production of sugar by using response surface
methodology (RSM). Details on this research design are shown in Figure 1.6.
1. Screening of Enzymes: Enzymatic hydrolysis
Cellulase
Glucoamylase
α-amylase
2. Effect of parameters on the best enzyme
 Incubation time
 Enzyme concentration
3. Optimization by RSM
Figure 1.6: Research design for the production of sugar
16
3.2
Enzymatic Hydrolysis
Experiment was carried out with constant pretreated substrate 1 g (dried
weight) in shake flask (250 mL volume) with enzyme. The substrate was suspended
to mixture are Sodium Hydroxide (NaOH) and Citric acid (C6H8O7) as buffer. The pH
of buffer was measured by pH meter. The enzymatic hydrolysis was performed by
incubating the mixture in an oven. Supernatant was collected through filter paper.
Reaction was stopped by boiling the mixture at 100 °C for 3 minutes.
3.3
Screening of Enzymes
Different types of enzyme were used to determine the best enzyme that can
produce the most yield of sugar by feeding three types of commercial enzymes into the
treated cassava residue: (1) Cellulase, (2) Glucoamylase, (3) α-amylase. On cellulase
treatment, cassava residue was hydrolyzed with cellulase at PH 4.8, 40 °C, α-amylase
at pH 5.5, 100 °C and glucoamylase at pH 4.5, 60 °C. Time to hydrolyze is 7 hour and
concentration of each enzymes is 1 g/mL.
3.4
Effect of Parameters
In the production of sugar, the yield of the product depends on its cultural
parameters in executing the experiments. There are various parameters that affect the
yield of sugar such as enzyme concentration, incubation time, reaction time, reaction
temperature, agitation rate, pH of buffer used and also the concentration of substrate.
However, this research is to study about the effect of incubation time and
enzyme concentration. It is believe that these two parameters give a big impact on
sugar yield.
17
3.5
Effect of Incubation Time
The best enzyme chosen from above is used in this step but with a range of
time of 2, 4, 6, 8, 10 and 12 hours.
3.6
Effect of Enzyme Concentration
The best time will then used for further study on cultural conditions but with a
range of concentration of 0.5, 1.0, 1.5, 2.0 and 2.5 g/mL.
3.7
Concentration of Glucose
This concentration of glucose analysis is to determine how many reducing
glucose yield from the hydrolyzed samples all along this research experiments.
3.7.1
Preparation of DNS reagent
An amount of 10.6 g of 3,5 - dinitrosalicylic acid crystals was dissolved with
19.8 g of NaOH in 1,416 of distilled water. 306 g of sodium-potassium tartrate
(Rocchelle salts) was added into the mixture. A phenol crystal was melt at 50 °C using
a water bath. Then 7.6 mL of the dissolved phenol was added to the mixture. Next, 8.3
g of sodium meta-bisulfate (Na2sS2O4) was added and finally, NaOH was added to
adjust the pH to 12.6, if required (Fisher and Stein, 1961).
3.7.2
Concentration of Glucose Analyzed by DNS
Total glucose produced was measured by DNS method (Sanjeev et al., 2002).
The reaction was done by boiled 2 mL of diluted samples in test tubes for 3 minutes in
water at 100 °C. At the end of boiled period, 2 mL of the sample is then mixed with 2
mL of DNS reagent to stop the reaction followed by boiled for 5 minutes to generate
18
the colour. A colour change was measured at wavelength 540 nm by using Uv-vis
Spectrophotometer. Blank lacking of distilled water was analyzed simultaneously with
each batch of samples. Standard curve of glucose was prepared by replacing the
sample with various concentration of glucose. Concentration glucose was calculated
from the absorbance (A540nm) of the sample using the calibration curve.
3.8
Response Surface Methodology (RSM)
Design Expert Software (State-Ease Inc, Statistic Made Easy, Minneapolis,
MN, USA, (Version 6.0.4) was applied to execute the response surface methodology.
The central composite design (CCD) was chosen in the optimization process due to its
ability in providing factorial analysis in 2 levels of the factors involves (enzyme
concentration & incubation time), which will be from the centre point to the star point.
