Identification and Optimization of a Liquid Medium for the
Culture of Colletotricum graminico/a
An Honors Thesis (HONRS 499)
by
Jennifer L. Minter
Thesis Advisor
Dr. James Mitchell
Ball State University
Muncie, Indiana
May 2004
Expected Date of Graduation
May 8, 2004
Abstract
Crude carbon compounds were evaluated in several stages to determine the optimal
liquid-culture medium for conidial production in Colletotrichum graminicola. Among
those tested, use of unfiltered V8 medium resulted in optimum conidiation. % v/v of this
medium was evaluated along with pH and conductivity using a central composite design
to optimize conidiation in C. graminicola. The predicted titer using this model was
4.78x10 7 conidia/mL, using a medium consisting of 40.14% v/v V8, with a pH of6.16
and a conductivity of28.03mS.
Acknowledgements
•
I would like to thank Dr. James Mitchell for his tireless help throughout the entire
process. He trained me on technique, provided all of the materials, and assisted
me with many experiments. He also helped me sort through the results of each
individual experiment and edited my drafts of the written paper.
•
I would also like to thank Dr. David LeBlanc for providing me with a few critical
numbers for my statistical analysis. Also, his course on statistics in biology made
it possible for me to analyze my data.
Table of Contents
Introduction and Rationale
1
Identification and optimization of a liquid medium
4
for the culture of Colletotrichum graminicola
Appendix A
10
AppendixB
12
Appendix C
19
Appendix D
24
Appendix E
31
Appendix F
43
Introduction and Rationale
A major problem for farmers has always been weeds, which has been dealt with
in several ways throughout human history. In the past, farmers relied entirely on manual
weeding. Many farmers, particularly in less-developed regions, rely on this technique
today. However, with the recent increase in the size of farms, manual weeding quickly
became impractical. The current strategy for these farmers is usually herbicides.
Although they are often the only recourse, herbicides pose environmental concern. Most
do not rapidly degrade in the environment and thus persist long after the weeds have been
killed. They have been detected in our surface waters [1, 11], the waters that Muncie and
many communities largely rely on for drinking water.
An alternative to chemical herbicides is the use ofbioherbicides. Bioherbicides
are organisms, usually fungi, which naturally infect the unwanted weed in the wild. Most
fungi are host-specific, meaning that they only infect certain plants and leave others
largely alone. A farmer can apply the organism to his weeds in order to infect and kill
the plants without harming his crops. Typically, when a fungal bioherbicide is applied
with the same application technologies as chemical herbicides [19], it infects and kills the
weed host within 1-2 weeks. After death ofthe weed host, the bioherbicide organism
naturally dies back to its usual numbers in the environment because its food source is
depleted. In contrast to chemical herbicides, the use of fungal bioherbicides does not
result in any toxic substance that could accumulate in the environment or appear as
surface or groundwater contaminants.
1
One weed that poses major problems for farmers is johnsongrass [Sorghum
halepense (L.) Pers.]' It is an exotic grass native to the Mediterranean region which has
established itself in warm regions of all major agricultural areas of the world [13]. It has
been reported as one of the world's ten worst weeds [10]. Johnsongrass reduces crop
yields in com [2, 12, 16], soybeans [23], and cotton [IS]. It also hosts insect and disease
pests of grain sorghum [9, 13], and interbreeds with grain sorghum [13].
Numerous chemical herbicides have been developed and tested for efficacy
against johnsongrass [8, 18]. Several fungal bioherbicides have also been proposed for
the control ofjohnsongrass [5,6, 14, 15,24]. One of these is Colletotrichum graminicola
(Ces.) Wils., which causes anthracnose [14], a plant disease. This organism has also been
proposed as a means to control barnyard grass [Echinochloa crus-gallI], a common weed
problem in rice [26].
Only three fungal bioherbicides have been registered for use in North America
[17,20]. One of the three fungal bioherbicides was Collego, which contained a different
species of Colletotrichum; C. gloeosporioides [3, 20]. These fungi infect by using
spores, a dormant product of fungal reproduction, which germinate on the plant and
infect it. Low-cost methods for producing infective spores must be used in order for a
profit to be maintained. Submerged-culture fermentations are currently considered to be
the most economical method of production [7]. In this method, the fungus grows and
produces spores in a liquid medium. This study aimed to develop an optimized
submerged-culture medium for the sporulation of Collelolricum graminicola.
Over the course of four months, many media were tested. To begin, basic liquids
were tried, such as the brine juice in canned vegetables, V8, sugar solutions, coffee, and
2
tea. Based on the results of that experiment, V8 juice, com syrup, and canned pea brine
were selected for further testing. Subsequent experiments tested various concentrations
of those media and also tested several additives to the basic media, including the
ingredients in Collego medium that were effective for the other species of
Colletotrichum. The results eliminated com syrup and pea brine from the possibilities.
After determining that unfiltered V8 led to more spores than filtered V8, this medium
was used in the final experiment. The final experiment made use of a statistical tool in
the JMP4 software which predicted the optimum conditions for the best sporulation of the
fungus. The conditions tested were V8 concentration, pH, and conductivity. Optimal
sporulation was predicted at 40.14% V8 by volume with a pH of 6.16 and a conductivity
of28.03mS. The predicted amount of spores with these conditions was 47.8 million
spores per milliliter. The results indicate a very promising spore production in this
medium, but more research would be necessary to determine the effect the spores
produced in. this medium would have on the johnsongrass plant.
3
Identification and optimization of a liquid medium for the
culture of Colletotrichum graminicola
Jennifer L. Minter
Honors Thesis Credit (HONRS 499)
Department of Biology, Ball State University, Muncie, IN 47306, USA
May 2004
Abstract
Crude carbon compounds were evaluated in several stages to determine the optimal liquid-culture medium
for conidial production in Colletotrichum graminicola. Among those tested, use of unfiltered V8 medium
resulted in optimum conidiation. Percent v/v of this medium was evaluated along with pH and conductivity
using a central comfosite design to optimize conidiation in C. graminicoia. The predicted titer using this
model was 4.78xlO conidialmL, using a medium consisting of40.14% v/v V8, with a pH of6.16 and a
conductivity of28.03mS.
Several fungal bioherbicides have been
proposed for the control of johnsongrass [5,6,
Introduction
14, 15,24]. One of these is Colletotrichum
graminicola (Ces.) Wits., which causes
Johnsongrass [Sorghum halepense (L.) Pers.] is
an exotic grass native to the Mediterranean
anthracnose [14]. This organism has also been
region which has established itself in warm
proposed as a means to control barnyard grass
regions of all major agricultural areas of the
[Echinochloa crus-galli], a common weed
world [13]. It has been reported as one of the
problem in rice [26].
world's ten worst weeds [10]. Johnsongrass
Only three fungal bioherbicides have been
reduces crop yields in com [2, 12, 16], soybeans
registered for use in North America [17, 20].
[23], and cotton [15]. It also hosts insect and
One of the three fungal bioherbicides was
disease pests of grain sorghum [9, 13], and
Collego, which contained a different species of
hybridizes with grain sorghum [13]. This
Colletotrichum; C. gloeosporioides [3, 20].
perennial grass propagates by seeds and
Low-cost methods for producing infective spores
rhizomes, with propagation by rhizomes leading
must be used in order for a profit to be
to the most detrimental effects on crop yields
maintained. Submerged-culture fermentations
[16].
are currently considered to be the most
Numerous herbicides have been developed
economical method of production [7]. This
and tested for efficacy against johnsongrass [8,
study aimed to develop an optimized submerged18]. Although they are often the only recourse
culture medium for the sporulation of
for farmers, herbicides pose environmental
Colletotrichum graminicola.
concern because many do not rapidly degrade
and have been detected in surface waters [1, 11].
