Optimization of lactic acid production in SSF by Lactobacillus amylovorus NRRL B-4542 using Taguchi methodology.

Abstract

Lactic acid production parameter optimization using Lactobacillus amylovorus NRRL B-4542 was performed using the design of experiments (DOE) available in the form of an orthogonal array and a software for automatic design and analysis of the experiments, both based on Taguchi protocol. Optimal levels of physical parameters and key media components namely temperature, pH, inoculum size, moisture, yeast extract, MgSO4 . 7H20, Tween 80, and corn steep liquor (CSL) were determined. Among the physical parameters, temperature contributed higher influence, and among media components, yeast extract, MgSO4 . 7H20, and Tween 80 played important roles in the conversion of starch to lactic acid. The expected yield of lactic acid under these optimal conditions was 95.80% and the actual yield at optimum conditions was 93.50%.

Full text

Page 1

The Journal of Microbiology, February 2005, p.38-43
Copyright ⓒ 2005, The Microbiological Society of Korea
Vol. 43, No. 1
NOTE
Optimization of Lactic Acid Production in SSF by Lactobacillus amylovorus
NRRL B-4542 Using Taguchi Methodology
Pyde Acharya Nagarjun1, Ravella Sreenivas Rao1, Swargam Rajesham2 and Linga Venkateswar Rao1,*
1Department of Microbiology, Osmania University, Hyderabad -500 007, India
2PRRM Engineering College, Shabad, Ranga Reddy -509 217, India
(Received July 21, 2004 / Accepted December 2, 2004)
Lactic acid production parameter optimization using Lactobacillus amylovorus NRRL B-4542 was per-
formed using the design of experiments (DOE) available in the form of an orthogonal array and a soft-
ware for automatic design and analysis of the experiments, both based on Taguchi protocol. Optimal
levels of physical parameters and key media components namely temperature, pH, inoculum size,
moisture, yeast extract, MgSO4· 7H20, Tween 80, and corn steep liquor (CSL) were determined.
Among the physical parameters, temperature contributed higher influence, and among media com-
ponents, yeast extract, MgSO4· 7H20, and Tween 80 played important roles in the conversion of starch
to lactic acid. The expected yield of lactic acid under these optimal conditions was 95.80% and the
actual yield at optimum conditions was 93.50%.
Key words: DOE, Lactic acid production, Lactobacillus amylovorus, Optimization, Taguchi Methodology
Lactic acid is a natural organic acid with wide applica-
tions in pharmaceutical, chemical, food, and health care
industries (Abdullatif et al. 2002). It is also widely used in
biodegradable plastics, meat preservation, and environ-
mentally friendly green solvents (Payot et al. 1999). It is
primarily produced by chemical synthesis in industries
other than biotechnological processes. Because of its great
demand, several microorganisms are screened worldwide
to determine their abilities to produce lactic acid. Lactic
acid producing bacteria (LAB) are well known to produce
lactic acid, of which Lactobacillus acidophilus, L. del-
brueckii, L. intermedius, L. plantarum, L. caseii, and L.
amylovorus were the most promising strains. Fungi like
Rhizopus sps (Litchfield, 1996) and genetically engi-
neered yeast cells have also been studied for lactic acid
production. (Donilo et al. 1999).
Various starchy substrates (Xiaodong et al. 1997), cel-
lulosic substrates (Venkatesh, 1997), like corn straw
(Xuexia et al. 2001), processed wood (Ana et al. 2001),
and alfalfa fiber (Hassan et al. 2001), have been used for
lactic acid production. In the present study, we used raw
corn starch for lactic acid production.
Solid Substrate Fermentation (SSF) has proved its supe-
riority over submerged fermentation technology in many
ways, including less water required, easy scale up, high
substrate utilization, easy elimination of contamination,
easy down streaming processing, and cost effectiveness
(Pandey, 1992; Krishna and Padma, 1998; Pandey et al.
1999; Xuexia et al. 2001; Soccol et al. 2003).
It has been reported that lactic acid production is depen-
dent on various process variables, including sucrose, yeast
extract, CaCO3 (Kotzamanidis et al. 2002), and some
types of bacterial species. In another study (Sudip et al.
2001), optimal control of starch additions in fed-batch
processes caused improved performance of the simulta-
neous saccharification and fermentation of starch to lactic
acid. Payot et al. (1999) reported that Bacillus coagulans
growth depends on aeration and nitrogen supplementa-
tion, temperature and pH, the initial concentration of
sugar affects the fermentation process for lactic acid pro-
duction. These studies demonstrated that optimization of
media components are important for lactic acid produc-
tion.
Taguchi’s method is based upon an approach, which is
completely different from the conventional practices of
quality engineering. This methodology emphasizes inte-
grating quality into products and processes, whereas usual
practice relies upon inspection (Taguchi, 1986). Taguchi
essentially utilizes the conventional statistical tools, but
simplifies them by identifying a set of stringent guidelines
* To whom correspondence should be addressed.
(Tel) 91-40-27682246; (Fax)
(E-mail) vrlinga@yahoo.com