The relation between the coded values and actual values is described in Equation 3.1:
Xi = (Ai – Ao) / ∆A
(Equation 3.1)
Where;
Xi = Coded value of the variable
Ai=Actual value
Ao= Actual value of Ai at the centre point
The quadratic model to predict the optimal point is coded in Equation 3.2:
Y=βo+∑βiXi+∑βiiXi2+∑βijXiXj
Where;
Y= Predict response variable
βo= Offset term
βi= Linear effect
βii= Interaction effect
(Equation 3.2)
19
X= Coded levels of the independent variables
The regression equation above was optimized for the optimal values using
Design Expert Software. An experiment was conducted in a randomized order to stay
away from systematic bias. Next, the ANOVA test was used to analyzed the
experimental data obtained. P-value was determined to identify the significance of the
quadratic model. A P-value ≤0.05 are considered to be significant.
20
CHAPTER 4
RESULT AND DISCUSSION
4.1
Screening of Enzymes
Three types of enzymes were screened to determine which one of them is the
best enzyme that can hydrolyze the cassava residue producing the most glucose. These
enzymes were used as catalysts to produce glucose that contain in cassava residue.
Figure 1.7 shows the difference between these three enzymes in concentration
of glucose. The experiments were conducted in the same cultural conditions which are
incubated at 7 hours in an oven with 1 g/mL of each enzyme concentration.
Cellulase shows the highest yield of concentration of glucose in Figure 1.7. An
amount of 1.018 g/L, 0.6813 g/L and 0.9664 g/L of concentration were detected from
cellulase, glucoamylase and α–amylase respectively. Based on report Mark et al.,
(2007), hydrolyses with cellulase had higher yields of glucose, galactose and xylose
than hydrolyses without cellulase. Moreover, Krishnamurthy et al., (2010) reported
that, by using cellulose pretreatment for the any production, higher yield will be
obtained. Therefore, cellulase enzyme was used for the next study on the effect of
cultural conditions on glucose concentration.
21
Concentration glucose (g/L)
1.2
1
0.8
0.6
0.4
0.2
0
Glucoamylase
Cellulase
Types of enzyme
α-amylase
Figure 1.7: Screening of different enzyme.
4.2
Effect of Parameters on Glucose Concentration
Studies on the parameters that affect the concentration of glucose were carried
out by using the conventional method. The method studies one factor at a time
(OFAT) while keeping the others at a certain level. This study was done to determine
the optimum range of parameters for further optimization process. Principally, the low
and high levels of every parameter were obtained before and after peak values
respectively in the appropriate experiments. The parameters involved were incubation
time and enzyme concentration.
4.2.1
Effect of Incubation Time
The best time was determined by conducting an enzymatic hydrolysis, where
the samples were incubated in an oven at 40° C by using 1 g/mL enzyme
concentrations, whereas the incubation time is varied in a range from 2 – 12 hours.
The effects of incubation time on the concentration of glucose are shown in Figure 1.8.
22
Concentration gluose (g/L)
1.2
1
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
12
14
Time (hour)
Figure 1.8: Effects of incubation time
Based on the result in Figure 1.8, initially the concentration of glucose
produced increased exponentially at the time of 2 – 6 hour. Based on report by Hao et
al., (2010), as substrate loading increased, the end-product inhibition caused by
cellobiose and glucose increased. It is because, the enzyme produces product at an
initial rate that is approximately linear for a short period after the start of the reaction.
When there were higher of time, the more the interaction between enzymes and
substrates.
However, as the reaction proceeds and substrate is consumed, the rate
continuously slows. Based on the Figure 1.8, at the time of 8 – 12 hour the
concentration of glucose started to decreased. Based on report Cinar, (2005), the
higher enzyme loadings resulted in faster total hydrolysis and probably end-product
inhibition occurred. Moreover, Kristense, (1972) reported that a digestibility trials
undertaken on redclaw gastric juice here showed that reducing sugars could be
produced in relatively short time frames suggesting that cellulose digestion by redclaw
occurs rapidly. As redclaw have very short, simple digestive tracts effectively a
straight tube. Digestive enzymes need to act rapidly as digestive times are relatively
rapid.
23
The best time to be used in order to obtain the maximum yield of glucose is 6
hours. This condition will then used for the effect of enzyme concentration.