An alternative to chemical herbicides is the
Materials and Methods
use ofbioherbicides. Typically, when a fungal
Organism
bioherbicide is applied as inundative inoculum
with the same application technologies as
Single-spore isolates of C. graminico/a were collected at
chemical herbicides [19], it infects and kills the
locations in Arkansas and Texas. Stock cultures were
weed host within 1-2 weeks. After death of the
maintained on both potato-dextrose agar slants under mineral
oil and glycerol-skim milk at -BO°C. The inoculum conidia
weed host, the bioherbicide organism is naturally
were produced on Torula yeast agar (fA). The TA medium
reduced in numbers to background levels. In
contained: 15g. Torutein-IO (Provesta, Hutchinson, MN),
contrast to chemical herbicides, the use of fungal
ISg M-I 00 (Grain Processing Corporation, Muscatine, IA),
bioherbicides does not result in any toxic
l.Og. K2 HPO., O,5g. MgSO. x 711,0, and ISg. agar (Difco,
Detroit, MI) per liter of deionized water. For each
substance that could accumulate in the
experiment, 5 plates ofTA medium were inoculated and
environment or appear as surface or groundwater
incubated on a laboratory bench for 7 days at 22-24°C under
contaminants.
fluorescent lights (I : I, Gro-Lux: Cool White) adjusted to a
14-hour photoperiod. The TA medium plates were
4
produced oval conidia. The com syrup produced
falcate conidia, similar to the inoculum conidia,
so it was also selected for further study. Raw
data from this experiment are shown in appendix
aseptically scraped with sterile cotton swabs and conidia
were suspended in 10mL sterile deionized water.
Submerged culture
A.
Liquid culture experiments were conducted using 2S0-mL
Erlenmeyer flasks, each containing SOmL medium. An
appropriate volume of inoculum was introduced to the
autoclaved (IS-minute liquid cycle) media, resulting in an
initial spore concentration of2xl04 conidialmL. Cultures
were incubated at 22-24°C on a rotary shaker at 220rpm.
Flasks were manually shaken daily to remove aerial mycelial
growth on the flask wall. Conidia were counted with a
hemacytometer under the microscope after 6 days of culture
unless indicated otherwise.
T A medium in liquid form was evaluated
(100 and 33% v/v) along with dilutions of Pea
and filtered V8 (5, 10, 15,20, and 30% v/v).
This experiment was conducted in quadruplicate.
Greatest numbers were again observed in V8 and
Pea; TA and com syrup were dropped from
further consideration. Raw data and analysis of
this experiment are shown in appendix B.
The additive ingredients of Collego, a
medium developed for a different species of
Colletolrichum, were tested with this organism
using selected carbon sources, and CaC03 was
tested for it's efficacy as an additive. The
experiment was performed with 8 replicates per
treatment. It was noted during counting that
some of the treatments exhibited evidence of
conidial germination. There was no significant
difference between 30% filtered V8 and 30%
filtered V8 with Collego additives (Table 2).
These two treatments were significantly different
from all Pea treatments and all treatments with
the CaC03 additive (p<0.05) and were selected
for further testing. Raw data and analysis of this
experiment are shown in appendix C.
Thus far, aU experiments had been
conducted using filtered V8. Comparisons were
made between the effects of filtered versus
unfiltered V8 in both plain media and media
augmented with Collego additives at 15 and 30%
v/v concentrations. This experiment was
performed with 8 replicates per treatment.
Because of the earlier observation of conidial
germination on day 6 of culture, daily counts
were also performed to determine the optimal
harvesting period for this organism. Conidial
counts leveled out around day 5 for all
Media and experimental design
Crude carbon sources shown in Table I were evaluated for
sporulation ofthe fungus. Canned vegetables, including V8
juice unless indicated otherwise, were filtered through four
layers of grade 40 cheesecloth and the filtrate (brine) was
used in the experiment. Coffee was prepared on a Mr. Coffee
automatic-drip coffeemaker using the amount recommended
by the manufacturer. Tea was prepared by placing 3 tea bags
in I L boiling water for 20min. Dilutions of concentrated
media were made using deionized water. Later experiments
tested TA liquid medium, which was prepared similarly to
the solid medium with the omission of the agar. COLLEGO
medium was also evaluated, which was prepared from the
following ingredients: Sg. KN03, 2.Sg. K2HP04, 1.2Sg
MgS04 x 7H1 0, 109 sucrose (Difco, Detroit, MI),O.Olg
FeCh, 75mL V8 juice, and 42SmL deionized water. This
basic formulation contains 15% V8 v/v; alterations of the
formula were tested using 30% V8 v/v and 15% Pea v/v.
Thirty percent V8 and I S% Pea were also tested using 3gfL.
CaCo, as an additive. When adjustments of pH were
required, 50% NaOH and 1: I 0 and I: 100 dilutions were used
to increase pH and concentrated HCI and I: 10 dilution were
used to decrease pH.
Results
Nineteen carbon sources were evaluated for
conidia production and shape (Table J).
Sporulation was observed in all concentrations of
com syrup, V8, and Pea. Greatest numbers were
observed in V8 and Pea, but both media
atIon 0 fC o11etolrichurn graminicoia
T a ble 1: Cru de carb on sources eva uatedfior SPOlrul'
Type (concenDrcttion)
Canned vegetable brine
(5, 10, and 15% v/v)
i
!
Specific ingredient
V8 juice', sliced earrotsb, butter beansb, mustard greensb, golden hominy", whole kernel
golden com (no saltt, sliced Irish potatoesb, cut yamsb, leaf spinichb, pinto beansb, green
shelled blackeye peasb, cut beetsb, peas!, green beansb and cut okra'
Com (Karo dark)g, Molasses·
Syrups
(0.2,0.5, and 1% w/v)
Coffee and Tea
Guatemalan blend eoffeed , regular teae
(l0, 30, and 100% v/v)
•Campbell Soup Co. (Cambden, NJ)
"Alliance World Coffees (MunCIe, IN)
eLipton (Englewood Cliffs, NJ)
~arsh Supermarkets, LLC (Indianapolis, IN)
C Bruce Foods Corporation (New Iberia, LA)
'DelMonte Foods (San Francisco, CAl
gEPC Int. (Englewood Cliffs, NJ)
"B&G Foods (Roseland, NJ)
5
treatments (Figure I), so flasks were counted at
day 5 instead of day 6 from this point forward.
Conidia counts were significantly higher for
three ofthe unfiltered treatments (V8 30 and
15% v/vand 30% v/v V8 with Collego additives)
than for the fourth unfiltered treatment and all
filtered treatments (Table 3). Unfiltered plain
V8 was selected for use in the final optimization
experiment. Raw data and analysis of this
experiment are shown in appendix D.
Table 3: Colletotrichum graminicola conidia
production in crude filtered or unfiltered media
with or without Collego additives
Medium and Concentration
Table 2: Colletotrichum graminico/a conidia
production in crude liquid media with selected
additives
Medium
30% v/v filtered V8
with 3gIL CaC03
COLLEGO
. 15% v/v Pea with
• Collego additives
. 15% v/v Pea with
• 3g1LCaC0 3
15% v/v Pea
30% v/v filtered V8
with Collego
additives
30% v/v filtered V8
Mean loglll
conidialmL
3.38
c
3.52
3.62
bc
bc
3.72
bc
4.11
5.27
b
a
5.82
a
!
Mean LoglO
conidia/mL
3.79
d
15% v/v filtered V8 with
Collego additives
15% v/v unfiltered V8 with
Collego additives
30% v/v filtered V8 with
Collego additives
30% v/v unfiltered V8 with
Collego additives
15% v/v filtered V8
15% v/v unfiltered V8
30% v/v filtered V8
30% v/v unfiltered V8
5.51
bed
4.76
cd
5.94
abc
5.15
6.16
4.66
6.69
bed
ab
bcd
a
Figure 1: Co//etotrichum graminico/a conidia
production in selected liquid media
B
-g
-
7
- - -..........
--~----.-
.......
---
- _ .........._ - -
.!!
:26
c
o
(.) 5
o
"C""
C)
,34
3
+-------~------~------~------~------~----~
o
25
50
75
100
125
150
Hours post-innoculation
-- 30% Col Filt
30% VB Filt
-- 30% Col Untilt
-- 30% VB Unfilt
15% Col Filt
-+- 15% VB Filt
6
.t+-.
-
15% Col Untilt!
15% VB Untilt
Conductivity adjustments were made with KCI
ranging from 0% w/v to 0.02% w/v. Results of
this standardization are shown in appendix E.
The value was 0.97. Conidial concentration
results for V8 versus pH a V8 versus
conductivity are shown in Figure 2. Optimal
sporulation was predicted at 40.14% v/v V8 with
a pH of6.16 and a conductivity of28.03mS.