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Vol. 43, No. 1Optimization of lactic acid production in SSF by Lactobacillus amylovorus~
39
for experiment layout and analysis of results. Taguchi’s
standardized version of DOE, popularly known as the
Taguchi method or Taguchi approach, was introduced in
USA in the early 1980’s (Ross, 1996).
The application of DOE requires careful planning, pru-
dent layout of the experiment, and expert analysis of
results (Roy 1990). Based on years of research and appli-
cations, Taguchi has standardized methods for each of
these DOE application steps. Thus, DOE using Taguchi
approach has become a much more attractive tool for
practicing engineers and scientists (Roy, 2001).
This Taguchi method not only provides aid for consid-
erable time and cost-saving, but also leads to a more fully
developed process. It has several design arrays, such as
OA12, OA 18, OA 36, and OA 54, which enable a focus
on main effects and help to increase the efficiency and
reproducibility of small-scale experiments.
In the present study, we optimized lactic acid produc-
tion in SSF by Lactobacillus amylovorus NRRL B-4542
using Taguchi methodology. To the best of our knowl-
edge, no reports are available on lactic acid production
optimization by Lactobacillus amylovorus NRRL B-4542
in SSF using Taguchi Methodology.
The microorganism Lactobacillus amylovorus NRRL B-
4542 used in this study was obtained from the USDA
Northern Regional Research Center, Peoria, Illinois.
Lactobacillus amylovorus NRRL B-4542 was maintained
on Cheng et al. (1991) media, (0.034 g FeSO4, 1.0 g Sodium
acetate 1.23 g MgSO4·7H2O, 0.034 g MnSO4·H2O, 0.65 g
K2HPO4·3H2O, 0.5 g KH2PO4, 30 g yeast extract, 10 g sol-
uble starch, 15 g agar/l medium, pH 5.5). Slants were pre-
pared with Cheng et al. Medium, inoculated and incubated
at 40oC for 24 h, and stored at 4oC until further use.
The organism was grown in 50 ml Cheng et al. (1991)
liquid medium in 250 ml conical flask containing 1% sol-
uble starch by incubating at 40oC for 18 h, to obtain the
required cell concentration (O.D. 1.7 at 660 nm). The
above cells were centrifuged, washed, and used as inocula
in Solid State Fermentation.
Total lactic acid was estimated according the method of
Kimberley and Taylor (1996).
Taguchi has established OAs (Orthogonal Arrays) to
describe a large number of experimental situations,
mainly to reduce experimental errors and to enhance the
efficiency and reproducibility of laboratory experiments.
The symbolic designation of these arrays indicates the
main information on the size of the experimentation, e.g.
Table 1. Factors and their levels assigned to different columns
FactorLevel 1
35
4.5
14
50
1.0
0.05
0.05
0.50
Level 2
40
5.5
17
60
2.0
0.1
0.1
1.0
Level 3
--
6.50
20
70
3.00
0.15
0.15
1.50
Temperature (oC)
pH
Inoculum (%, v/w)
moisture (%, v/w)
Yeast Extract (%, w/v)
MgSO4·7H2O (%, w/v)
Tween 80 (%, v/v)
CSL (%, w/v)
Table 2. L18 (21X 37) Orthogonal Array
Experiment
No.
Column
% of Lactic acid production
(g/g Substrate)
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
1
1
1
2
2
2
3
3
3
1
1
1
2
2
2
3
3
3
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
4
1
2
3
1
2
3
2
3
1
3
1
2
2
3
1
3
1
2
5
1
2
3
2
3
1
1
2
3
3
1
2
3
1
2
2
3
1
6
1
2
3
2
3
1
3
1
2
2
3
1
1
2
3
3
1
2
7
1
2
3
3
1
2
2
3
1
2
3
1
3
1
2
1
2
3
8
1
2
3
3
1
2
3
1
2
1
2
3
2
3
1
2
3
1
1
2
3
4
5
6
7
8
9
83.4
83.5
88.9
87.5
88.5
84.5
89.7
82.5
86.7
89.8
90.9
83.4
91.5
85.3
84.3
88.5
87.4
89.9
10
11
12
13
14
15
16
17
18

