Skip to content
Publicly Available Published by De Gruyter July 25, 2018

Improvement of delta-endotoxin production from local Bacillus thuringiensis Se13 using Taguchi’s orthogonal array methodology

Lokal Bacillus thuringiensis Se13’ün Delta-endotoxin Üretiminin Taguchi Ortogonal Dizi Metodu Kullanılarak Arttırılması
  • Ardahan Eski , Zihni Demirbağ and İsmail Demir ORCID logo EMAIL logo

Abstract

Objective

The insecticidal activity of Bacillus thuringiensis directly depends on the yield of delta-endotoxins. In this study, various nutritional and cultural parameters influencing delta-endotoxin synthesis by a local isolate of B. thuringiensis Se13 were investigated using Taguchi methods.

Methods

In the first experiment, four factors, incubation period, incubation temperature, initial pH and medium, each at four levels, were selected and an orthogonal array layout of L16 was carried out. In the second experiment, Taguchi’s orthogonal array method of L27 was used to evaluate the effects of the different concentration of medium components. Taguchi’s signal–noise ratio and variance analysis were applied to determine the effect of the factors. After each experiment, verification studies were carried out using determined optimum conditions.

Results

The optimum conditions for incubation period, incubation temperature, initial pH, and medium determined as 72 h, 30°C, pH 9, and M4 medium, respectively. In the second experiment, soybean flour (5%), glucose (5%), KH2PO4 (0.3%), K2HPO4 (0.1%), MgSO4 (0.4%) were determined as the optimum conditions. The delta-endotoxin yield was elevated to 1559.25 μg mL−1 when the factors were adjusted to optimum level.

Conclusion

Optimization using the Taguchi method appeared to be a good choice for the overproduction of delta-endotoxin.

Özet

Amaç

Bacillus thuringiensis’in insektisidal aktivitesi delta-endotoksin miktarı ile doğrudan ilgilidir. Bu çalışmada, lokal bir izolat olan B. thuringiensis Se13’ün delta-endotoksin sentezini etkileyen çeşitli besinsel ve kültürel parametreler Taguchi metodu kullanılarak araştırıldı.

Gereç ve Yöntem

İlk deneyde, inkübasyon süresi, inkübasyon sıcaklığı, besiyeri ve besiyerinin başlangıç pH’ını kapsayan dört faktörün dört farklı seviyesi seçildi ve uygulamada L16 ortogonal dizisi kullanıldı. İkinci deneyde ise besiyeri içeriğindeki maddelerin farklı konsantrasyonlarının toksin üretimine etkisi Taguchi L27 ortogonal dizisi kullanılarak test edildi. Faktörlerin etkisini belirlemek için sinyal-gürültü oranı ve varyans analizi uygulandı. Her deneyden sonra optimum koşullar kullanılarak doğrulama çalışmaları gerçekleştirildi.

Bulgular

İnkübasyon süresi, inkübasyon sıcaklığı, başlangıç pH’sı ve besiyeri için optimum koşullar, 72 saat, 30°C, pH 9 ve M4 besiyeri olarak tespit edildi. İkinci deney sonucunda, M4’ün optimum içeri de 5% soya unu, 5% glukoz, 0.3% KH2PO4, 0.1% K2HPO4, 0.4% MgSO4 olarak belirlendi. Belirlenen optimum koşulların kullanılmasıyla endotoksin miktarı 1559.25 μg ml−1’ye yükseldi.

Sonuç

Sonuç olarak, Taguchi metodu ile optimizasyonun yüksek miktarda delta-endotoksin üretimi için iyi bir seçenek olduğu ortaya çıktı.

Introduction

After the World War II, the development of chemical insecticides and their usage in management of insect pests have resulted in significant growth in agricultural production. However, uncontrolled overuse of chemical insecticides has led to the development of insect resistance, extinction of natural enemies and predominance of the secondary pest [1], [2]. Also, serious environmental and health issues began to be recognized by the presence of chemical residues in food, water, and air [3], [4], [5], [6].

To neutralize these problems, microbial insecticide is one of the most promising alternatives over chemical insecticides, which offers less or no harm to non-target fauna and flora. Bacillus thuringiensis is insect pathogen that has been used to control insect pests for several decades [7], [8]. The insecticidal activity of this pathogen is based on delta endotoxins produced by the bacterial cells during the sporulation. For improving the efficiency of B. thuringiensis, the yield of the delta-endotoxin must be optimized. These toxin proteins have many characteristics of secondary metabolites and their quantity is affected by cultural conditions such as incubation period, incubation temperature, initial pH, concentrations of medium components, etc. [9], [10], [11]. So that, optimization of the culture conditions is still one of the most important issue.