The predicted spore titer with these conditions
was 4.78x10 7 conidiaimL. Raw data and
analysis of this experiment are shown in
appendixF.
An orthogonal CCD in the JMP4 software
was utilized to optimize conidia production with
the following variables: unfiltered V8
concentrations, pH, and conductivity. Prior to
conducting the experiment, pH and conductivity
were standardized over a wide range of V8
concentrations pre- and post- autoclave in order
to predict starting pH and conductivity from
desired post-autoclave pH and conductivity.
r
Figure 2: Contour plots of pH and
conductivity interaction with % v/v V8 to affect
spore yield of Colletotrichum graminicola
Discussion
.. ~ dtr":--,-,-,~ '~~~~
....
"".
,,~
Fungi differ by species in optimal medium for
sporulation. As seen in this study, the Collego
medium, optimal for Colletotrichum
gioeosporioidies, is not the optimal medium for
C. graminicola. The orthogonal CCD predicted
a spore titer of 4.78x10 7 conidialmL with
40.14% v/v V8, a pH of6.16 and a conductivity
of28.03mS. This titer would be reached after 5
days of incubation at 22-24°C. It is possible that
other factors not considered in this study could
affect conidia production, and if considered, lead
to a greater spore titer. However, the titer
predicted by the orthogonal CCD model is
satisfactory and further extensive testing would
only be necessary if it were determined that a
higher initial titer would be required for
economical production.
It must be remembered that the overall goal
of this study is to develop a liquid-culture
medium that has the potential to be used in the
future to produce conidia for use as a
bioherbicide against johnsongrass. As noted
earlier, conidia shape in V8 is oval, not falcate as
is produced on solid media. This has been
observed by others [4,22] and the effects on
spore germination have been studied [4]. It was
found that oval conidia germinated similarly to
falcate conidia and had the additional advantage
of having less strict requirements of surface
hydrophobicity. Further tests would need to be
done to confirm that finding with this medium
and to determine the virulence of the conidia
produced by this medium on the johnsongrass
plant.
10910 conidi~L
\6.94
.,I
;
-
,"
BI
~"
!'
~,
.
,.'
.'L
jOt'
\7.14~.-,
"
-I
,
'f
'<1)
, ' 94'" ,
"
13
58
7
14.Mitchell J (1993) Potential of Colletotrichum
graminicola and Gloeocercospora sorghi as
biological herbicides for control of Johnson
grass. Plant Pathol I: 31-36
15.Mitchell J, Njalamimba-Bertsh M, Bradford
N, Birdsong J (2003) Development ofa
submerged-liquid sporulation medium for the
johnsongrass bioherbicide Gloeocercospora
sorghi. J Ind Microbiol Biotechnol 30:599605
16.Mitskas M Tsolis C, Eleftherohorinos I
(2003) Interference between com and
johnsongrass (Sorghum halepense) from seed
or rhizomes. Weed Sci 51:540-545
17.Mortensen K (1988) The potential of an
endemic fungus, Colletotrichum
gloeosporioides, for the biological control of
round-leaved mallow (Malva pusilla) and
velvetleaf (Abutilon theophrasti). Weed Sci
36:473-478
18.Orr J, Mitich L, Roncoroni E (1995)
Postemergence herbicide controls johsongrass,
other weeds in field com. Cal Agr 49:33-38
19.5mith R Jr (1982) Integration of microbial
herbicides with existing pest management
programs. In: Charudattan R, Walker L (eds)
Biological Control of weeds with plant
pathogens. Wiley, New York, pp 189-206
20.Templeton G (1982) Status of weed control
with plant pathogens. In: Charudattan R,
Walker L (eds) Biological control of weeds
with plant pathogens. Wiley, New York, pp
29-44
21. Templeton, G (1986) Mycoherbicide research
and the University of Arkansas--past, present,
and future. Weed Sci 34 (Suppl. 1): 35-37
22.Thomas, M Frederiksen, R (1995) Dynamisc
of oval and falcate conidium production of
Colletotrichum graminicola from sorghum
Mycologia 87:87-89
23.Williams C, Hayes R (1984) Johnsongrass
(Sorghum halepense) competition in soybeans
(Glycine max). Weed Sci 32:498-501
24.Winder R, Van Dyke C (1990) The
pathogenicity, virulence, and biocontrol
Potential of two Bipolaris species on
johnsongrass (Sorghum halepense) Weed Sci
38:89-94
25.Wood M, Murray D, Banks J, Verhalen L,
Westennan R, Anderson K (2002)
Johnsongrass (sorghum halepense) density
effects on cotton (Gossypium hirsutum)
harvest and economic value. Weed Tech
16:495-501
26. Yang Y -K, Kim S-O, Chung H-S, Lee Y-H
(2000) Use of Colletotrichum graminicola
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3.Bowers, R (1986) Commerialization of
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4.Chaky J, Anderson K, Moss M, Vaillancort L
(200 I) Surface hydrophobicity and surface
rigidity induce spore gennination in
Colletotrichum graminicola. Phytopathology
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Singh M (2001) Field evaluation of a fungal
pathogen mixture for the control of seven
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Four foliar fungi for controlling seedling
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of selected herbicides on C02 assimilation,
cholorophyll fluorescence, and stomatal
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Seasonal abundance of sorghum midge.
Cotton Newslett 17:8-12
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Honolulu, pp 54-61
II.Kalkhoff S, Lee K, Porter S, Terrio P,
Thunnan E (2003) Herbicides and herbicide
degradation products in upper Midwest
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9
Appendix A: Carbon Source Screening
Table 4: Conidiation in crude media and observations
Flask
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
i
i
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Flask Contents
Beets 5%
Beets 10%
Beets 15%
Blackeye Peas 5%
Blackeye Peas 10%
Blackeye Peas 15%
Hominy 5%
Hominy 10%
Hominy 15%
Potatoes 5%
Potatoes 10%
Potatoes 15%
Carrots 5%
Carrots 10%
Carrots 15%
Spinach 5%
Spinach 10%
Spinach 15%
Peas 5%
Peas 10%
Peas 15%
Butter Beans 5%
Butter Beans 10%
Butter Beans 15%
V85%
V81O%
V815%
Mustard Greens 5%
Mustard Greens 10%
Mustard Greens 15%
Okra 5%
Okra 10%
Okra 15%
Pinto Beans 5%
Pinto Beans 10%
Pinto Beans 15%
Com 5%
Conidia
Present
Observations
-
-
I
+
Very few, mix of oval and falcate
-
+
+
+
+
Mix of oval and falcate, few
Many, most oval
Many, most oval
Very few, all falcate
-
+
+
++
Many, most falcate, some oval
Many, mix of oval and falcate
Very many, mix of oval and falcate
-
+
+
oval conidia
Many, all oval
+
Many, all oval
+
Very few
10
•
Flask
i Number
38
39
! 40
41
42
.43
44
45
,46
147
48
149
50
, 51
,52
53
54
55
56
57
!
,
Flask Contents
Corn 10%
Corn 15%
Green Beans 5%
Green Beans 10%
Green Beans 15%
Yams 5%
Yams 10%
Yams 15%
Corn Syrup 0.2%
Corn Syrup 0.5%
Corn Syrup 1%
Molasses 0.2%
Molasses 0.5%
Molasses 1%
Coffee 10%
Coffee 30%
Coffee 100%
Tea 10%
Tea 30%
Tea 100%
Conidia
Present
Observations
+
+
oval conidia
Many ~ all oval
-
i
-
+
+
+
+
+
+
-
oval and falcate conidia
Little v(,:getative biomass
All falcate
Little vegetative biomass
Many, all oval
-
-
-
-
Table 5: Conidia counts in selected crude media
Flask
Number
46
47
! 48
i 49
! 50
25
! 26
27
• 31
132
33
19
20
21
!
Flask Contents
Corn Syrup 0.2%
Corn Syrup 0.5%
Corn Syrup 1%
Molasses 0.2%
Molasses 0.5%
V85%
V81O%
V815%
Okra 5%
Okra 10%
Okra 15%
Peas 5%
Peas 10%
Peas 15%
Log1o
Conidia/mL
4.19
4.03
4.43
4.29
4.75
4.49
4.89
5.36
4.21
3.22
4.55
4.88
5.05
5.30
!
i
11
Appendix B: V8, Corn Syrup, Pea, and TA
Table 6: Explanation of Abbreviations
Medium
Abbrev.
