Statistical analysis on optimisation of Microbial Keratinase enzyme screened from Tirumala and Tirupati soil samples

Suneetha Vuppu* and Raj Vuppu
School of Biosciences and Technology, VIT, Tamilnadu, India and Dahlia Park, Singapore
Corresponding Author:Suneetha Vuppu and Raj Vuppu, Email: [email protected], [email protected]
Received:03 July 2012 Accepted: 01 August 2012
Citation: Suneetha Vuppu and Raj Vuppu “Statistical analysis on optimisation of Microbial Keratinase enzyme screened from Tirumala and Tirupati soil samples” Int. J. Drug Dev. & Res., July-September 2012, 4(3): 253-258. doi: doi number
Copyright: © 2010 IJDDR, Suneetha Vuppu and Raj Vuppu et al. This is an open access paper distributed under the copyright agreement with Serials Publication, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Due to the availability of powerful expression systems in various microbes, the large scale commercial production of new and useful enzymes is becoming increasingly attractive (10.11), The development of microbial enzymes for commercial use is a specialized field which requires.Continuous screening of new and improved strains from potential sources.Scaling up of enzyme production by optimizing conditions of fermentation(1,2,3,4,12,14) .Strain improvement of the isolated strains by classics or by recombinant methods of genetic manipulation. Statistical analysis, Down stream purification technology and formulation of enzymes(12,13,15). The biochemical heterogenicity and ecological diversity of microbes including actinomycetes and their exceptional capacity of producing secondary metabolites make them an obvious target as the source of new enzymes. They are considered potential sources of proteases with typical substrate specificities such as keratinases which attack the normally unreactive keratin(7,8). Microbial keratinases play a key role in leather processing, (5),bioconversion of keratinous waste materials to feed supplement, or source of amino acids. Thus keratinase producing organisms have both economic and ecological value (6,7,8,14). Studies on statistical analysis of keratinase have not been fully exploited hence it is addressed as the primary objective of the present study.


Keratinase, Tirumala, Keratin, Statistical analysis


Tirumala is being visited by thousands of pilgrims every day from all parts of India as well as whole world, all round the year to worship Lord Venkateswara. (5.6.7) The place has relatively high deposits of keratin as compared to any other place as most of the pilgrims coming to Tirumala consider head tonsuring as the most sacred act of offering to God. Further the prevailing climatic conditions also facilitate the growth of thermo-tolerant organisms.(1,2,3,4,9) Thus this place was presumed to provide good enrichment for potential keratinophilic and keratinolytic organisms and soil samples collected from various locations in Tirumala Hills and Tirupati were analyzed to isolate keratinolytic organisms. Statistical analysis was carried out through SPSS 10.1 package


Studies of the comparison of keratinase production in three different media showed that the keratinase production was highest in production media(8,9,10,11). Hence production media was selected as the medium of choice for optimization of nutritional as well as physical parameters, including type and concentration of carbon source, nitrogen source, effect of pH, temperature, agitation on keratinase production using the six test strains.
Statistical analysis: Statistical analysis was carried out through SPSS 10.1 package and p levels at 0.05 were considered as significant. Initially descriptive statistics was carried for the target variable keratinase enzyme production with respect to the effect of the type of carbon and nitrogen source, physical parameters of growth like pH, temperature, agitation, duration of the fermentation and the strains (17),. The enzyme production was not showing variation with respect to duration of fermentation in different strains as the keratinase production reached its maximum yield by 240-288 hrs. of fermentation in most of the cases. Hence, further analysis was confined to other variables only. Chi-square (c2) values strengthened the use of univariate analysis and logistic regression to know the interactions as well as relative importance of one variable over the other. For the analysis of variance highest keratinase enzyme activity was taken as dependent variable and nutrient source and type of strain as factors. For physical parameters like pH, temperature and agitation also, the keratinase yield was taken as dependent variable and pH and strain; temperature and strain; agitation and still conditions in relation to strains, were taken as factors (9,12,13,&17). The analysis was carried out independently for carbon source, substitution of nitrogen source, addition of nitrogen source, pH, temperature and agitation.
For the logistic regression, a multinominal full factorial model was fitted with type of sugar as dependant variable and keratinase enzyme activity as covariate. Subsequent controls of the type of strain in the model, did not alter the odds ratio and hence only keratinase enzyme activity was retained in the model. The factorial model was obtained for parameters like carbon source, substitution of nitrogen source, addition of nitrogen source, pH, temperature and agitation etc. A low yielding keratinase enzyme condition as specified in each case, was taken as reference in the analysis. From the logistic analysis, those variables with significant yielding (P<0.05) when compared to the reference were selected and applied for Tukeys harmonic mean comparison for identifying the theoretical combination that yielded maximum keratinase activity(16,17). The best combination was also studied for fermentation to check the relevance of theoretical model taking into account the cost factor of the nutrients also.