Conventional experimental design methods for optimization are too complicated and difficult to be used. In addition, these methods need many experiments when the number of application parameters increases. However, statistical design of experimental methods provides an easier and equally efficient approach to optimize several operational variables. The frequently used methods are: factorial design, central composite design (CCD), Taguchi methods, response surface methodology (RSM), Plackett-Burmana and Box-Behnkena (BBD) design, and artificial neural network (ANN) [12]. Taguchi’s optimization technique reduces cost and improves quality. The advantages of Taguchi method over the other methods are that several factors can be simultaneously optimized, and more quantitative information can be extracted from fewer experimental trials [13]. Taguchi experimental design method, a useful tool for designing high quality system was developed by Taguchi. This method uses a design of orthogonal arrays to conduct a set of experiments and tells us how different parameters affect the yield in a small number of experiments instead of testing all the possible combinations [14]. The results of the Taguchi method are then transformed into a signal-to-noise (S/N) ratio. Taguchi uses the S/N ratio to measure the quality characteristics deviating from the desired values. Based on the analysis of the S/N ratio, the optimal levels of the process factors are determined. Furthermore, a statistical analysis of variance (ANOVA) is performed to see which process factors are statistically significant. Finally, a confirmation experiment is conducted to validate the optimal process factors obtained from the Taguchi method [15].

Although Taguchi method initially was developed for improving the quality of goods manufactured, more recently, it has been also applied to engineering [16], [17], [18], biotechnology [12], [19], [20], marketing [21] and advertising [22]. Several reports are available about the application of Taguchi’s method in the field of biotechnology. The effect of growth factors including carbon, nitrogen, and mineral concentrations, physical, chemical and other fermentation factors have been investigated and reported in various studies which have discussed the optimum conditions of these factors required for optimization of primer and seconder metabolites. Shukla and Goyal [23] developed an efficient fermentation process for improving dextransucrase production from Weissella confusa Cab3 by Taguchi’s orthogonal array methodology. Muhammad et al. [24] applied Taguchi’s experimental design for optimizing the production of thermostable polypeptide antibiotic from Geobacillus pallidus SAT4. El-Bendary et al. [19] studied cultural parameters influencing mosquitocidal toxin using Taguchi experimental design. Chenthamarakshan et al. [25] used Taguchi method to optimize extracellular laccase enzyme production in solid state fermentation from the fungi Marasmiellus palmivorus LA1.

Considering this information, we focused to investigate the statistical optimization of cultural conditions for production of delta-endotoxins of B. thuringiensis strain Se13. Various parameters as the effects of incubation period, incubation temperature, initial pH, medium, and concentrations of medium components were evaluated for maximum delta-endotoxin production using Taguchi’s orthogonal array method.

Materials and methods

Microorganism and culture maintenance

The strain Se13 of B. thuringiensis was obtained from Karadeniz Technical University, Department of Biology, and Laboratory of Microbiology. This strain isolated from Spodoptera exigua cadaver (Lepidoptera: Noctuidae) in our previous study and were found to contain cry1 and cry2 endotoxins which are effective on lepidopterans. Also, it had efficiency on the larvae of the S. exigua which is a polyphagous pest (unpublished data).

Stock culture of the isolate strain was stored in 30% glycerol at −80°C. Before each experiment, the bacterium was subcultured from frozen stock onto a tryptic soy agar medium (TSA) and a loopful of bacteria was used to inoculate a 250 mL Erlenmeyer flask containing 50 mL of sterilized tryptic soya broth (TSB) medium. The flask was incubated in a rotary shaker at 250 rpm at 30°C for 12 h. A 1% (v/v) inoculum from this flask was then used to inoculate 500 mL Erlenmeyer flasks containing 100 mL of sterilized medium.

Design of experiments

The first experiments were designed using Taguchi’s orthogonal array method based on four levels and four factors, and 16 runs were used to optimize the effect of incubation period, incubation temperature, initial pH, and medium for maximum delta-endotoxin production from B. thuringiensis strain Se13. Table 1 indicates the selected factors and their levels for optimization of delta-endotoxin production by this bacterial strain. Shake flask experiments were performed according to the experimental design listed in Table 2. The compositions of the media used in the study were given in Table 3. All chemicals used in the study were purchased from Sigma-Aldrich (USA).