0.2
CI
Com Syrup (% w/v
0.5 I 1.0 I 1.5
C2 I C3 I C4
I
I
2.0
C5
5
PI
M
I1
I P2
P3
P4
30
P5
5
VI
I
l
V8 (% v/v)
10 I 15 I 20
V2 I V3 I V4
I
I
30
V5
Table 7: Raw data of conidia COWlts in selected concentrations ofV8, Corn Syrup, Pea,
andTA
Flask Number
1
2
3
4
5
cr
8
9
10
I
11
12
13
14
15
I 16
. 17
18
19
20
21
22
23
24
25
26
27
.28
29
30
i 31
32
Flask Conte
P1
P1
P1
P1
P2
P2
P2
P2
P3
P3
P3
P3
P4
P4
P4
P4
P5
P5
P5
P5
V1
V1
V1
V1
V2
V2
V2
V2
V3
V3
V3
V3
LO~lO
conidialmL
0
4.38
4.22
3.52
4.81
4.53
4.52
4.24
0
4.84
4.94
5.76
5.23
5.83
5.19
4.81
5.82
5.67
5.1
5.63
3.82
0
3.22
3.92
4.29
4.67
4.78
4.14
5.19
5.24
4.65
4.46
12
TA (% Vi
100 I 3
Tl I 1
Flask Number
33
34
35
36
37
38
39
40
41
42
• 43
144
i 45
46
i 47
r-----48
49
~
50
• 51
52
53
54
55
56
57
58
59
60
.61
62
63
.64
65
66
67
68
i
~
...
Flask Contents
Loglo conidialmL
V4
V4
V4
4.58
5.61
5.19
V4
4.3
V5
V5
V5
V5
C1
5.73
5.87
6.15
C1
C1
C1
C2
C2
C2
C2
C3
C3
C3
C3
C4
C4
C4
C4
C5
C5
C5
C5
T1
T1
T1
T1
T2
T2
T2
T2
i
!
5.89
4.22
4.14
4.22
4.19
4.19
4.05
3.82
4.25
4.18
4.11
3.75
3.89
4.25
3.59
4.12
i
3.89
4
3.52
3.35
3.74
0
0
0
3.65
0
3.35
3.05
0
13
Conoentration
Medium
Summary of
For categories in
No Selector
Group
Cl
C2
C3
C4
C5
PI
P2
P3
P4
P5
Tl
T2
VI
V2
V3
V4
V5
Count
Mean
Median
Std[)ev
Min
Max
Skewness
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4.1925
4.0775
3.9825
3.9625
3.6525
3.83
4.525
3.885
5.265
5.555
0.9125
1.6
2.74
4.47
4.885
4.92
5.91
4.205
4.12
4
4.005
3.63
3.87
4.525
4.89
5.21
5.65
0
1.525
3.52
4.48
4.92
4.885
5.88
0.0377492
0.191028
0.198221
0.289525
0.281351
2.05423
0.232737
2.62259
0.421545
0.314166
1.825
1.85158
1.85264
0.304083
0.389401
0.591326
0.175119
4.14
3.82
3.75
3.59
3.35
0
4.24
0
4.81
5.1
0
0
0
4.14
4.46
4.3
5.73
4.22
4.25
4.18
4.25
4
4.38
4.81
5.76
5.83
5.82
3.65
3.35
3.92
4.78
5.24
5.61
6.15
-0.737887
-0.581849
-0.177076
-0.386925
0.218205
-1.04588
-6.66134e-15
-1.06691
0.440355
-0.914134
1.1547
0.0130972
-1.06277
-0.0544547
-0.108418
0.131349
0.567641
There is no one transformation that will make all data sets normally distributed. Thus,
the Kruskal-Wallis test must be used. Data values were converted to ranks and ANOVA
was used to statistically evaluate the transformed data.
4
It
It
1
2
:
3
C
0
n
..
c
0
4.58
0
-0.75
n
0
0.75
"t
I
-0.75
0
n
It
5
V
5.4
"n
5.1
n
c
<:;
..
4.8
t
r
4.5
n
"ti
I
0
-0.75
n
0
I
-0.75
"n
0.75
I
0
0.75
nsoor4ts
,
p
6.1
4
:
:
C
C
0
i
I
nscores
nscores
4
4.6
r
4.20
i
I
4.8
n
t
"t
0
0
n
4.33
n
t
r
a
5.0
..
0
n
t
t
I
C
0
e
5.2
:
C
n
2
It
3
4.65
C
6.0
3
0
n
e
5.9
n
t
r
a
5.8
...'"
n
t
2
,
r
a
t
t
0
i
nsooreS
0
0.75
0
n
-0.75
0
nsoores
14
0.75
0
n
-0.75
0
nscores
0.75
P
2
C
4.80
4.65
0
C
3.75
4.5O
c
e
c
"n
t
1.25
r
r
i
0.75
5.8
C
5.6
I
-0.75
n
T
1
5.4
5.2
..c
1.50
t
O.75
-0.75
n
I
0
-8.75
n
C
2
C
3
0
n
C
4.0
e
n
t
0
I
i
0.15
0
3.9
r
a
C
4.0
"n
..
c
e
c
3.8
t
t
r
r
Q
t
n
iii
0
8.75
n
3.6
3.90
3.75
3.69
3.45
t
-0.15
iii
n:s:cores
0.75
0.75
4.0
3.9
3.8
0
n
I
I
-0.75
0
nsCOf"b
15
I
-iii. 75
nsconts
n
CI
0
i
I
-8.75
n
C
5
n
I
8
4.1
t
nscores:
4.2
8.75
t
a
t
4.14
0
C
Moores
n
-0.75
nscores
n
i
0
8
n
c
r
C
0.75
n
.
4.16
-8.75
t
0
4.1
0
4.18
Q
C
4
0
4.2
C
0
1.:5Iil
I
I
0.75
4.28
n
..c
nscor••
e
t
2.25
r
4.22
0
n
t
C
a
t
nseores
n
c
3.00
0
I
8.75
T
2
n
8
t
0
I
O.75
n
r
I
0
8
nsOOres
2.25
a
i
C
n
n
t
C
1
8.75
3.88
0
t
r
0
n
n
a
I
-0.75
I
C
0
e
I
8
ns:cOr4ts
n
c
5.00
t
8
0
nsoores:
p
5
5.25
a
j
I
0
-0.75
5.:5Iil
n
a
t
t
C
.
