|Year : 2009 | Volume
| Issue : 1 | Page : 37-42
Statistical optimization of process parameters influencing the biotransformation of plant tannin into gallic acid under solid-liquid fermentation
Bibhu Prasad Panda1, Rupa Mazumder2, Rintu Banerjee3
1 Pharmaceutical Biotechnology Laboratory, Faculty of Pharmacy, Jamia Hamdard (Hamdard University), New Delhi - 110 062, India
2 School of Pharmacy and Technology Management, Narsee Monjee Institute of Management and Higher Studies (NMIMS University), V. L. Mehta Road, Vile Parle (West), Mumbai, India
3 Microbial Biotechnology and Downstreaming Laboratory, Department of Agricultural and Food Engineering, Indian Institute of Technology, Khargpur, India
|Date of Submission||20-Nov-2009|
|Date of Decision||05-Dec-2009|
|Date of Acceptance||10-Dec-2009|
|Date of Web Publication||23-Apr-2010|
Bibhu Prasad Panda
Pharmaceutical Biotechnology Laboratory, Faculty of Pharmacy, Jamia Hamdard (Hamdard University), New Delhi - 110 062
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Purpose : To optimize and produce gallic acid by biotransformation of plant tannin under solid-liquid fermentation. Materials and Methods : Optimization of different process parameters like temperature, relative humidity, pH of the liquid medium, fermentation period, volume of inoculum weight of substrate influencing gallic acid production from plant tannin were carried out by EVOP factorial method. Results : Maximum gallic acid yield of 93.29% was produced at 28ΊC, 70% relative humidity, pH 6, a 72-hour fermentation period, 3 ml inoculum volume, and 25 g substrate weight, from plant tannin under solid-liquid fermentation. Conclusion : Gallic acid (3, 4, 5-trihydroxy benzoic acid), an important organic acid for synthesis of propyl gallate and trimethoprim, was produced by solid-liquid fermentation using Rhizopus oryzae NRRL 21498. Combination of Evolutionary operation, factorial design, and response surface methodology was applied successfully to elucidate the effect of different process parameters influencing biotransformation of natural tannin (powdered chebulic myrobalan fruit) to gallic acid.
Keywords: Chebulic myrobalan fruit, evolutionary operation, gallic acid, rhizopus oryzae, response surface methodology, solid state fermentation
|How to cite this article:|
Panda BP, Mazumder R, Banerjee R. Statistical optimization of process parameters influencing the biotransformation of plant tannin into gallic acid under solid-liquid fermentation. J Pharm Bioall Sci 2009;1:37-42
|How to cite this URL:|
Panda BP, Mazumder R, Banerjee R. Statistical optimization of process parameters influencing the biotransformation of plant tannin into gallic acid under solid-liquid fermentation. J Pharm Bioall Sci [serial online] 2009 [cited 2019 Oct 22];1:37-42. Available from: http://www.jpbsonline.org/text.asp?2009/1/1/37/62685
Among the many industrial important organic acids, gallic acid (3, 4, 5-trihydroxy benzoic acid) plays a significant role for synthesis of propyl gallate and trimethoprim, particularly in the food and pharmaceutical industries. , Gallic acid is obtained by the microbial biotransformation of tannins, by enzyme tannin acyl hydrolase (tannase E.C. 126.96.36.199);  this enzyme is biosynthesized by a number of fungi mainly Rhizopus oryzae, Aspergillus foetidus, Aspergillus niger, Aspergillus awamori, and Penicillium variable,,,,,, and the biotransformation process is highly dependent on the environmental condition as it influences the fungal growth and the biosynthesis of the tannase enzyme. 
The response surface methodology (RSM) based on factorial experiments and the mathematical model for RSM is derived, which fits the experimental data, to calculate the optimal response of the system. RSM has the advantage over other optimization techniques, in that, interaction of two variables can be visualized by a surface plot. ,, It has been noted that in the recent past a number of literatures have published regarding optimization of 'n' variable biological experiments by evolutionary operation (EVOP) factorial design techniques, ,,, wherein, experiments are conducted according to the factorial design and interpretation of the results is carried out by the decision-making procedure of EVOP. However, a problem arises when the interaction effects are negative or positive and smaller than the error limit, and the magnitude of change in the mean effect is small. In that case according to the decision-making procedure of EVOP it is advisable to select a new search level without suggesting whether to minimize or maximize the parameter level, to achieve the desired goal 13. If we hybridize the advantage of EVOP factorial design techniques and RSM, it may provide a more effective decision-making procedure. Here, the experiments are designed based on factorial techniques and the results are analyzed by combining EVOP and RSM techniques.