Effect of the carbon source on production of Keratinase: Galactose, the carbon source in production media was replaced with equivalent concentration of different carbon sources, to study the influence of carbon source on keratinase production. The results and corresponding statistical analysis are summarized in Tables -1-8. The results indicate that glucose supported highest keratinase production with the test strains followed by galactose and starch. Lactose and glycerol were next in order.
The ANOVA and other statistical analysis revealed that the type of sugar as well as the type of strain and their interactions were significant. Glucose gained a odds ratio of 2.022, followed by 1.616 and 1.392 by galactose and starch respectively (Table-1a & 1b).
Designing of fermentation media for optimum production of keratinase: The carbon and nitrogen sources which were showing significance in the logistic regression model were grouped by descriptive statistics for keratinase activity (Table-3). Further, the Tukey’s harmonic mean for sample size with alpha level significant at 0.05, was carried out and categorization is shown in Table- 4. The yielding of keratinase in the subset one was nearly identical for starch and galactose among carbon sources and (NH4)2 HPO4 as nitrogen source. Similarly in the subset 2, groundnut cake, and soyabean meal among nitrogen sources and glucose among carbon source are almost identical. The results of comparison of the designed fermentation media based on the statistical analysis on keratinase production taking KLH104 and KLF16 strains showed that the media with glucose as carbon source and soyabean meal as nitrogen source was only marginally better than media with starch as carbon source and soyabean meal as nitrogen source


The production and activity of enzymes are influenced by various physical and nutritional factors. In the present study, the optimum nutritional and physical conditions for production of keratinases were determined for the six isolates, identified to be producing more than 10 KU/ml in the initial study in production media
The univariate ANOVA of the data on the effect of carbon source on keratinase production revealed that the types of sugar as well as the type of strain and their interactions were significant statistically. The maximum keratinase enzyme activity in each of the cases found significant variance with the type of sugar as given by the corresponding F values (Table - 1a & 1b). In order to find out the relative yield of keratinase with respect to sugars, a multinominal logistic model was carried out taking type of sugar as dependent variable and enzyme activity as covariate. Glycerol the carbon source in basal media was taken as reference to derive the odds ratio. The results indicated that the production of keratinase was 2.022 folds higher followed by galactose and starch which were 1.616 and 1.392 folds more respectively when compared with glycerol, and were significant. Mannitol, lactose and maltose were insignificant on enzyme yield as compared to reference. When the type of strain was included in the model as covariate and the odds ratio value was not altered significantly and hence the results are not shown. No significant difference in the keratinase yield with the interaction of strains and fermentation duration was observed as most of the strains reached a maximum by 240- 288 hrs of incubation, after which there was a decrease in the production of enzyme.Multinominal logistic models and descriptive statistics explains the clearly the studies regarding keratinase enzyme production and paved the further improvement in the microbial keratinase studies.


We want to express our sincere thanks to Statistics professors of Singapore and SV university ,Tirupati for helping us regarding SPSS and ANOVA soft ware training etc and our sincere thanks and gratitude to Respected and beloved Chancellor Dr.G.Viswanathan,VIT for his support and encouragement.

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