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40Nagarjun et al.
J. Microbiol.
L18 has 18 trials. The total number of degrees of freedom
available in an OA is equal to the number of trials minus
one. Each column consists of a number of conditions
depending on the levels assigned to each factor. In the
present study, all 8 columns are assigned with different
factors, as indicated in Table 1. The temperature factor
has been assigned with only two levels. Hence, it has nine
level 1 and nine level 2 conditions (21). While, in the case
of the other seven factors, each factor is assigned with
three levels. Therefore, these factors have six level 1, six
level 2, and six level 3 conditions (37). Table 2 shows the
layout of the L18 (21X 37) orthogonal array used in the
present study.
All of the combination experiments using the assigned
parameter values were conducted using a 250 ml conical
flask containing 10 g of deoiled groundnut cake as a solid
support and 10% raw corn starch as substrate, 0.034 g
FeSO4, 0.034g MnSO4·H2O, 0.65g K2HPO4·3H2O, 0.5g
KH2PO4/ l medium were added and sterilized by auto-
claving at 121oC for 15 min, dried, and kept ready for
inoculations. For this, required concentrations of inocu-
lum and other media components, previously weighed
and sterilized, were added according to the DOE and were
then incubated at temperature of 37oC and 40oC for 72 h.
After 72 h of fermentation, the culture broth was sepa-
rated and analyzed for lactic acid (Table 2).
QUALITEK 4 Software for automatic design and anal-
ysis of Taguchi experiments was used to study the fol-
lowing objectives of the analysis. Identification of the
individual influence of each factor. Determination of the
optimum condition. Estimation of performance at the
optimum condition.
The main effects of the factors at the assigned levels on
lactic acid production are depicted in Table 3. The differ-
ence between the average value of each factor at levels 2
and 1 indicates the relative influence of the effect; the
larger the difference, the stronger the influence. The sign
of the difference (+ or -) indicates whether the change
from level 1 to level 2 increased or decreased the result
(Table 3). Based on this data, it can be seen that yeast
extract and inoculum showed strong negative influences
when switched from one level to another. Strong positive
influence was noticed in terms of temperature and
MgSO4·7H2O with the assigned levels (Table 3). How-
ever, when interactions of different factors were calcu-
lated (Table 4), it is interesting to note that the factors
perceived to be the least significant, such as CSL, pH, and
moisture (at their individual levels), showed the highest
severity index percentages. Similarly, the severity index
percentage for CSL (the weaker impact factor) vs
MgSO4·7H2O (the stronger impact factor) was 71.01.
These results suggest that the influence of one factor on
lactic acid production was dependent upon the condition
of the other factors in optimizing lactic acid production
process parameters.
The percentage contributions of each factor are shown
in an ANOVA Table 5. The last column of the ANOVA
indicates the influence of each factor. Yeast extract was
the most significant factor for lactic acid production. The
confidence level for the yeast extract factor was observed
to be 99.84%, while MgSO4·7H2O was significant with a
confidence level of 99.31%. Moisture, pH, and CSL fac-
tors showed negligible influences on the lactic acid yield
at their individual levels.
Optimum conditions and their performance in terms of
contribution to achieving maximum yield of lactic acid
production were given in Table 6. It can be seen from the
table that factors such as yeast extract, MgSO4·7H2O,
and Tween 80 play more significant roles in product for-
mation than do the other selected parameters and their
levels. The expected result at optimum conditions was
95.80%, the actual yield at optimum conditions was
93.50%.
Pooling was done until the degrees of freedom (DOF)
of the error term became close to half of the total of the
experimental DOF. Thus the pH, moisture, and CSL were
pooled. These ANOVA are shown in Table 7. Tempera-
ture, inoculum, yeast extract, MgSO4·7H2O, and Tween
80 were significant to the overall process, with confidence
levels of 98.98, 97.70, 99.84, 99.31, and 98.71, respec-
tively.
After pooling of optimum conditions, performance was
given in Table 8. The formation of product under optimal
conditions was estimated using only the significant fac-
tors. Among all of the factors studied, yeast extract,
MgSO4·7H2O, Tween 80, and inoculum were most
important for lactic acid production. These results indicate
that the expected result under optimal conditions was
94.04%, while the actual yield at optimum conditions was
93.10%.
The pH, therefore, can be left at 5.5 (level 2) without
adjustment with acid. The moisture can be held at 60%
(level 2), and the CSL can be held at 0.5 g (level 1) for
economical consideration. The levels of other factors
should be held as indicated in Table 8.
A combination of factors and their levels involved in
Table 3. Main effects of the factors at the assigned levels on lactic acid
production
Factor
Temperature (oC)
pH
Inoculum (%, v/w)
moisture (%, v/w)
Yeast Extract (%, w/v)
MgSO4· 7H2O (%, w/v)
Tween 80 (%, v/v)
CSL (%, w/v)
Level 1
86.133
86.649
88.399
86.700
87.283
85.450
85.966
86.400
Level 2
87.888
86.933
86.350
87.750
84.950
87.116
86.533
87.599
Level 3
--
87.450
86.283
86.583
88.799
88.466
88.533
87.033
L2 - L1
1.755
0.284
-2.050
1.049
-2.333
1.665
0.567
1.198