4.2.2
Effect of Enzyme Concentration
Based on the result from the effect of enzyme concentration, the best time was
used to study the effect on enzyme concentration. All of the samples were incubated
for 6 hours, in an oven at 40 °C by using a various concentration of enzyme in a range
of 0.2 – 2.5 g/mL. The effects of enzyme concentration on the concentration of
glucose are shown in Figure 1.9.
Concentration glucose (g/L)
1.06
1.04
1.02
1
0.98
0.96
0.94
0.92
0.9
0.88
0.86
0
0.5
1
1.5
2
2.5
3
Concentration enzyme (g/mL)
Figure 1.9: Effect of enzyme concentration
Based on the result in Figure 1.9, initially the concentration of glucose
produced increased exponentially at the concentration of 0.5 – 1.5 g/mL. However, at
the concentration of 2.0 – 2.5 g/mL the concentration of glucose started to decreased.
When there were higher concentration of enzyme, the more the interaction
between enzymes and substrates so, the concentration of glucose increased initially.
Based on report by Rajesh et al., (2009), higher enzyme loading rate decreased the
24
migration time of cellulases on the cellulose surface and increased the liberation of
reducing sugars till the complete surface coverage of the substrate.
Moreover, Sarkar et al., (1998) reported that increase in enzyme concentration
saturated the active sites of the enzyme with substrate leading to lower activity.
Nevertheless, when there are excessive of enzymes, there are higher probabilities of
inhibitors to inhibit these enzymes that caused by proteins interaction. This may
deduce the results that less glucose concentration as it is been proved by the above
Figure 1.9 at the concentration of 2.0 - 2.5 g/mL of enzyme.
Based on a report by Nathalie et al., (2006), the understanding of proteins
interactions is important for plant protection. The protections could be required to
enhance plant resistance. The role of plant cell wall polysaccharides in disease
resistance has been reviewed recently. To penetrate and use plant cell walls
nutritionally, pathogens secrete a remarkable array of polysaccharides degrading
enzymes
including
exopolygalacturonases,
endopolygalacturonases,
pectin
methylesterases, pectin lyases and pectate iyases, acetyl esterases xylanases and a
variety of endogluconases that cleave cellulose, plants resists hydrolytic attack by
deploying inhibitor proteins of Cell Wall Degrading Enzymes (CWDE).
As a result, the best enzyme concentration was at the 1.5 g/mL. Clearly, these
two parameters; incubation time and enzyme concentration are very important in
concentration of glucose. Thus, these factors are used for further study in the
optimization to obtain higher yield of glucose concentration.
25
4.3
Determination of the Optimum of Parameters Sugar Production
Using Response Surface Methodology (RSM)
Optimization on the parameters in glucose productions are performed using
response surface methodology (RSM). The parameters involved are incubation time
and enzyme concentration. The low level and high level of the parameters are
determined from the previous result. Table 2.2 shows the low level and high level of
each parameter.
Table 2.2: Low level and high level of parameters
Parameters
Incubation Time (hr)
Enzyme Concentration (g/mL)
Low Level
High Level
4
8
1.0
2.0
By using central composite design (CCD), the experiments with different
combination of enzyme concentration and reaction time were performed. Experiments
arranged by Design Expert Software and the producing of concentration of glucose
are listed Table 2.3
Run 4 gave the highest concentration of glucose which produced 1.063 g/L of
concentration. The parameters of Run 4 are: 1.50 g/mL of enzyme concentration and 6
hours of incubation time. Conversely, Run 3 gave the lowest concentration of glucose
which produced only 0.8122 g/L of concentration. The parameters of Run 3 are: 1.00
g/mL of enzyme concentration and 8 hours of incubation time. This is because,
according to Marimuthu et al., (2005), less prolonged reaction time, the better sugar
yields. Moreover, Sun and Cheng, (2002) reported that a increasing the dosage of
cellulases in the process, to a certain extent, can enhance hydrolysis yields.