Table 1:

Factors and their levels used in the first experiment.

FactorsLevel 1Level 2Level 3Level 4
Incubation period (h)487296120
Incubation temperature (°C)25303437
Initial pH6789
MediumM1M2M3M4
Table 2:

L16 orthogonal array of Taguchi experimental design and corresponding delta-endotoxin production by B. thuringiensis strain Se13.

ExperimentsFactors and levelsδ-Endotoxin

(μg mL−1)
Incubation

period
Incubation temperatureInitial pHMedium
11111491.242
21222346.596
31333535.989
41444641.258
52123710.043
62214844.860
72341674.987
82432504.868
93134772.569
103243779.386
113312340.050
123421329.689
134142584.684
144231762.890
154324708.128
164413569.580
Table 3:

Composition of the media used in the first experiment.

Media
M1M2M3M4
Dextrose (%1.5)Starch (%1.5)Glucose (%4)Soybean flour (%2.5)
Cottonseed flour (%1)Yeast (%1)Yeast (%1.5)Glucose (%2.5)
Peptone (%0.2)Corn steep liquor (%0.2)Corn steep liquor (0.4)K2HPO4 (%0.3)
Yeast (%0.2)CaCO3 (%0.8)(NH)SO (%0.4)KH2PO4 (%0.3)
CaCO3 (%0.1)NaPO2H2 (%0.4)MgSO4 (%0.06)MgSO4 (%0.4)
MgSO4 (%0.03)FeSO4 (%0.06)
FeSO4 (%0.002)ZnSO4 (%0.001)
ZnSO4 (%0.002)MnSO4 (%0.01)
CaSO4 (%0.001)

In the second experiments, Taguchi’s orthogonal array method of L27 used to evaluate the effects of components of the M4 which was determined as the optimum medium in the first experiment. The levels of the factors were studied, and the layout of the L27 Taguchi’s orthogonal array was shown in Tables 4 and 5, respectively.

Table 4:

Factors and their levels used in the second experiment.

FactorsLevel 1Level 2Level 3
Soybean flour (%)12.55
Glucose (%)12.55
KH2PO4 (%)0.10.30.5
K2HPO4 (%)0.10.30.5
MgSO4 (%)0.20.40.8
Table 5:

L27 orthogonal array of Taguchi experimental design and corresponding δ-endotoxin production by B. thuringiensis Se13.

ExperimentsFactors and levelsδ-Endotoxin (μg/mL)
Soybean flourGlucoseKH2PO4K2HPO4MgSO4
111111697.54
211112773.16
311113749.81
412221762.11
512222945.15
612223925.36
713331756.75
813332817.18
913333902.51
1021231934.44
1121232998.45
1221233978.56
13223111151.29
14223121311.29
15223131302.56
16231211263.56
17231221325.65
18231231261.60
1931321942.98
20313221020.06
21313231061.94
22321311196.48
23321321256.48
24321331188.12
25332111467.26
26332121638.75
27332131471.56

Taguchi’s signal–noise ratio and variance analysis (ANOVA) were applied to determine the effect of the factors. All assays were performed in triplicate runs, and results were evaluated using Minitab 17 software (Minitab Inc., USA).

Validation of the model

In order to validate the models, fermentations were carried out using the optimum conditions obtained by Taguchi method. The results were compared to estimated values obtained by the software.

Determination of delta-endotoxins

Delta-endotoxin concentration was determined in the solubilized crystal preparation from each culture medium as illustrated by Zouari et al. [26]. At the end of the fermentation, 1 mL of each sample was centrifuged at 13,000×g for 10 min at a temperature of 4°C. The pellets were washed thrice with 1 M cold NaCl and then thrice with cold sterile distilled water. After, the pellets were dissolved with 50 mM NaOH and incubated 2 h at 30°C, the suspensions were centrifuged at 13,000×g for 10 min at 4°C. The supernatant including insecticidal crystal protein was used to determine the delta-endotoxins concentration according to Bradford method [27], using bovine serum albumin as a standard.