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Vol. 43, No. 1Optimization of lactic acid production in SSF by Lactobacillus amylovorus~
41
the production of lactic acid by Lactobacillus amylovorus
NRRL B-4542 was identified for obtaining maximum
yield. The DOE using the Taguchi protocol has proved to
be economical in optimization of lactic acid production.
From the analysis, it is evident that the yeast extract,
MgSO4·7H2O, and Tween 80 were found to be insignif-
icant, while the rest of the factors were determined to be
significant with confidence levels of 70% and above. The
Table 4. Estimated interaction of severity index for different parameters
FactorsColumns@
6 x 8
4 x 8
3 x 4
2 x 4
3 x 7
2 x 7
3 x 5
4 x 5
1 x 2
2 x 6
5 x 7
2 x 5
7 x 8
2 x 3
4 x 6
2 x 8
1 x 8
1 x 5
3 x 8
1 x 7
3 x 6
6 x 7
1 x 6
5 x 8
1 x 4
5 x 6
4 x 7
1 x 3
SI (%)$
71.01
62.14
61.94
59.54
49.04
44.23
42.81
38.93
38.49
37.30
36.92
35.87
32.63
31.37
21.87
20.83
19.99
19.07
18.88
17.21
15.21
14.61
13.77
13.47
10.67
7.48
1.32
0.32
Col*Levels&
[2,1]
[2,1]
[1,2]
[2,2]
[1,2]
[1,3]
[1,3]
[2,3]
[2,3]
[1,3]
[1,3]
[2,3]
[3,2]
[2,1]
[2,3]
[3,3]
[2,2]
[2,3]
[1,2]
[2,3]
[2,3]
[3,3]
[1,3]
[3,1]
[2,2]
[1,3]
[2,3]
[2,1]
MgSO4· 7H2O xCSL
moisture x CSL
Inoculum x moisture
pH x moisture
Inoculum x Tween80
pH x Tween 80
Inoculum x Yeast Extract
Temparature (oC) x pH
moisture x Yeast Extract
pH x MgSO4· 7H2O
Yeast extract x Tween 80
pH x Yeast Extract
Tween80 x CSL
pH x Inoculum
moisture x MgSO4· 7H2O
pH x CSL
Temparature (oC) x CSL
Temparature (oC) x Yeast Extract
Inoculum x CSL
Temparature (oC) x Tween80
Inoculum x MgSO4· 7H2O
MgSO4· 7H2O x Tween80
Temperature (oC) x MgSO4· 7H2O
Yeast extract x CSL
Temperature (oC) x moisture
Yeast extract x MgSO4· 7H2O
moisture x Tween 80
Temperature (oC) x inoculum
14
12
7
6
4
5
6
1
3
4
2
7
15
1
2
10
9
4
11
6
5
1
7
13
5
3
3
2
@Columns - Represent the column locations to which the interacting factors are assigned.
$SI - interaction severity index (100% for 90 degree angle between the lines, 0% for parallel lines.
*Col - Shows column that should be reserved if this interaction effect were to be studied (2-L factors only).
&Opt - Indicates the factor levels desirable for the optimum conditions (based strictly on the first 2 levels). If an interaction is included in the study
and found to be significant (in ANOVA), the indicated levels must replace the factor levels identified for the optimum conditions without consid
erations of any interaction effects.
Table 5. Analysis of Variance (ANOVA)
Factors
Temperature (oC)
pH
Inoculum (%, v/w)
moisture (%, v/w)
Yeast Extract (%, w/v)
MgSO4.7H2O (%, w/v)
Tween 80 (%, v/v)
CSL (%, w/v)
Other/Error
Total
DOF
1
2
2
2
2
2
2
2
2
17
Sums of squares
13.870
1.963
17.371
4.959
45.131
27.406
21.815
4.321
-0.001
136.836
Variance
13.870
0.981
8.685
2.479
22.565
13.703
10.907
2.160
-0.001
F-Ratio
277406.313
19638.797
173716.433
49597.250
451315.759
274063.475
218151.096
43217.669
Pure sum
13.870
1.963
17.371
4.959
45.131
27.406
21.815
4.321
percent
010.136
001.435
012.694
003.624
032.981
020.027
015.941
003.158
000.004
100.00%