2.50
t
4.35
5.75
0
n
a
P
4
n
n
e
n
t
5.88
0
n
c
P
3
I
0.75
5.00
B
~~BB~
3.75
C
0
n
c
e
n
t
r
a
t
2.50
i
0
n
1.25
Cl
C2
C3
C4
C5
PI
P2
P3
P4
P5
Tl
T2
VI
V2
Mecliut"l'l
Analysis of Variance For
No Selector
Source
df
Sums of Squares
Mean Square
F-ratio
10
51
07
80937
21290
4851
20147
80937
1331
95.1176
850.91
13.993
Canst
Mdm
Error
Total
Rank: Concentration
16
Prob
~
0.0001
0.0001
V3
V4
V5
Summary of
For categories in
No Seleotor
Group
C1
C2
C3
C4
C5
P1
P2
P3
P4
P5
T1
T2
V1
V2
V3
V4
V5
SE
SE
SE
Rank: Conoentration
MediuM
Count
M.an
Median
StdD_
Min
Max
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
32
28.375
23.875
25.25
16.5
23.25
43.125
42.625
56.625
59.5
7.375
7.375
14.25
40.625
50.25
49.75
65.75
32.75
28.25
23.75
24.25
15.25
23.75
43.5
51.5
56
60.5
4.5
6.75
14.75
43
50.5
50
66.S
2.61406
7.81425
5.2341
9.52628
5.49242
17.1002
5.54339
25.9916
6.42099
4.65475
5.75
3.47311
8.5098
9.0312
7.5
8.77021
2.62996
28.5
19.5
18
15
11.5
4.5
36
4.5
49.5
53
4.5
4.5
4.5
28.5
42
40
62
34
37.5
30
37.5
24
41
49.5
63
65
64
16
11.5
23
48
58
59
68
"((k(N+ 1))/12)
"((17(69))/12)
9.89
MSD = Qa=o.05, k=l7, df-= ro (SE)
MSD 4.792 (9.89)
MSD =47.4
Table 8: Media and their mean ranks, with results of the Kruskal-Wallis test
Medium
Tl
T2
VI
C5
. PI
C3
! C4
C2
C]
V2
P3
P2
V4
• V3
i P4
i P5
i V5
Mean Rank
7.375
7.375
14.25
16.5
23.25
23.875
25.25
28.375
32
40.625
42.625
43.125
5
50.25
56.625
59.5
65.75
c
c
be
be
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
ab
ab
a
I
17
Table 9: Media and condia concentrations with results of the Kruskal-Wallis test
Medium
Com Syrup (%w/v)
0.2
0.5
1.0
1.5
I
2.0
i Pea (% v/v)
5
10
15
20
30
V8 (%v/v)
5
10
15
20
30
TA (% v/v)
33
100
Mean
IOglO
conidia/mL
4.19
4.08
3.98
3.96
3.65
abc
abc
abc
abc
bc
3.03
4.53
3.89
5.27
5.56
abc
abc
abc
ab
ab
2.74
4.47
4.89
4.92
5.91
be
abc
abc
abc
a
0.91
1.60
e
e
18
Appendix C: Collego and CaC03
Table 10: Explanation of abbreviations
Medium and Concentration
! 30% v/v filtered V8
• 30% v/v filtered V8 with Collego additives
30% v/v filtered V8 with 3g/L CaC03
15% v/v Pea
15% v/v Pea with Collego additives
15% v/v Pea with 3g/L CaC03
Collego as originally formulated
Abbreviation
V8
"(1 "'''llego
V8-Ca
P
P-collego
P-Ca
COLLEGO
!
Table 11: Conductivity and pH ofV8 and Pea media with and without Collego additives
orC aC03
Medium
i
V8
V8-collego
V8-Ca
P
P-collego
P-Ca
COLLEGO
Conductivity
(mS) preautoclave
5.33
18.38
5.38
0.699
14.70
0.730
16.69
Conductivity
(mS) postautoclave
5.57
19.24
5.66
0.731
15.19
0.763
17.12
pH preautoclave
pH postautoclave
4.13
6.58
6.25
6.85
7.49
7.47
7.01
4.28
6.41
5.96
6.65
7.03
7.41
6.52
Table 12: Raw data of conidia counts in V8 and Pea with and without Collego additives
orCaC03
Flask Number Flask Contents
Log1o
conidia/mL
1
5.812913
V8
2
6.068186
V8
3
V8
5.740363
V8
6.089905
.4
·5
V8
5.716003
6
V8
5.414973
1
•7
·8
9
10
11
12
13
V8
V8
V8-collego
V8-collego
V8-collego
V8-collego
V8-collego
5.585461
6.164353
5.278754
5.290035
5.389166
5.243038
5.09691
19
Flask Number
Flask Contents
LoglO
conidialmL
I
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
V8-collego
V8-collego
V8-collego
V8-Ca
V8-Ca
V8-Ca
V8-Ca
V8-Ca
V8-Ca
V8-Ca
V8-Ca
COLLEGO
COLLEGO
COLLEGO
COLLEGO
COLLEGO
COLLEGO
COLLEGO
COLLEGO
P
P
P
P
P
P
P
P
P-collego
P-collego
P-collego
P-collego
P-collego
P-collego
P-collego
P-collego
5.361728
5.585461
4.954243
2.745075
2.745075
3.222716
i
36
37
38
39
40
41
i 42
43
44
45
46
47
48
49
i 50
51
52
53
54
55
56
!
!
P-Ca
P-Ca
P-Ca
P-Ca
P-Ca
P-Ca
P-Ca
P-Ca
3.045323
3.647383
4.954243
3.647383
3.045323
3.522444
3.045323
3.745075
3.045323
3.444045
3.69897
3.920645
3.745075
4.58995
3.69897
4.235528
4.222716
3.69897
4.557507
3.58995
4.324282
3.920645
3.444045
3.69897
13.69897
3.522444
2.745075
3.920645
4.025306
3.58995
3.647383
3.786041
3.948902
3.647383
3.974972
3.444045
3.69897
20
I
i
i
i
Summary of
For oategories in
No Seleotor
Group
COLLEGO
P
P-Ca
P-collego
va
va-Ca
Va-collego
Log Conidia/mL
Medium
a
Mean
3.52085
4.11473
3.71721
3.6221'31
5.8241'32
3.38157
5.27492
Median
3.511'371
4.22912
3.67318
3.59897
5.77664
3.1341'32
5.28439
StdOey
1'3.327386
1'3.398776
1'3.170586
1'3.41'37687
0.264348
1'3.724589
0.191'3039
Min
3.1'34532
3.58995
3.4441'34
2.7451'37
5.41497
2.74507
4.95424
COLLEGO
P
P-Ca
P-oollego
va
Count
a
8
8
a
8
8
Max
3.921'365
4.58995
3.97497
4.1'32531
6.16435
4.95424
5.58545
6
5
L
o
9
C
o
n
i
d
a
I
4
m
L
3
Medium
21
va-Co
va-oollego
c
4.50
0
L
L
3.8
P
E
G
0
L
3.0
4.23
0
9
L
C
0
9
n
i
C
d
0
n
i
4.90
0
3.4
3.2
3.75
CI
I
d
rn
i
CI
I
-9.75
I
rn
L
I
9
L
I
9.75
I
0
I
-0.75
nscor • •
I
0.75
nscores
p
3.875
I
I
.
C
CI
L
0
0
0
L
3.3
0
9
3.625
n
i
d
i
3.6
9
3.759
<;I
C
3.<;1
0
0
P
C
3.0
0
n
3.599
i
Q
d
I
rn
I
-9.75
L
I
I
13
0.75
Q
I
I
I
I
-0.75
0
0.75
rn
L
nsoc,..••
n.cor••
V
8
V
8
Q
L
L
0
'"
d
i
4.9
0
9
5.8
C
n
i
4.5
c
6.0
C
3.5
0
i
5.<:1
d
3.0
Q
a
/
.......
/
rn
L
-0.75
0
0.75
L
5.55
"I
0
..
I
:5.48
9
0
L
5.25
"9
C
5.10
"n
i
d
i
I
CI
I
m
-8.75
I
I
13
9.75
nsoor• •
nsoor • •
V
8
I
-9.75
0
I
0.75
L
22
2
S21argest I S smallest
(0.72)2 I (0.18)2
Fmax 16
Fcritical with k=7, df=7 = 11.8
F max
Fmax
There is evidence of skew in the data sets and the variances are not all equal. However,
since sample sizes are the same, ANOVA can still be used with these rather modest
deviations from normality and equal variance.