Results

The Taguchi orthogonal array is a convenient and useful choice for optimization of biotechnological processes. So, the effects of culture conditions, incubation period, incubation temperature, initial pH and medium differences, on the delta-endotoxin production by B. thuringiensis strain Se13 were tested via Taguchi experimental design in 16 runs. The experimental results showed significant variation in the yield of endotoxin, and its production was found to be extensively dependent on the culture conditions. The yield of delta-endotoxin ranges from 346.60 to 844.86 μg mL−1 (Table 2). The signal-noise ratio was used to determine optimum levels of the tested factors. The S/N ratio should have a maximum value to obtain optimum delta-endotoxin production, according to the Taguchi method. So, the larger is better approach was used. Although the highest yield of endotoxin was obtained from run 6 with a combination of 72 h for incubation period, 30°C for incubation temperature, pH6 for initial pH and M4 for medium, optimum conditions determined as 72 h, 30°C, pH9 and M4 medium (Figure 1).

Figure 1: Effects of cultural conditions on the S/N ratios for the production of delta-endotoxin.
Figure 1:

Effects of cultural conditions on the S/N ratios for the production of delta-endotoxin.

In addition, analysis of variance (ANOVA) was used to determine the how much variation of each factor has contributed and, the results were shown in Table 6. While medium with 46.79% has shown highest positive impact on the delta endotoxin production among the tested factors, pH with 13.81% showed the least impact. The ANOVA of the delta-endotoxin production has the model F-value of 18.22, indicated that the model is significant. The model obtained from ANOVA displayed that the multiple correlation coefficient of R2 is 0.9865 i.e. the model can explain 98.65% variation in the response. Also, adequate precision value of the model was determined as 14.038. So, this model may be used to navigate the design space. The model showed standard deviation, mean, coefficient of variance (CV) and predicted residual sum of square (PRESS) values of 43.04, 599.8, 7.18, and 1581E+005, respectively. Point prediction for achieving the highest delta-endotoxin production based on the levels of the factors was shown in Table 7. Validation experiment was performed under optimum conditions, and the values found to be 971.2 μg mL−1. It displayed that the experimental value was compatible with the predicted value.

Table 6:

Analysis of variance for the first experiment.

SourceDFSeq SSContributionAdj SSAdj MSF-valuep-Value
Incubation period38569120.87%856912856415.420.025
Incubation temperature37052317.18%705232350812.690.033
Initial pH35670413.81%567041890110.200.044
Medium319209146.79%1920916403034.570.008
Error355571.35%55571852
Total15410566100.00%
  1. S=43.03 R-Sq=98.65% R-Sq(adj)=93.23%.

Table 7:

Point prediction for the first optimization process.

PredictionStandard error of mean95% Confidence interval low95% Confidence interval highOptimum conditions
979.5038.7856.041102.9672 h, 30°C, pH 9, M4

In a second step, the effect of M4 medium components were tested using Taguchi’s orthogonal array method of L27. The yield of delta-endotoxin ranges from 697.54 to 1638.75 μg mL−1 (Table 5). The results of the Taguchi orthogonal array experiments were evaluated using S/N ratio and ANOVA and, the results were shown in Figure 2 and Table 8, respectively. The optimum conditions of delta-endotoxin production were obtained from run 26 with a combination of soybean flour (5%), glucose (5%), K2HPO4 (0.3%), KH2PO4 (0.1%) and, MgSO4 (0.4%). Contributions of the medium ingredients on the delta-endotoxin production were shown in Table 8. While soybean flour with 57.95% has shown highest positive impact on the delta-endotoxin production among the tested factors, KH2PO4 with 2.44% showed the least impact. The ANOVA of the delta endotoxin production has the model F-value of 76.14 indicated that the model was significant. The model obtained from ANOVA displayed that the multiple correlation coefficient of R2 was 0.9794 i.e. the model can explain 97.94% variation in the response. Also, adequate precision value of the model was determined as 30.032. So, this model may be used to navigate the design space. The model showed standard deviation, mean, coefficient of variance (CV) and predicted residual sum of square (PRESS) values of 46.29, 1077.77, 165 4.29 and 97610.61, respectively. Point prediction for achieving the highest delta-endotoxin production based on the levels of the factors was shown in Table 9. The result of validation experiment was found to be 1559.25, and it was corresponded well with the predicted value of 1568.74 by the model.

Figure 2: Effects of medium ingredients on the S/N ratios for the production of delta-endotoxin.
Figure 2:

Effects of medium ingredients on the S/N ratios for the production of delta-endotoxin.