26
Table 2.3: Experiments designed by design expert software
Run
Factor 1 A: Time
(hr)
Factor 2 A: Enzyme
Concentration (g/mL)
1
2
3
4
5
6
7
8
9
10
11
3.17
6.00
8.00
6.00
6.00
4.00
8.00
8.83
4.00
6.00
6.00
1.50
0.79
1.00
1.50
1.50
2.00
2.00
1.50
1.00
2.21
1.00
Response:
Reducing Sugar
(g/L)
1
0.9898
0.8122
1.063
1.022
1.04
0.8832
0.7921
1.053
0.9932
1.042
Table 2.4 shows the ANOVA and regression analysis for the concentration of
glucose. The precision of a model can be checked by determination coefficient (R2)
and correlation confession (R). As a rule, a regression model having an R2 value
higher than 0.9 is considered to have a very high correlation (Haaland, 1989). The
value of R indicates better correlation between the experimental and predicted values.
In Table 2.4, the P-value obtained for regression model 0.0006 compared to a
desired significant level of 0.05. This signified that the regression model is precise in
predicting the pattern of significance to the concentration of glucose.
27
Table 2.4: ANOVA for response surface quadratic model for the concentration of
glucose.
Sources
Sum of
Square
0.092
Model
Degree
of
Freedom
5
Mean
Square
FValue
P-Value R2
(Prob>F)
35.83
0.0006
116.89
0.0001
0.96
0.3713
57.72
0.0006
6.67
0.0492
1.36
0.4500
0.9728
Significant
0.018
A-Enzyme
concentration
B-Incubation
Time
1
0.060
0.060
1
4.931E004
4.931E004
1
A2
0.030
0.030
1
B2
3.413E003
3.413E003
5
Residual
2.558E003
5.115E004
3
Lack of Fit
1.717E003
5.723E004
2
Pure Error
8.407E004
Correlation
Total
Not
Significant
4.203E004
10
0.094
To investigate the effects of the two parameter on the concentration of glucose,
the response surface methodology was used and the three-dimensional plot was drawn.
Figure 1.10 shows the response surface plot for the parameters studied; incubation
time and enzyme concentration.
Incubation time and enzyme concentration gave the significant effect to the
concentration of glucose (Figure 1.10). Higher enzyme concentration and shorter
incubation time increase the concentration of glucose, whereas lower enzyme
concentration and longer incubation time decrease the concentration of glucose. The
maximal concentration of 1.063 g/L glucose was obtained when using 1.5 g/mL of
enzyme concentration and 6 hours of incubation time.
28
Based on report by Marimuthu et al., (2005), less prolonged reaction time, the
better sugar yields. Moreover, Sun and Cheng, (2002) reported that a increasing the
dosage of cellulases in the process, to a certain extent, can enhance hydrolysis yields.
The production of total and individual soluble glucose reached the maximal level after
five hours of reaction. Extending the reaction beyond five hours enhanced the yields of
glucose, but did not result significant increase in the concentration of total soluble
glucose from cassava residue.
Figure 1.10: Response surface plot of glucose concentration: Incubation time vs.
enzyme concentration.
29
4.4
Optimization of Sugar Production Using Response Surface Methodology
(RSM)
Based on the Table 2.5, the concentration of glucose was successfully
optimized after the optimization process was carried out. The Incubation time and
enzyme concentration used was reduced after optimization. Table 2.5 shows summary
of the optimized parameters for concentration of glucose.
Table 2.5: Summary of the optimized parameters for sugar production
Parameters
Before Optimization
Value
Concentration
After Optimization
Value
Concentration Glucose (g/L)
Glucose (g/L)
Incubation
Time (hr)
8.83
Enzyme
concentration
(g/mL)
Experimental
1.066
1.059
4.47
0.7921
1.50
Predict
1.32
30
CHAPTER 5
CONCLUSION AND RECOMMENDATIONS
5.1
Conclusion
The concentration of glucose was successfully obtained by hydrolyzing the
cassava residue by using hydrolyses enzymes. Three types of enzymes which are
cellulase, glucoamylase and α-amylase have been employed to hydrolyze the substrate;
cassava residue to produce glucose. The best enzyme that yields the most glucose is
cellulose, followed by α-amylase and glucoamylase.
Therefore, cellulose is used for further parameters studied by (i) incubation time
at 4 – 8 hours and (ii) enzyme concentration in the range of 0.5 – 2.5 g/mL. Based on
the result before optimization, the response surface methodology shows that the
conditions in concentration of glucose by using 1.50 g/mL of enzyme concentration at
8.83 of incubation time producing only 0.7921 g/L of reducing glucose. However after
optimization occurred, the concentration of glucose was successfully increased until
1.059 g/L using the optimum condition of 1.32 of enzyme concentration at 4.47 of
incubation time.