Analysis of Variance For
No S.leoctor
Source
Const
Mdm
Error
Total
HSD
HSD
HSD
df
1
6
49
55
Log Conidia/mL
Sums of Squares
991.561
43.9835
7.67083
51.1:i544
I"IeGIn Square
991.561
7.33059
0.156548
F-ratio
6333.9
46.827
Prob
S 0.0001
S 0.0001
Q (1=0.05, k=7, df=49 v'(MSE/ni)
4.389 v'(0.156548/8)
0.614
Table 13: Media and condia concentrations with results of the ANOVA test
Medium
30% v/v filtered V8 with
3gIL CaC03
COLLEGO
15% v/v Pea with Collego
additives
15% v/v Pea with 3gIL
CaC03
15% v/v Pea
30% v/v filtered V8 with
Collego additives
30% v/v filtered V8
Mean logllJ
conidialmL
3.38
e
3.52
3.62
be
be
3.72
be
4.11
5.27
b
a
5.82
a
23
Appendix D: Filtered versus Unfiltered V8
Table 14: Explanation of Abbreviations
Medium and Concentration
15% v/v filtered V8 with Collego additives
• 15% v/v unfiltered V8 with Collego additives
• 30% v/v filtered V8 with Collego additives
30% v/v unfiltered V8 with Collego additives
15% v/v filtered V8
15% v/v unfiltered V8
30% v/v filtered V8
30% v/v unfiltered V8
Abbreviation
Co115F
Co115U
Col30F
Col30U
V 15F
V 15U
V30F
V30U
I
i
Table 15: Conductivity and pH of selected concentrations of filtered and unfiltered V8
with and without Collego additives
Medium
Col15F
• Col15U
Col30F
Col30U
V 15F
• V 15U
V 30F
V30U
Conductivity
(mS) preautoclave
11.95
11.85
13.19
13.05
1.93
1.96
3.97
3.89
Conductivity
(mS) postautoclave
16.53
16.50
18.63
18.38
2.76
2.73
5.67
5.56
pH preautoclave
pH postautoclave
6.98
7.01
6.51
6.55
4.27
4.20
4.39
4.23
6.39
6.56
6.28
6.33
3.96
3.48
3.95
3.93
i
Table 16: Raw data of conidia counts at selected times post-innoculation
Hours post-innoculation
Medium
Col30F
Col30U
Col15F
Col15U
V30F
V30U
V15F
V15U
43
4.346352974
4.46834733
4.434568904
4.84509804
4.086359831
4.460897843
4.357934847
4.086359831
I
78
102
4.801404
6.318063
4.620136
5.607455
4.206826
6.763428
4.83123
6.403121
5.217484
6.201397
4.40824
5.667453
4.85187
6.829304
5.875061
6.537819
24
136
5.45 i
6.03
4.24 i
5.71 •
4.86 •
6.67
5.92
6.63
Table 17: Raw data of conidia counts in selected concentrations of filtered and
unfilteredV8 with and without Collego additives
i
Flask Number
Flask Contents
Log1o conidialmL
1
2
3
4
5
6
7
8
9
10
Col30F
Col30F
Col30F
Col30F
Col30F
Col30F
Col30F
Col30F
Col30U
Col30U
Col30U
Col30U
Col30U
Col30U
Col30U
Col30U
Col15F
Col15F
Col15F
Col15F
Col15F
Col15F
Col15F
Col15F
Col15U
Col15U
Col15U
Col15U
Col15U
Col15U
Col15U
Col15U
V30F
V30F
V30F
V30F
V30F
V30F
V30F
V30F
4.72
4.55
4.64
4.73
4.68
4.75
4.56
5.45
5.90
5.85
5.90
5.87
6.30
6.00
5.64
6.03
3.05
3.65
4.19
3.22
4.03
3.95
4.03
4.24
5.27
5.35
5.42
5.98
5.72
5.08
5.51
5.71
3.35
6.15
4.60
3.70
4.18
4.37
6.10
4.86
11
i
12
.13
14
15
16
17
118
19
, 20
21
22
23
24
25
26
, 27
28
29
30
31
32
33
, 34
! 35
36
37
38
39
40
!
I
I
I
i
I
!
!
i
25
Flask Number
41
42
43
44
45
46
47
48
49
• 50
51
• 52
53
54
55
56
·57
58
59
160
61
62
63
64
i
Summary of
For categories in
No Selector
&-oup
Col15F
Col15U
Col38F
CoI38U
V15F
V15U
V38F
V38U
Flask Contents
L0210 conidialmL
V30U
V30U
V30U
V30U
V30U
V30U
V30U
V30U
V15F
V15F
V15F
V15F
V15F
V15F
V15F
V15F
V15U
V15U
V15U
V15U
V15U
V15U
V15U
V15U
6.51
6.58
6.72
6.82
6.72
6.69
6.80
6.67
5.66
5.37
5.63
5.47
4.61
5.34
3.22
5.92
6.37
6.37
5.95
6.22
6.09
5.66
6.06
6.53
!
I
i
Log.8 conidia/mL
Hedium
Count
H_
Median
StdDev
Hin
Max
8
8
8
8
8
8
8
8
3.79295
5.50678
4.76067
5.93745
5.15281
6.15714
4.66198
6.68841
3.9871
5.46756
4.70881
5.99173
5.42044
6.15192
4.48143
6.79429
8.446193
8.29001
8.287358
8.186876
8.868885
0.277716
1.9228
8.193273
3.84532
5.07918
4.55023
5.63849
3.22272
5.66276
3.34635
6.5892
4.23553
5.98453
5.44716
6.29667
5.91645
6.53148
6.15229
6.81823
26
6
L
*
0
9
1
0
5
c
0
El
n
i
d
a
I
m
L
4
Col15F
Col15U
Col30F
V15F
Col30U
M&dium
27
V15U
V30F
V30U
c
0
I
1
5
<..:
4.25
0
4.00
5
U
3.75
0
3.5e
9
I
0
I
I
F
L
0
9
1
e
L
n
i
d
0
3.25
n
-e.75
m
0
i
Q
e.75
I
m
nscores
....L
0
I
0
0
nscores
I
3
0
U
5.25
L
6.15
L
0
9
1
0
5.00
c
4.75
0
6.00
9
I
0
5.85
c
0
0
n
n
i
d
i
d
i
I
-0.75
I
C
m
5.2
d
I
I
GI
5.4
i
GI
3
0
F
5.6
c
c
0
5.8
-0.75
nscor_
I
0
0.75
Q
I
m
L
28
5.70
I
-0.75
nscor_
0
0.75
V
V
1
5
1
5
U
:
F
5.25
L
L
0
0
9
9
1
0
0
0
n
i
d
i
3.75
I
m
-0.75
L
V
o
0.75
nscores
0.75
ns:cor&s:
V
5.00
3
0
U
:
5.750
L
5.25
0
0
9
9
5.575
5.500
1
0
o
-0.75
L
F
L
5.8
a
I
m
a
3
0
5.0
c
c
n
i
d
5.2
1
0
4.50
5.4
1
0
4.50
c
c
0
0
n
i
d
n
i
d
i
3.75
a
I
m
a
I
m
-0.75
L
o
5.525
0.75
-0.75
L
nscores
o
0.75
ns:cores
The 30% V8 with collego additives treatment has a large positive outlier that is not
eliminated by data transformation. With such an extreme violation of normality, the
Kruskal-Wallis test must be used. Data values were converted to ranks and ANOVA was
used to statistically evaluate the transformed data.
Analysis of Variance For
No Selector
Source
Const
Mdm
Error
Total
df
1
7
56
63
Sums of Squares
67600
17032.2
4805.81
21839
Summary of
For categor i es in
No Selector
Group
Col15F
Col15U
Col38F
Col38U
V15F
V15U
V38F
V38U
Rank: Log 18 conidia/mL
Count
8
8
8
8
8
8
8
8
Mean Square
67600
2433.17
85.8359
F-ratio
787.55
28.347
Rank: Log 18 conidia/mL
Medium
Mean
6.9375
32.125
19.875
42.75
25.9375
49.375
21.625
60.375
Median
7.75
30.5
19.5
41.5
29.5
50.5
14.5
60.5
StdDev
3.88622
7.33753
4.94072
5.80025
12.3879
6.84392
18.7688
2.55927
29
Min
1
24
14
34
2.5
36
4
55
Max
12
45
30
53
43
57
51
64
Prob
0.0001
~ 0.0001
SE "«k(N+1)112)
SE = "«8(65»/12)
SE 6.58
MSD = Qa=O.05, k=8, df-= 00 (SE)
MSD 4.286 (6.58)
MSD=28.21
Table 18: Media and their mean ranks, with results of the Kruskal-Wallis test
Treatment
Col15F
• Co130F
V30F
V l5F
Col15U
Co130U
V 15U
V30U
Mean Rank
6.94
d
19.88
cd
21.63
bed
26.94
bcd
32.13
bed
42.75
abc
ab
49.38
60.38
a
I
I
Table 20: Mean conidia concentration for filtered and unfiltered V8, with or without
Collego additives, and results of the Kruskal-Wallis test
Medium and Concentration
15% v/v filtered V8 with
CoUego additives
15% v/v unfiltered V8 with
Collego additives
I 30% v/v filtered V8 with
Collego additives
! 30% v/v unfiltered V8 with
• Collego additives
15% v/v filtered V8
15% v/v unfiltered V8
30% v/v filtered V8
30% v/v unfiltered V8
Mean Log1o
conidialmL
3.79
d
5.51
bed
4.76
cd
5.94
abc
5.15
6.16
4.66
6.69
bed
ab
bed
a
I
30
Appendix E: pH and Conductivity Calibrations
Table 21: Results of pH and conductivity calibrations for selected concentrations ofV8
0/0
Flask
Number V8
i
1
2
3
4
5
6
7
8
9
10
.11
12
.13
!