Table 8:

Analysis of variance (ANOVA) for second experiment and contributions of the medium ingredients.

SourceDFSeq SSContributionAdj SSAdj MSF-valuep-Value
Model101631000163100016310075.980.0001a
Soybean flour296514757.95%965147482600224.810.0001a
Glucose243860426.34%438604219300102.170.0001a
KH2PO42406452.44%40645203229.470.0019a
K2HPO421369048.22%1369046845231.890.0001a
MgSO42497392.99%497392486911.590.0008a
Error16343452.06%343452146
Total2616655383100%
  1. S=46.33 R-Sq=97.94% R-Sq(adj)=96.65%.

  2. aSignificant terms.

Table 9:

Point prediction for the second optimization process.

PredictionStandard error of mean95% Confidence interval low95% Confidence interval highOptimum conditions
1568.7429.571506.051631.43Soybean flour (5%)
Glucose (5%)
KH2PO4 (0.3%)
K2HPO4 (0.1%)
MgSO4 (0.4%)

Discussion

Using microorganisms for pest management is the best alternative to conventional pesticides. Bacillus thuringiensis producing delta endotoxin proteins during sporulation is the mostly used bacterium for this purpose. These proteins are harmless to non-target organisms, are completely biodegradable and cause no toxic residual products to accumulate in the environment. The yield of these delta endotoxins varies depending on the nutritional and cultural conditions. In this study, Taguchi orthogonal array method was successfully applied to test the relative importance of culture conditions and medium components on delta-endotoxin production of B. thuringiensis strain Se13. In the process, the effects of incubation period, incubation temperature, initial pH and, concentration of medium ingredients were investigated.

Incubation period is significantly important factor for delta-endotoxin production. Delta-endotoxins were produced during sporulation phage of B. thuringiensis. Sporulation starts after 48 h of growth and, spores and crystals are released from the cells, complete sporulation is achieved at 54 h, and by 72 h the spores and crystals are found to have been released. Seventy-two hours was determined as the best time for maximum delta-endotoxin production in our study (Figure 1). A decrease in the amount of endotoxin was observed after 72 h due to the fact that the endotoxins are degraded by the proteases [28].

The normal temperature for toxin production of B. thuringiensis is 30°C, but it can show variability. Özkan et al. [29] determined that synthesis of Cry4Ba toxin from B. thuringiensis israilensis HD500 was the optimal when the organism was grown at 25°C whereas Cry11Aa synthesis was optimal at 30°C. On the other hand, Yousten et al. [30] and Lacey [31] found that spore and toxin productions were adversely affected at 35°C. At the present work, optimum temperature for the delta-endotoxin production was determined as 30°C (Figure 1).

The changes of pH can affect the delta-endotoxin production. Morris et al. [32] found that starting pHs 7.0 and 8.0 were suitable for toxin production from the B. thuringiensis HD133. Zou et al. [33] indicated that although the bacteria could grow in weak acid medium, it blocked the metabolic pathway which directly affected the yield of delta-endotoxin. In neutral (7.0) or slightly alkaline (9.0) medium, both physical growth and delta-endotoxin production reached the maximum. On the contrary of these, Içgen et al. [34] found that crystal protein synthesis was more efficient at a narrow pH range of 5.5–6.5. In our study, initial pH was a significant factor and the optimum initial pH was determined as 9.0 using Taguchi’s experimental design (Figure 1). On the other hand, initial pH for mosquitocidal toxins production by Lysinibacillus sphaericus using Taguchi’s experimental design was not a significant factor [19].

Optimization of medium is one of the effective approaches to promote delta-endotoxin production. Firstly, four different media were tested for maximum endotoxin production using the Taguchi assay design, and the most effective medium was identified as M4 medium (Figure 2). It contains soybean flour (2.5%) as nitrogen source, glucose (2.5%) as carbon source, K2HPO4 (0.3%), KH2PO4 (0.3%), MgSO4 (0.4%) as mineral elements. When the effect of different concentrations of the medium ingredients on delta-endotoxin production was examined using the Taguchi method, and optimum concentrations of the components were determined as 5% soybean flour, 5% glucose, 0.3% K2HPO4, 0.1% KH2PO4 and 0.4% MgSO4 (Figure 2).