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42Nagarjun et al.
J. Microbiol.
DOE using the Taguchi methodology for optimizing the
yield of lactic acid production was an efficient strategy.
We are grateful to R.K.Roy, Michigan, USA for pro-
viding us with QUALITEK-4 software.
References
Abdullatif, T. and Y. Shang-Tian. 2002. Production of L(+)- lactic
acid from glucose and starch by immobilized cells of Rhizopus
oryzae in rotating fibrous bed bioreactor. Wiley Periodicals,
Inc.
Ana, B.M., L.A. Jose, and C.P. Juan. 2001. Strategies to improve
the bioconversion of processed wood into lactic acid by simul-
taneous saccharification and fermentation. J. Chem. Technol.
Biotechnol. 76, 279-284
Cheng, P., R. Mueller, S. Jaeger, R. Bajpai, and G. Iannotti. 1991.
Lactic acid production from enzyme thinned corn starch using
Lactobacillus amylovorus. J. Ind. Microbiol. 7, 27-34.
Donilo, P., M.B. Michele, B. Luca, M. Rossella, B. Davide, C. Vit-
torio, L. Jefferson, L. Chi-li, M.R. Bianca, F. Laura, and A.
Lilia. 1999. Replacement of a metabolic pathway for large-
scale production of lactic acid from engineered yeasts. Appl.
Eviron. Microbial. 65, 4211-4215.
Table 6. Optimal Conditions and Performance
Factors
Temperature (oC)
pH
Inoculum (%, v/w)
moisture (%, v/w)
Yeast Extract (%, w/v)
MgSO4.7H2O (%, w/v)
Tween 80 (%, v/v)
CSL (%, w/v)
Level Description.
40
6.5
14
60
03
0.15
0.15
1
Level
2
3
1
2
3
3
3
2
contribution
0.877
0.438
1.388
0.738
1.788
1.455
1.522
0.588
8.793
87.011
95.805
Total contribution from all factors
Current grand average performance
Expected result at optimal conditions
Table 7. Analysis of pooled Variance (POOLED ANOVA)
Factors
Temperature (oC)
pH
Inoculum (%, v/w)
moisture (%, v/w)
Yeast Extract (%, w/v)
MgSO4.7H2O (%, w/v)
Tween 80 (%, v/v)
CSL (%, w/v)
Other/Error
Total
DOF
1
(2)
2
(2)
2
2
2
(2)
8
17
Sums of squares
13.870)
(1.963)
17.371)
( 4.959)
45.131)
27.406)
21.815)
( 4.321)
11.245)
136.84)
Variance
13.870
F-Ratio
09.867
POOLED (CL = 98.84)
06.179
POOLED (CL = 92.18%)
16.053
09.748
07.759
POOLED (CL = 77.07)
Pure sum
12.464
Percent
0 9.108
08.68514.56 10.640
22.565
13.703
10.907
42.32
24.594
19.033
30.926
17.973
13.887
01.405 17.466
100.00%
Table 8. Optimal Conditions and their Performance in Production of Lactic Acid
Factors
Temperature (oC)
Inoculum (%, v/w)
Yeast Extract (%, v/w)
MgSO4· 7H2O (%, v/w)
Tween 80 (%, v/w)
Level Description
400
14
03
0.15
0.15
Level
2
1
3
3
3
Contribution
0.877
1.388
1.788
1.455
1.522
7.030
87.011
94.041
Total contribution from all factors
Current grand average performance
Expected result at optimal conditions