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
10
10
10
10
10
10
10
10
10
10
15
15
15
15
15
15
15
15
15
15
30
30
30
30
30
30
30
30
30
30
50
50
50
50
50
50
pH post- Grams Conductivity
pH prepreautoclave autoclave KCI
added autoclave(mS)
4.33
4.18
--2.83
2.77
--2.03
11.99
--6.94
8.76
--11.19
7.41
--1.87
0
-0.5
9.20
--15.99
I
--1.5
tI2.7
--2
129.3
--4.15
4.22
--2.63
2.63
--2.00
2.01
--6.14
6.69
--7.23
9.86
--0
2.54
--0.5
10.36
--I
15.98
--23.4
1.5
--29.4
2
--4.11
4.09
--3.08
3.09
--2.01
2.01
--8.86
6.65
--9.98
7.05
--0
4.74
--0.5
11.74
---I
18.35
-1.5
25.1
--2
31.0
---4.03
4.02
-2.26
2.30
--3.08
3.22
--7.78
6.29
--9.07
6.84
--0
7.38
---
--
31
Conductivity
postautoclave( mS)
---
I
i
--
--
--
1.96
10.29
17.86
25.6
32.6
-----2.87
11.52
18.00
26.2
32.8
I
!
I
---
---
-5.32
12.97
20.5
27.9
34.5
--
--
----
8.15
i
I
%
Flask
Number V8
37
38
139
40
41
42
43
44
45
46
47
~
50
pH prepH post- Grams
autoclave autoclave KCI
added
0.5
--
IT
50
50
75
75
75
75
75
75
75
75
75
75
--
--
---
3.94
2.94
2.27
6.60
9.42
3.91
2.96
2.22
5.84
7.18
--
--
--
---
---
--
----
I
1.5
2
Conductivity
preautoclave(mS)
14.17
21.0
26.6
33.0
--
I --
---
---
--0
0.5
I
1.5
12
32
---
10.30
16.57
22.8
28.7
34.5
Conductivity
postautoclave(mS)
15.80
22.9
29.5
36.1
-----
-11.18
18.1
25.0
32.2
37.5
Figure 3: Plotted pre-post autoclave differences vs. post-autoclave readings
pH difference pre-post autoclave vs. pH post-autoclave for
10%V8
4
•
3.5
~
y = 0.2469x2
1.7537x + 2.7225
R2 = 0.9392
3
ftS
~
-g
2.5
ftS
2
Q.
e
I
1.5
Q.
CD
CJ
C
e
:s
~
J:
Q.
0.5
o
6
2
-0.5
-1
pH post-autoclave
33
7
8
pH difference pre-post autoclave vs. pH post-autoclave for
15%V8
3
y = 0.2125x2 -1.5384x + 2.4157
R2 = 0.9346
2.5
-~'f----~--~
6
-0.5·
-1
pH post-autoclave
34
7
•
...........-----,
8
pH difference pre-post autoclave vs. pH post-autoclave for
30%V8
3.5
=
3
Y 0.1837~ -1.108x + 1.539.
R2 0.9959
(»
>
CIS
U 2.5
=
.s
::::I
CIS
( Ii
2
0
.
Q.
I
(»
1.5
Q.
(»
CJ
c
!
:E
":J:
0.5
,..-
Q.
0
..
-0.5
~-I-
4
2
--.. -
-~.---"~-
5
.. -"-
pH post-autoclave
35
6
7
$
pH difference pre-post autoclave vs. pH post-autoclave for
50%V8
2.5
y=0.1699x2-1.064x+ 1.5197
R2 = 0.9988
2
2
5
-0.5
pH post-autoclave
36
6
7
pH difference pre-post autoclave vs. pH post-autoclave for
75%V8
2.5 ..
2
y= 0.1555x -1.0415x + 1.6535.
R2 = 0.993
CD
>
ca
U
S
;
2
1.5
u;
oCo
eCo
I
CD
()
c
e
:E"C
J:
Co
0.5
o
--~·~~~···· ---~~~-................~-2
3
4
5
- - - - - - T - - .............
-0.5
pH post-autoclave
37
--~------~---6
7
..
conductivity difference pre-post autoclave vs. conductivity
post-autoclave for 10% va
4
3.5
G)
>
ftI
..
..
..•
(j
0
:::1
ftI
en
3
2.5
0
Q.
G)
2
Q.
G)
(J
c 1.5
!
~
"0
J:
Q.
0.5
0
0
5
10
15
20
25
conductivity post-autoclave (mS)
38
30
35
conductivity difference pre-post autoclave vs. conductivity
post-autoclave for 15% va
4
CD
>
3.5
.!!
u
.s
3
....C'O
8
Co
2.5
::l
I
...Co
CD
2
CD
u 1.5
c
...
~
=s
CD
::J:
Co
1
0.5
o +--------,------,r-----~------~------~------_,------~
o
5
10
15
20
25
30
35
conductivity post-autoclave (mS)
39
conductivity difference pre-post autoclave vs. conductivity
post-autoclave for 30% va
4
GI
>
I'G
U
3.5
--..
0
3
:=
I'G
2.5
0
CI
...GIC-
2
GI
Q
c: 1.5
...
<D
~
"tJ
J:
C-
0.5
0,
0
5
10
15
20
25
conductivity post-autoclave (mS)
40
30
35
40
conductivity difference pre-post autoclave vs. conductivity
post-autoclave for 50% va
3.5
Q)
3
>
CIS
-U
0
2.5
:::l
CIS
I /)
0
Q.
2
I
...Q.
Q)
Q)
u
1.5
cQ)
...
!E
'0
::I:
•
Q.
0.5
0
0
5
10
15
20
25
conductivity post-autoclave (mS)
41
30
35
40
conductivity difference pre-post autoclave vs. conductivity
post-autoclave for 75% V8
4
3.5
uE
!
o
y
=0.0946x - 0.1243
•
R2 = 0.8847
•
3
:::::s
(/)
2.5
o
Co
!
2
Co
CD
U
ec 1.5
~
:s
J:
Co
0.5
O'~-----r-----'------'------r------'-----'-----~----~
o
5
10
15
20
25
conductivity post-autoclave (mS)
42
30
35
40
Appendix F: V8 Concentration, pH, and Conductivity
Table 22: Adjustments in pH to achieve desired post-autoclave pH
Desired pH
3
6.5
3
6.5
4.75
4.75
2.5
7
4.75
V8 concentration
18%
·18%
53%
53%
13%
58%
35.5%
135.5%
35 .5%
1
Adjusted pH pre-autoclave
2.713
7.9
2.86
8.28
4.65
5.95
2.42
9.78
5.17
I
I
I
I
i
I
Table 23: Adjustments in conductivity to achieve desired post-autoclave conductivity
I
V8 concentration
18%
18%
53%
53%
13%
58%
·35.5%
35.5%
35.5%
Desired Conductivity (mS)
12
38
12
38
25
25
25
8.3
41.7
Adjusted Conductivity preautoclave (mS)
10.71
34
10.83
34.62
22.36
22.73
22.47
7.45
37.48
I
I
i
Table 24: Target pH and conductivity, actual pH and conductivity, conidia
concentration, and media color
Flask
Number
.1
12
3
4
5
6
7
.8
9
110
V8%
18%
18%
18%
18%
53%
53%
53%
53%
13%
58%
Target
Actual
pH
Conductivity pH
Conductivity LOgIO
conidialmL
3
12
3.4
11.40
3.70
3
38
3.5
36.1
3.70
]2
6.5
6.6
11.40
6.24
6.5
36.0
38
6.8
6.71
12
3
3.4
11.52
4.00
3
3.6
4.07
38
36.5
6.5
12
6.8
11.35
6.59
6.5
38
7.0
36.7
7.15
4.75
25
4.9
24.1
6.53
4.75
25
6.0
23.9
7.20
43
Medium I
i
color
Tan
i
L orange
D green i
Brown I
D green
Orange
D green
Brown
Olive~r.