The delta-endotoxin proteins which compose approximately 30% of total protein of B. thuringiensis are synthesized from amino acids derived from the complex nutrients. Içgen et al. [35] were used soybean flour, peptone, corn steep liquor, and casamino acid as a nitrogen source. They found that all of them had no negative effect on the delta-endotoxin synthesis. However, peptone was the best choice for optimum toxin production. Ben Khedher et al. [36] and Ennouri et al. [37] displayed that soybean had positive affect on delta-endotoxin production. Our results agree with reports that nitrogen and carbon sources are the main components that affect the synthesis of delta-endotoxin [38].

Different concentrations of inorganic phosphate and trace metals are known to affects the yield of delta-endotoxin. Tokcaer et al. [39] indicated that K2HPO4 levels were critical for effective synthesis of crystal toxin. Özkan et al. [29] also, reported that the highest yield of both Cry4Ba and Cry11Aa were obtained at 50–100 mM concentration of K2HPO4. We also determined that K2HPO4, KH2PO4, and MgSO4 levels were important factors for optimum delta-endotoxin production (Table 8). On the other hand, Khedher et al. [36] showed that K2HPO4, KH2PO4, and MgSO4 had no significant effect on delta-endotoxin synthesis using Plackett-Burman design.

Conclusion

Cultural conditions and nutritional requirements show differences for each strain of B. thuringiensis. So, Taguchi’s experimental design was used for optimization of delta-endotoxin production from a local isolate of B. thuringiensis strain Se13 in this study. In view of the results obtained in the study showed that incubation period, incubation temperature, initial pH, concentrations of the medium components significantly affected delta-endotoxin production. The delta-endotoxin yield was elevated to 1542.25 μg mL−1 when the factors were adjusted to best level for endotoxin production. This study sets an example for the application of the Taguchi method for development of biological processes.

Award Identifier / Grant number: The Scientific Research and Project Coordinator in Karadeniz Technical University

Award Identifier / Grant number: FHD-2017-5778

Funding statement: This research was supported by The Scientific and Technological Research Council of Turkey, Funder Id: 10.13039/501100004410 (2211-C) and The Scientific Research and Project Coordinator in Karadeniz Technical University, Funder Id: 10.13039/501100004045 (FHD-2017-5778).

References

1. Rosas-García NM. Biopesticide production from Bacillus thuringiensis: an environmentally friendly alternative. Recent Pat Biotechnol 2009;3:28–36.10.2174/187220809787172632Search in Google Scholar PubMed

2. Bravo A, Likitvivatanavong S, Gill SS, Soberón M. Bacillus thuringiensis: a story of a successful bioinsecticide. Insect Biochem Mol Biol 2011;41:423–31.10.1016/j.ibmb.2011.02.006Search in Google Scholar PubMed PubMed Central

3. Alam MJ, Hilbeck A, Nicolopoulou-Stamati P, Maipas S, Kotampasi C, Stamatis P, et al. Chemical pesticides and human health: the urgent need for a new concept in agriculture. Front Public Heal 2016;4:148.Search in Google Scholar

4. Igbedioh SO. Effects of agricultural pesticides on humans, animals, and higher plants in developing countries. Arch Environ Heal Int J 1991;46:218–24.10.1080/00039896.1991.9937452Search in Google Scholar PubMed

5. Keikotlhaile BM, Spanoghe P, Steurbaut W. Effects of food processing on pesticide residues in fruits and vegetables: a meta-analysis approach. Food Chem Toxicol 2010;48:1–6.10.1016/j.fct.2009.10.031Search in Google Scholar PubMed

6. Weber J, Halsall CJ, Teixeira C, Small J, Solomon K, Hermanson M, et al. Endosulfan, a global pesticide: a review of its fate in the environment and occurrence in the Arctic. Sci Total Environ 2010;408:2966–84.10.1016/j.scitotenv.2009.10.077Search in Google Scholar PubMed

7. Mazid S, Ch Rajkhowa D. A review on the use of biopesticides in insect pest management paper subtitle: biopesticides – a safe alternative to chemical control of pests. Int J Sci Adv Technol 2011;1:169–78.Search in Google Scholar

8. Xu C, Wang BC, Yu Z, Sun M. Structural insights into Bacillus thuringiensis Cry, Cyt and parasporin. Toxins 2014;6: 2732–70.10.3390/toxins6092732Search in Google Scholar PubMed PubMed Central