The main aim of the present investigation is, therefore, to examine the combined effectiveness of EVOP factorial and RSM techniques for determining the interaction effect of process parameters, such as, temperature, relative humidity, pH of the liquid medium, fermentation period, volume of inoculum, and weight of the substrate, on the bioconversion of natural tannin (chebulic myrobalan fruits) to gallic acid.
| Materials and Methods|| |
Rhizopus oryzae (RO IIT RB-13 NRRL 21498) was isolated from the soil of the Indian Institute of Technology (IIT), Kharagpur campus, and used for bioconversion of tannin to gallic acid. This fungal strain was maintained in 2% malt extract agar slants  [Figure 1]a-f.
Fruits (chebulic myrobalan) of Terminalia chebula were collected from the forest of Orissa and dried at a temperature of 60Ί C for 96 hours in an oven, to remove free moisture. The dried fruits were then powered in a grinder and screened to 70 - 80 ΅m particle size. 
Pre-induced inoculums of the fungus Rhizopus oryzae were prepared using a modified Czapek-Dox medium containing 2% tannic acids as the sole carbon source. 
Experimental set up (fermentation)
The solid substrate (powdered chebulic myrobalan fruits) and an aqueous salt mixture containing sodium nitrate 2.5 g/L, potassium dihydrogen phosphate 1 g/L, magnesium sulfate 0.5 g/L, and potassium chloride 0.5 g/L, respectively, were placed in a float, in a vessel of the GROWTEK bioreactor.  After sterilization, the solid medium was inoculated, with induced inoculum containing 10 to 12 fungal propagules of Rhizopus oryzae per milliliter of medium, and incubated in a humidity camber for bioconversion of natural tannin to gallic acid under different process conditions, as per the experimental design.
Gallic acid extraction
Gallic acid from the fermented medium was extracted according to the procedure given by Kar et al. 
Design of experiments and analysis of experimental results
The experimental design was formulated according to the factorial design techniques for six different environmental parameters at different randomly selected levels [Table 1] and all experiments were replicated for two cycles, cycle I and cycle II, to minimize standard deviations and error limits. The difference in gallic acid production between the two cycles and average gallic acid production were calculated to estimate the effects and error limits. The magnitude of the effects and the change in mean effects and error limits (of average, effects, and change in mean effects) were calculated according to the relationship given in the literature,  and the optimization procedure was facilitated by the fitting of an empirical polynomial equation to the experimental results, where the coefficients in the equation were related to the effects and interaction of the different process parameters. 
The analysis of the results were carried out as per the decision-making procedure of the evolutionary operation  and three-dimensional response surfaces were constructed for the fitted polynomial equation by software MATLAB TM of Math Work Inc. USA.
| Results|| |
The experimental conditions of six different process parameters, such as, temperature (X 1 ), relative humidity (X 2 ), pH of the liquid medium (X 3 ), fermentation period (X 4 ), volume of inoculum (X 5 ), and weight of the substrate on float (X 6 ) for first set (set I), second set (set II), and gallic acid yields are shown in [Table 2] and [Table 3]. Calculation of the effects and error limits are presented in [Table 4] and [Table 5], respectively, for the first and second sets of experiments.
The individual and interactive effects of six different process parameters in the first set of experiments (Set I) were calculated according to the evolutionary techniques [Table 4] and fitted into the following empirical polynomial equation for gallic acid production (in the coded factor):
% yield of gallic acid = 71.577-0.882X 1 +1.632X 2 + 1.118X 3 +2.962X 4 +1.559X 5 + 3.136X 6 -3.024X 1 X 2 -0.594X 1 X 3 -2.113X 1 X 4 -1.25X 1 X 5 +0.309X 1 X 6 +0.393X 2 X 3 -1.197X 2 X 4 -0.287X 2 X 5 +0.147X 2 X 6 0.468X 3 X 4 -0.826X 3 X 5 +1.368X 3 X 6 +0.925X 4 X 5 + 0.162X 4 X 6 +0.862X 5 X 6 -0.457X 1 X 2 X 3 +0.353X 1 X 2 X 4 -0.894X 1 X 2 X 5 +0.635X 1 X 2 X 6 +0.137X 1 X 3 X 4 +0.487X 1 X 3 X 5 + 0.296X 1 X 3 X 6 +0.171X 1 X 4 X 5 1.225X 1 X 4 X 6 + 0.421X 1 X 5 X 6 -0.576X 2 X 3 X 4 +1.498X 2 X 3 X 5 -0.497X 2 X 3 X 6 +0.175X 2 X 4 X 5 -0.006X 2 X 4 X 6 -0.184X 2 X 5 X 6 +0.363X 3 X 4 X 5 +0.254X 3 X 4 X 6 +0.279X 3 X 5 X 6 +0.145X 4 X 5 X 6 + 0.073X 1 X 2 X 3 X 4 +0.617X 1 X 2 X 3 X 5 -0.065X 1 X 2 X 3 X 6 -0.632X 1 X 2 X 4 X 5 +0.670X 1 X 2 X 4 X 6 -1.051X 1 X 2 X 5 X 6 - 0.092X 1 X 4 X 5 X 6 -0.882X 1 X 3 X 4 X 5 + 0.647X 1 X 3 X 4 X 6 -0.248X 1 X 3 X 5 X 6 + 0.322X 2 X 3 X 4 X 5 0.580X 2 X 3 X 4 X 6 -0.042X 2 X 3 X 5 X 6 -0.017X 2 X 4 X 5 X 6 + 0.973X 3 X 4 X 5 X 6 -0.770X 1 X 2 X 3 X 4 X 5 -1.143X 1 X 2 X 3 X 4 X 6 +1.039X 1 X 2 X 3 X 5 X 6 + 0.839X 1 X 2 X 4 X 5 X 6 -0.849X 1 X 3 X 4 X 5 X 6 -1.368X 2 X 3 X 4 X 5 X 6 - 0.0005X 1 X 2 X 3 X 4 X 5 X 6