This research demonstrated that this agricultural waste of cassava residue which
had no economical value could be converted by enzymatic hydrolysis to a more
valuable product.
As a result, with optimum use of cassava residue, it will not only solve the
environmental pollution problems but also will give back a high economic value
product to the cassava starch plant industry above all.
31
5.2
Recommendations
In order to improve this research method and results to a better concentration
of glucose, it can be carried out in various modifications on these parameters. The
concentration of glucose using the method of enzymatic hydrolysis can also be done
using the method of acid hydrolysis which is cheaper in terms of the cost of raw
material. Besides using only single enzyme, the concentration of glucose can be
increased by mixing the three commercialized enzymes (cellulose, glucoamylase and
α-amylase). There are also differences when using immobilize enzyme and free
enzyme in concentration of glucose which can be studied to compare the concentration
between the two types of enzymes.
The concentration of glucose was analyzing using DNS reagent. But for future
studies, analyzing the samples using High Performance Liquid Chromatography
(HPLC) can gives more accurate concentration of glucose. In this study, the substrate
used is cassava residue. In future, to study the concentration of substrate will give the
significant in concentration of glucose. Moreover, there are more substrate other than
cassava residue that are also capable of producing sugar such as Cotton Stalk, Corn
Stover, Wheat Straw, Tobacco Stalk and Palm Oil Empty Fruit Bunch.
This research can also be done by varying the agitation rate and the reaction
temperature while incubating. The pH of the buffer may give effect on the
productions. Therefore, this can also be study by varying the pH of buffer. In addition,
other kind of buffer can also be used such as Potassium Phosphate and Ampso.
32
REFERENCES
Adinarayana, K. and Suren, S. (2005). Response Surface Optimization of Enzymatic
Hydrolysis of Maize Starch for Higher Glucose Production. Biochemical
Engineering. 27: 179-190
Ammbati, P. and Ayyanna, C. (2001). Optimization of Medium Constituents and
Fermentation Condition for Citric Acid Production from Palmyra Jaggery
using Response Surface Methodology. World Journal of Microbiology and
Biotechnology. 17: 331-335
Badal, C. S. (2004). Production, purification and properties of endoglucanase from a
newly isolated strain of Mucor circinelloides. Process Biochemistry.
39: 871-1876
Cecilia, L. Joao, O. T. Karen, F. O. Crisla, S. S. and Meline, R. M. (2009).
Optimization of temperature, sugar concentration, and inoculum size to
maximize ethanol production without significant decrease in yeast cell
viability. Biotechnological Products and Process Engineering. 83: 627-637
Chauhan, B. and Gupta, R. (2004). Application of Statistical Experimental Design for
Optimization of Alkaline Protease Production from Bacillus sp. RGR-14.
Biochemistry Process. 39: 2115-2122
Gaur, R. and Gupta, A. (2008). Lipase from Solvent Tolerant Pseudomonas
aeruginosa
strain:
Production
Optimization
by
Response
Surface
Methodology and Application. Bioresource Technology. 99: 4796-4802
Gunda, K.P.B. and Walteir, L. (1970). Enzymatic Degradation of Cellulose,
Cellulose Derivatives and Hemicelluloses In Relation to the Fungal Decay of
Wood 1. Wood Science and Technology. 4: 273-283
Hao, F. Chen, Z. and Xiang, Y.S. (2010). Optimization of enzymatic hydrolysis of
steam-exploded corn stover by two approaches: Response surface methodology
or using cellulase from mixed cultures of Trichoderma reesei RUT-C30 and
Aspergillus niger NL02. Biosource Technology. 101: 4111-4119
Sowbhagya, H.B. Srinivas, P. and Krishnamurthy, P. (2010). Effect of enzymes on
extraction of volatiles from celery seeds. Food Chemistry. 120: 230-234
33
Herrera, S. J. Tellez, L. Ramirez, J. A and Vazquez, M. (2003). Production of Xylose
from Sorghum Straw using Hydrochloric Acid. Cereal Science.