9. Prabakaran G, Hoti SL. Influence of amino nitrogen in the culture medium enhances the production of delta-endotoxin and biomass of Bacillus thuringiensis var. israelensis for the large-scale production of the mosquito control agent. J Ind Microbiol Biotechnol 2008;35:961–5.10.1007/s10295-008-0370-5Search in Google Scholar PubMed

10. Kwalimwa D. Optimization of growth conditions of Bacillus thuringiensis isolates from various sources in Kenya and toxicity assays of their delta-endotoxin against Chilo partellus. Master’s thesis, Jomo Kenyatta University of Agriculture and Technology, 2012.Search in Google Scholar

11. Hoa NT, Chinh TT, Thi D, Anh M, Binh ND, Thi L, et al. Optimization of fermentation medium compositions from dewatered wastewater sludge of beer manufactory for Bacilus thuringiensis delta endotoxin production. Am J Agric For 2014;2:219–25.10.11648/j.ajaf.20140205.12Search in Google Scholar

12. Makowski K, Matusiak K, Borowski S, Bielnicki J, Tarazewicz A, Maroszyńska M, et al. Optimization of a culture medium using the Taguchi approach for the production of microorganisms active in odorous compound removal. Appl Sci 2017;7:56.10.3390/app7080756Search in Google Scholar

13. Pundir R, Chary GH, Dastidar MG. Application of Taguchi method for optimizing the process parameters for the removal of copper and nickel by growing Aspergillus sp. Water Resour Ind 2016. doi:10.1016/J.WRI.2016.05.001.10.1016/J.WRI.2016.05.001Search in Google Scholar

14. Karna SK, Sahai R. An overview on Taguchi Method. Int J Eng Math Sci 2012;1:11–8.Search in Google Scholar

15. Karna SK, Singh RV, Sahai R. Application of Taguchi Method in Indian industry. Proceedings of the National Conference on Trends and Advances, Haryana, Faridabad: YMCA, University of Science & Technology, 2012:718–22.Search in Google Scholar

16. Quazi TZ, More P, Sonawane V. A case study of Taguchi Method in the optimization of turning parameters. Int J Emerg Technol Adv Eng 2013;3:622–6.Search in Google Scholar

17. Fei NC, Mehat NM, Kamaruddin S. Practical applications of Taguchi Method for optimization of processing parameters for plastic injection moulding: a retrospective review. ISRN Ind Eng 2013;2013:462174.10.1155/2013/462174Search in Google Scholar

18. Rosa JL, Robin A, Silva MB, Baldan CA, Peres MP. Electrodeposition of copper on titanium wires: Taguchi experimental design approach. J Mater Process Technol 2009;209:1181–8.10.1016/j.jmatprotec.2008.03.021Search in Google Scholar

19. El-Bendary MA, Abo Elsoud MM, Mohamed SS, Hamed SR. Optimization of mosquitocidal toxins production by Lysinibacillus sphaericus under solid state fermentation using statistical experimental design. Acta Biol Szeged 2016;60:57–63.Search in Google Scholar

20. Saravanan D, Prakash AA, Jagadeeshwaran D, Nalankilli G, Ramachandran T, Prabakaran C. Optimization of thermophile Bacillus licheniformis α-amylase desizing of cotton fabrics. Indian J Fibre Text Res 2011;36:253–8.Search in Google Scholar

21. Sutterfield JS, McKinley-Floyd LA. Using Taguchi Methods in a marketing study to determine features for a smartphone. Acad Mark Stud J 2012;16:53.Search in Google Scholar

22. Selden PH. Sales process engineering: a personal workshop. Milwaukee, Wisconsin: ASQC Quality Press, 1997.Search in Google Scholar

23. Shukla S, Goyal A. Development of efficient fermentation process at bioreactor level by Taguchi’s Orthogonal Array Methodology for enhanced dextransucrase production from Weissella confusa Cab3. Adv Microbiol 2012;2:277–83.10.4236/aim.2012.23033Search in Google Scholar

24. Muhammad SA, Ahmed S, Ismail T, Hameed A. Taguchi’s experimental design for optimizing the production of novel thermostable polypeptide antibiotic from Geobacillus pallidus SAT4. Pak J Pharm Sci 2014;27:11–23.Search in Google Scholar