Brown
I Flask
V8%
Number
·11
12
13
i 14
• 15
16
17
18
19
20
21
22
23
24
25
26
127
128
29
30
31
32
33
34
35
36
37
i 38
i 39
40
41
142
43
44
45
146
47
48
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
18%
18°
18%
18%
53%
53%
53%
53%
13%
58%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
18%
18%
18%
18%
53%
53%
53%
53%
13%
58%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
Target
Actual
pH
Conductivity pH
Conductivity Flask
Number
24.0
2.5
25
3.1
4.12
25
7.4
23.7
7.0
6.79
4.75
8.27
5.1
7.87
6.95
4.75
41.73
5.6
39.5
7.15
25
4.75
5.5
23.7
7.13
4.75
25
23.7
5.5
7.19
12
11.40
3
3.4
2.74
36.1
3
38
3.5
3.52
6.5
12
6.6
11.40
6.20
38
6.8
6.5
36.0
6.90
12
3
3.4
11.52
3.59
38
36.5
3
3.6
4.09
12
6.8
6.5
11.35
6.76
6.5
38
7.0
36.7
7.27
4.9
24.1
4.75
25
6.54
23.9
6.0
7.07
4.75
25
2.5
25
3.1
24.0
4.24
25
7.4
23.7
6.81
7.0
4.75
8.27
5.1
7.87
6.54
41.73
5.6
39.5
7.16
4.75
23.7
7.28
4.75
25
5.5
5.5
23.7
7.17
4.75
25
3.4
11.40
3.35
3
12
36.1
38
3.5
3.35
3
6.5
12
6.6
11.40
6.26
36.0
6.83
38
6.8
6.5
3
12
3.4
11.52
3.59
3.6
3
38
36.5
3.86
12
6.8
6.5
11.35
6.64
6.5
38
7.0
36.7
7.24
4.75
25
4.9
24.1
6.49
4.75
25
6.0
23.9
7.15
24.0
2.5
25
3.1
4.09
7.0
25
7.4
23.7
6.60
4.75
8.27
5.1
7.87
6.82
4.75
41.73
5.6
39.5
7.16
7.10
4.75
25
5.5
23.7
23.7
4.75
25
5.5
7.22
44
!V8% !
Orange
Brown
D green
Brown
Brown
Brown I
Tan
I
L orange i
Olive gr. I
Brown I
D green I
Orange
D green
Brown
Olive gr.
brown
. Orange
Brown
D green
Brown
Brown I
Brown i
Tan
I
L orange I
Olive gr.
Brown !
D green
Orange
D green
Brown
Olive gr. :
Brown
• Orange
Olive gr. i
D green
Brown J
Brown
Brown
Figure 4: Orthagonal CCD analysis of data in table 24
IResponse log10 conldia/mL
IWhole Model
IActual by, Predicted Plot
iii
•
7-
1.1
I
-..! •
• JI
::::I
•
«13 6-
..J
.E
.!!! 5:2
t:
~ 4-
..
".-
~ 33
I
I
4
5
7
6
log10 conidialmL Predicted P<.OOO1
RSq=0.97 RMSE=0.268
1Summary of Fit
RSquare
RSquare Adj
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.974558
0.968532
0.268049
5.892708
48
[ Analysis of Variance
Source
Model
Error
C. Total
OF
9
38
47
Sum of Squares
104.58464
2.73031
107.31495
Mean Square
11.6205
0.0719
F Ratio
161.7324
Prob> F
<.0001
ILack Of Fit
Source
Lack Of Fit
Pure Error
Total Error
OF
5
33
38
Sum of Squares
1.8510273
0.8792833
2.7303107
Mean Square
0.370205
0.026645
IParameter Estimates
Term
Intercept
V8(18,53)&RS
pH(3,6.5)&RS
conductivity (mS)(12,38)&RS
V8( 18.53)*pH(3.6.5)
V8(18.53)*conductlvlty (mS)(12,38)
pH(3.6.5}*condUclivity (mS)(12.38)
V8(18.53)"V8( 18,53)
pH(3,6.5)*pH(3,6.5)
conductivity (mS)(12,38)"conductivity (mS)( 12.38)
45
F Ratio
13.8940
Prob:> F
<.0001
MaxRSq
0.9918
Estimate
6.7904008
0.2318244
2.1105324
0.112163
-0.013438
-0.000415
0.0774555
-0.416562
-1.320171
-0.373732
Std Error
0.090169
0.048815
0.058234
0.052671
0.057443
0.057178
0.060033
0.066175
0.073988
0.073176
t Ratio
75.31
4.75
36.24
2.13
-0.23
-0.01
1.29
-6.29
-17.84
-5.11
Prob>ltl
<.0001
<.0001
<.0001
0.0398
0.8163
0.9942
0.2048
<.0001
<.0001
<.0001
IResponse lag10 conidia/mL
IWhole Model
IEffect Tests
Source
V8(18,53)&RS
pH(3,6.5)&RS
conductivity (mS)(12,38)&RS
V8(18,53tpH(3,6.5)
V8(18,53tconductivity (mS)(12 ,38)
pH(3,6.5tconductivity (mS)(12,38)
V8(18,53)"V8(18,53)
pH(3,6.5)*pH(3,6.5)
conductivity (mS)(12,38)*conductlvity (mS)(12,38)
I Residual by Predicted Plot
Nparm
1
1
1
1
1
1
1
1
1
OF
1
1
1
1
1
1
Sum of Squares
F Ratio
1.620470
22.5534
94.376469 1313.516
0.325819
4.5347
0.003932
0.0547
0.000004
0.0001
0.119605
1.6646
2.847090
39.6253
22.875255 318.3739
1.874161
26.0843
0.75...,.----------------,
iii
.g
0.50-
a::
0.25-
.~
... . .. .
-. .,-
...J
E
-
Cii 0.00'§
c:
8-0 .25 o
j-0.50-
,-r--r-i,
-0. 75-+--.-,~"'-...,...---r---'r--.-
3
4
5
6
7
log10 conidia/mL Predicted
I Response Surface
Coef
V8(18,53)
pH(3,6.5)
conductivity (mS)(12,38)
V8(18,53)
-0.416562
pH(3.6.5) conductivity (mS)(12,38) log1 0 conidialmL
-0.013438
-0.000415
0.2318244
0.0774555
2.1105324
-1.320171
-0.373732
0.112163
ISolution
Variable
Critical Value
V8(18,53)
40.140324
pH(3,6.5)
6.1564622
conductivity (mS)(12.38)
28.033043
Solution is a Maximum
Predicted Value at Solution 7.6835364
46
Prob > F
<.0001
<.0001
0.0398
0.8163
0.9942
0.2048
<.0001
<.0001
<.0001
Figure 5: Contour Profiler
i Response log10 conidialmL
[ Co ntour Profiler
Current X
Horiz Vert
®
0
o
0
Factor
V8(18,53)
I
pH(3,6 .S)
I
€I
conductivity (mS)(12,38) I
Response
log 10 conidialmL 1l
......_ _ _ _ _ _ _1J1
o
I
I
I
I
I
I
Contour
35.5
5.25875
23.8531 25
Current Y
7281 7 2nS984 1
"'--
Lo
It
HI limit
Lo Limn
Hi Limit
---.Ir------,I
I Response log10 conldiaJmL
I Contour Profller
Horiz Vert
o
o
o
Fador
V8(18,53)
0
0
0
I
pH(3,B.S)
I
I
I
I
I
conductivity (mS)( 12,38) I
Response
log10 conidi.a/mL ......
0 _ _ _ _ _~
'DI
I
I
Contour
Current X
35.5
5.25875
23.853125
Current Y
7.281 7.2n59841r----.'lIr---.:....~...,.1
Figure 6: Prediction profiter with maximized desirability
I Response log10 conidialmL
I Prediction Profiler
7.845
,,~ .
_. -_.-----
-~ - '-
.-
68.3536
2.74 .
~
Q)
0
.-'
....J
E
-- -------
-----
....
. -. ......
-- -.,._-_._-
(D
'0
<=0
u
0
~
9tL.-3
0
C.
n~
V8(18,53)
......J
pH(3.6.5)
47
21..1 n.o; .,
W
co
<.0
......
(]I
Conductivity (mS) (12.3S)
0
De 51 rabll tty