Effects of different process parameters were elucidated from the response surface plots, [Figure 1]a-f, which was constructed for the fitted polynomial equation. Each individual parameter was analyzed from the response surface plots and the decision-making procedure of the evolutionary operation technique, and it was found that gallic acid production increased when there was a decrease in temperature and increase in relative humidity, pH of the liquid medium, fermentation period, volume of inoculum, and weight of the substrate on float.
The next set of experiments (Set II) were conducted by selecting the best condition of the first set as the new search level for the second set, keeping the temperature at 28Ί C, relative humidity at 70%, pH of the liquid medium at 6, fermentation period at 72 hours, volume of inoculum at 3 ml, and weight of the substrate on float at 25 g for the initial search level A 1, and A 34 [Table 3] and analysis of results [Table 5] show that the effects of process parameters such as relative humidity, pH of the liquid medium, volume of inoculum, and weight of the substrate on float are smaller then the error limit. However, the effects of parameters, such as, temperature and fermentation period are positive and larger than the error limit, while change in the mean effect was large and negative. However, in set II there was no increase in gallic acid production under any experimental conditions, with respect to the initial search levels, with a gallic acid yield of 93.29%.
| Discussion|| |
The main objective of this investigation was to hybrid the EVOP factorial design with response surface methodology, to improve the decision-making procedure of the evolutionary operation technique. Here all the experiments were conducted according to the factorial design, and analysis of the results to select a new search level for the next set of experiments was done by combining the decision-making procedure of EVOP factorial design and the response surface methodology.
In the first set (Set I) the effect of temperature was negative and larger than the error limit, while change in the mean effect was small, in which case reducing the parameter level would help to maximize gallic acid production, which was further concluded from the response surfaces [Figure 1]a and f. In case of fermentation periods and weight of the substrate on the float, the effects were positive and larger than the error limits, in which case increasing the parameter levels would help to maximize gallic acid production, which was further concluded from the response surfaces [Figure 1]c-f. The effects of parameters such as, relative humidity, pH of the liquid medium, and volume of inoculum were positive and smaller than the error limits and the change in mean effect was also small, in which case according to the decision-making procedure of the evolutionary operation technique it was advisable to select a new search region and start a new phase of experiments.  In Evop factorial deign technique next set of experiments were performed, without suggesting whether to minimize or maximize the parameter levels to increase the gallic acid yield but from response surfaces [Figure 1]a-e there was a sharp indication that decreasing relative humidity and increasing pH of the liquid medium and volume of the inoculum would help to increase the gallic acid yield. The next set of experiments (Set II) were conducted based on the results of the set I experiments and there was no increase in gallic acid production under any experimental conditions with respect to the initial search levels (A 1 and A 34 ) of set II, that is, temperature at 28°C, relative humidity at 70%, pH of the liquid medium at 6, 72 hours fermentation period, volume of inoculum at 3 ml, and 25 g powdered chebulic myrobalan fruits on float of the solid state bioreactor, which yielded maximum gallic acid of 93.29 %, which was much higher than the gallic acid produced by Aspergillus niger Aa-20, Aspergillus awamori, Penicillium variable, and Rhizopus oryzae reported by Cueto et al. (2007), Seth and Chand (2000), Saxena and Saxena (2004), and Kar et al. (2002), respectively, under the monoculture mode. ,,,
In the present study it can be concluded that advantages of the evolutionary operation and response surface methodology can be combined to discover a new search level, to provide much better decision-making procedures for the existing EVOP factorial design technique, for optimizing the biological processes having n variable parameters.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]