25. Chenthamarakshan A, Parambayil N, Miziriya N, Soumya PS, Lakshmi MS, Ramgopal A, et al. Optimization of laccase production from Marasmiellus palmivorus LA1 by Taguchi method of design of experiments. BMC Biotechnol 2017;17:12.10.1186/s12896-017-0333-xSearch in Google Scholar

26. Zouari N, Dhouib A, Ellouz R, Jaoua S. Nutritional requirements of a strain of Bacillus thuringiensis subsp. kurstaki and use of gruel hydrolysate for the formulation of a new medium for δ-endotoxin production. Appl Biochem Biotechnol 1998;69:41–52.10.1007/BF02786020Search in Google Scholar

27. Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 1976;72:248–54.10.1016/0003-2697(76)90527-3Search in Google Scholar

28. Ennouri K, Ben Khedher S, Jaoua S, Zouari N. Correlation between delta-endotoxin and proteolytic activities produced by Bacillus thuringiensis var. kurstaki growing in an economic production medium. Biocontrol Sci Technol 2013;23:756–67.10.1080/09583157.2013.791364Search in Google Scholar

29. Özkan M, Dilek FB, Yetis Ü, Özcengiz G. Nutritional and cultural parameters influencing antidipteran delta-endotoxin production. Res Microbiol 2003;154:49–53.10.1016/S0923-2508(02)00006-2Search in Google Scholar

30. Yousten AA, Wallis DA, Singer S. Effect of oxygen on growth, sporulation, and mosquito larval toxin formation by Bacillus sphaericus 1593. Curr Microbiol 1984;11:175–8.10.1007/BF01567345Search in Google Scholar

31. Lacey LA. Production and formulation of Bacillus sphaericus. Mosq News 1984;44:153–9.Search in Google Scholar

32. Morris ON, Converse V, Kanagaratnam P, Davies JS. Effect of cultural conditions on spore-crystal yield and toxicity of Bacillus thuringiensis subsp aizawai (HD133). J Invertebr Pathol 1996;67:129–36.10.1006/jipa.1996.0020Search in Google Scholar

33. Zou H, Ding S, Zhang W, Yao J, Jiang L, Liang J. Study on influence factors in Bacillus thuringiensis production by semi-solid state fermentation using foodwaste. Procedia Environ Sci 2016;31:127–35.10.1016/j.proenv.2016.02.018Search in Google Scholar

34. Içgen Y, Içgen B, Özcengiz G. Regulation of crystal protein biosynthesis by Bacillus thuringiensis: I. Effects of mineral elements and pH. Res Microbiol 2002;153:599–604.10.1016/S0923-2508(02)01367-0Search in Google Scholar

35. Içgen Y, Içgen B, Ozcengiz G. Regulation of crystal protein biosynthesis by Bacillus thuringiensis: II. Effects of carbon and nitrogen sources. Res Microbiol 2002;153:605–9.10.1016/S0923-2508(02)01366-9Search in Google Scholar

36. Ben Khedher S, Jaoua S, Zouari N. Application of statistical experimental design for optimisation of bioinsecticides production by sporeless Bacillus thuringiensis strain on cheap medium. Brazilian J Microbiol 2013;44:927–33.10.1590/S1517-83822013000300043Search in Google Scholar PubMed PubMed Central

37. Ennouri K, Ayed RB, Hassen HB, Mazzarello M, Ottaviani E. Experimental design and Bayesian Networks for enhancement of delta-endotoxin production by Bacillus thuringiensis. Acta Microbiol Immunol Hung 2015; 62:379–92.10.1556/030.62.2015.4.3Search in Google Scholar PubMed

38. Farrera RR, Pérez-Guevara F, de la Torre M. Carbon: nitrogen ratio interacts with initial concentration of total solids on insecticidal crystal protein and spore production in Bacillus thuringiensis HD-73. Appl Microbiol Biotechnol 1998;49: 758–65.10.1007/s002530051243Search in Google Scholar

39. Tokcaer Z, Bayraktar E, Mehmetolu Ü, Özcengiz G, Alaeddinolu NG. Response surface optimization of antidipteran delta-endotoxin production by Bacillus thuringiensis subsp. israelensis HD 500. Process Biochem 2006;41:350–5.10.1016/j.procbio.2005.02.030Search in Google Scholar

Received: 2017-12-22
Accepted: 2018-02-27
Published Online: 2018-07-25

©2018 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 7.2.2023 from https://www.degruyter.com/document/doi/10.1515/tjb-2017-0364/html
Scroll Up Arrow