37: 267-274
Houjg, J.Y. Chen, K. C. and Hsu, W.H. (1989). Optimization of Cultivation Medium
Composition for Isoamylase Production. Journal of Applied Microbiology and
Biotechnology. 39: 61-64
Howard, R.L.A. Jansen, E.V.R. and Howard, S. (2003). Lignnocellulose
Biotechnology: Issues of Bioconversion and Enzyme Production. African
Biotechnology. 2: 602-619
Inci, C. (2005). Effects of cellulase and pectinase concentrations on the
colour yield of enzyme extracted plant carotenoids. Process Biochemistry. 40:
945-949
Jian, X. and Shijie, L. (2009). Optimization of Ethanol Production from Hot-water
Extracts of Sugar Maple Chips. Renewable Energy. 34: 2353-2356
Jin, Z. Yong, H.W. Ju, C. Ling Z. L. Ying, P. Z. and Si, L. Z. (2010. Optimization of
cellulase mixture for efficient hydrolysis of steam-exploded corn stover by
statistically designed experiments. Biosource Technology. 101: 4111-4110
Kristensen, J.H (1972). Carbohydrases of Some Marine Invertebrates With Notes on
Their Food and on the Natural Occurrence of the Carbohydrates Studied.
Biotechnology.14: 130–142.
Kyung, H.C, Hee, S.J, Na, K.L. Se, H.P, Na, W.L. and Hyun, D.P. (2009).
Optimization of the Enzymatic Production of 20 (S)-Ginsenoside Rg3 from
White
Ginseng
Extract
using
Response
Surface
Methodology.
New
Biotechnology. 26: 3-4
Li, L. Liu, X. Guo, Y and Ma, E (2005). Activity of the enzymes of the antioxidative
system
in
cadmium-treated
Oxya
chinensis
(Orthoptera
Acridoidae).
Environmental Toxicology and Pharmacology. 20: 412-416
Mark, R. W. Wilbur, W.W. Karel, G. and Randall G.C. (2007). Hydrolysis of
grapefruit peel waste with cellulase and pectinase enzymes. Bioresource
Technology. 98: 1596–1601
Cazetta, M.L. Celligoi, M.A.P.C. Buzato, J.B. Scarmino, I.S. and Silva, R.S.F.
(2005). Optimization Study for Sorbitol Production by Zymomonas Mobilis in
Sugar Cane Molasses. Process Biochemistry. 40: 747-751
34
Marimuthu, J. Ye, W.Z. In, W.K and Jung, K. L. (2009). Enhanced Saccharification
of Alkali-Treated Rice Straw by Cellulase from Trametes Hirsuta and
Statistical Optimization of Hydrolysis Conditions by RSM. Bioresource
Technology.100: 5155-516
Mutanda, T. Wilhelmia, B. S. and Whiteley, C. G. (2008). Response Surface
Methodology: Synthesis of Inulooligosaccharides with an Endoinulinase from
Aspergillus Niger. Enzyme and Microbial Technology. 43: 362-368.
Nathalie, J. (2006). Plant Protein Inhibitors of Cell Wall Degrading Enzymes. Trends
In Plant Science. 23: 359-367
Rajesh, S. Rajender, K, Kiran, B and Narsi, R.B. (2009). Optimization of Synergistic
Parameters for Thermostable Cellulase Activity of Aspergillus Heteromorphus
using Response Surface Methodology. Biochemical Engineering. 48: 28-35
Richard F. T. John, K. and Xi, Q. (2004). Starch-Composition, Fine Structure and
Architecture. Journal of Cereal Science. 39: 151-165
Rahman, S.H.A. Choudhury, J.P. Ahmad, A.L. and Kamaruddin, A.H. (2007)
Optimization Studies on Acid Hydrolysis of Oil Palm Empty Fruit Bunch Fiber
for Production of Xylose. Bioresource Technology. 98: 554-559
Sun, Y. and Cheng, J. (2002). Hydrolysis of Lignocellulosic Materials for Ethanol
Production: A review. Bioresoure Technolology. 83: 1–11
Steve, J. (2004). Improving starch for food and industrial applications. Current
Opinion in Plant Biology. 7: 210-218
Suthkamol. S. Ancharida, B. F. and Teerapatr, S. (2006). Conversion of Cassava
Waste into Sugar Using Aspergillus Niger and Trichoderma reesei for Ethanol
Production. Biotechnology. 54: 234-240
Teerapatr. S, Cholada, S. Bangatrat, P. Wichien, K. and Sirintip, C. (2004). Utilization
of Waste from Cassava Starch Plant for Ethanol Production. Sustainable
Energy and Environment (SEE). December 1-3. Hua Hin, Thailand, 360 – 365
Teerapatr, S. Lerdluk, K and La, S. (2006). Approach of Cassava Waste Pretreatments
for Fuel Ethanol Production in Thailand. Biotechnology. 31: 241-246
Xiao, M.X. Alys, J.A. Neil, A.R. Alex, J.A. Gang, P.X. and Peter, B.M. (1999).
Charac terisation of Cellulase Activity in the Digestive System of the
Redclaw Crayfish (Cherax quadricarinatus). Aquaculture. 180: 373-386
35
APPENDIX A
MATERIAL AND METHODS
Appendix A1
Preparation mixture of Sodium Hydroxide (NaOH), (2000 mL) and Citric Acid
(C6H8O7), (1000 mL) as Buffer
i)
Preparation of Sodium Hydroxide (NaOH), (2000 mL)
Molecular weight of Sodium Hydroxide, NaOH = 40 g/mol
Prepare volume of 10.8 mL from 0.1 M Sodium Hydroxide (NaOH) solution
Then, 10.8 mL Sodium Hydroxide ( NaOH) was diluted with 1000 mL
distilled
ii)
water in volumetric flask.
Preparation of Citric Acid (C6H8O7) , (1000 mL)
Molecular weight of Sodium Hydroxide, NaOH = 192.14 g/mol
Prepare mass of 19.214 g from 0.1 M on Citric Acid (C6H8O7)
Therefore, 19.2 g of Citric acid (C6H8O7) was mixed into 1000 mL distilled
water.
iii)
Preparation of buffer
Then, add some drop of Sodium Hydroxide, (NaOH) and Citric Acid
(C6H8O7) into the mixture to achieve the value of pH needed.
36
Appendix A2
Preparation of DNS Reagent (1 L)
Table A – 1: Ingredients of DNS Reagents
No.
Chemical
g/L
1.
DNS Acid
10
2.
Phenol Crystal
2
3.
Sodium Sulfite
0.5
4.
Sodium Hydroxide
10
5.
Potassium – Sodium Tartarate
182
37
Appendix A3
1.
Preparation of 1 mg/mL of Glucose Standard Solution (10 mL)
1 mg/mL = 1 mg of glucose in 1 mL buffer
1 mL = 1 mg
10 mL = 10 mg
Hence, 10 mg of glucose is added into 10 mL of buffer (Mixture of 0.1 M
Sodium Hydroxide, NaOH with 0.1 M Citric Acid)
2. Standard Curve of Glucose
Concentration Glucose (g/L)
OD (λ = 540 nm)
0.2
0.424
0.4
1.158
0.6
1.834
0.8
2.276
1.0
2.482
Standard Curve of Glucose
3
2.5
y = 2.617x + 0.0646
R² = 0.9572
A 540
2
1.5
1
0.5
0
0
0.2
0.4
0.6
0.8
Concentration glucose (g/L)
Figure A – 1: Standard Curve of Glucose
1
1.2
38
Appendix A 4
Enzyme Dilution
(W enzyme / V buffer) % = g/mL
Thus, W enzyme = 0.5 g, 1.0 g, 1.5 g, 2.0 g, 2.5 g
V buffer = 100 mL
Therefore, add each weight of enzyme into 100 mL of buffer buffer (Mixture of 0.1 M
Sodium Hydroxide, NaOH with 0.1 M Citric Acid).
39
APPENDIX B
OPTIMIZATION PROCESS
Appendix B1
ANNOVA Results for Optimization of Cultural Conditions
Figure B – 1: Graph of the normal plot of residual of sugar production
40
Figure B-2: Residual versus predicted values of sugar production
Figure B-3: Residual values of sugar production for each run experiment
41
Figure B-4: Residuals versus time values of sugar production
Figure B-5: Graph of the Outlier T of sugar production
42
Figure B-6: Graph o the Cook’s distance of sugar production
Figure B-7: Leverage versus run values of sugar production
43
Figure B-8: Graph of predicted versus actual values of sugar production
Figure B-9: Plot of the Box-Cox for power transforms