A Comparative Study of New Aspergillus Strains for Proteolytic Enzymes Production by Solid State Fermentation

A comparative study of the proteolytic enzymes production using twelve Aspergillus strains previously unused for this purpose was performed by solid state fermentation. A semiquantitative and quantitative evaluation of proteolytic activity were carried out using crude enzymatic extracts obtained from the fermentation cultures, finding seven strains with high and intermediate level of protease activity. Biochemical, thermodynamics, and kinetics features such as optimum pH and temperature values, thermal stability, activation energy (E a), quotient energy (Q 10), K m, and V max were studied in four enzymatic extracts from the selected strains that showed the highest productivity. Additionally, these strains were evaluated by zymogram analysis obtaining protease profiles with a wide range of molecular weight for each sample. From these four strains with the highest productivity, the proteolytic extract of A. sojae ATCC 20235 was shown to be an appropriate biocatalyst for hydrolysis of casein and gelatin substrates, increasing its antioxidant activities in 35% and 125%, respectively.

Acid protease production by solid-state fermentation using Aspergillus oryzae MTCC 5341: Optimization of process parameters Isolation, characterization and optimization of culture parameters for production of an alkaline protease isolated from Aspergillus tamarii (2007) Srinu Babu, G. et al.
Screening of nutritional parameters for the production of protease from Aspergillus oryzae (2007) Wu, T.Y. et al.
Investigations on protease production by a wild-type Aspergillus terreus strain using diluted retentate of pre-filtered palm oil mill effluent (POME) as substrate (2006) Negi, S. et al.
Optimization of amylase and protease production from Aspergillus awamori in single bioreactor through EVOP factorial design technique (2006) Sunil Kumar, O. et al.
Studies on cultural conditions and nutritional parameters for the production of protease enzyme by Aspergillus oryzae (2006) Ramanathan, T. et al.
Alkaline protease production and optimization in estuary isolate of Aspergillus sp (2005) Te Biesebeke, R. et al.
Branching mutants of Aspergillus oryzae with improved amylase and protease production on solid substrates

Determination of water activity
For water activity determination, petri plate containing 3g of wheat bran wetted with Czapek-Dox at the maximum absorption capacity (moisture 475%) were placed jointly with different glycerol standard solutions in a hermetic chamber. After preparation, the system was incubated at 28°C for 7 day and the residual moisture was determined by gravimetric measure.

Glycerol standard solution
Water activity Water (g) Glycerol ( This procedure performs a multifactor analysis of variance for HI. It constructs various tests and graphs to determine which factors have a statistically significant effect on HI. It also tests for significant interactions amongst the factors, given sufficient data. The F-tests in the ANOVA table will allow you to identify the significant factors. For each significant factor, the Multiple Range Tests will tell you which means are significantly different from which others. The ANOVA table decomposes the variability of HI into contributions due to various factors. Since Type III sums of squares have been chosen, the contribution of each factor is measured having removed the effects of all other factors. The P-values test the statistical significance of each of the factors. Since 3 P-values are less than 0.05, these factors have a statistically significant effect on HI at the 95.0% confidence level. This table applies a multiple comparison procedure to determine which means are significantly different from which others. The bottom half of the output shows the estimated difference between each pair of means. An asterisk has been placed next to 49 pairs, indicating that these pairs show statistically significant differences at the 95.0% confidence level. At the top of the page, 6 homogenous groups are identified using columns of X's. Within each column, the levels containing X's form a group of means within which there are no statistically significant differences. The method currently being used to discriminate among the means is Fisher's least significant difference (LSD) procedure. With this method, there is a 5.0% risk of calling each pair of means significantly different when the actual difference equals 0. The ANOVA table partitions the variability in Activity into separate pieces for each of the effects. It then tests the statistical significance of each effect by comparing the mean square against an estimate of the experimental error. In this case, 3 effects have P-values less than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level.

Proteolytic extract from strain 4 Design of experiment matrix and activity results
The lack of fit test is designed to determine whether the selected model is adequate to describe the observed data, or whether a more complicated model should be used. The test is performed by comparing the variability of the current model residuals to the variability between observations at replicate settings of the factors. Since the P-value for lack-of-fit in the ANOVA table is greater or equal to 0.05, the model appears to be adequate for the observed data at the 95.0% confidence level.
The R-Squared statistic indicates that the model as fitted explains 84.5192% of the variability in Activity. The adjusted Rsquared statistic, which is more suitable for comparing models with different numbers of independent variables, is 79.6815%. The standard error of the estimate shows the standard deviation of the residuals to be 2.49975. The mean absolute error (MAE) of 1.90762 is the average value of the residuals. The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file. Since the P-value is greater than 5.0%, there is no indication of serial autocorrelation in the residuals at the 5.0% significance level. The ANOVA table partitions the variability in Activity into separate pieces for each of the effects. It then tests the statistical significance of each effect by comparing the mean square against an estimate of the experimental error. In this case, 5 effects have P-values less than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level.

Model and regression coeffs. for activity
The lack of fit test is designed to determine whether the selected model is adequate to describe the observed data, or whether a more complicated model should be used. The test is performed by comparing the variability of the current model residuals to the variability between observations at replicate settings of the factors. Since the P-value for lack-of-fit in the ANOVA table is greater or equal to 0.05, the model appears to be adequate for the observed data at the 95.0% confidence level.
The R-Squared statistic indicates that the model as fitted explains 96.5152% of the variability in Activity. The adjusted R-squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 94.9068%. The standard error of the estimate shows the standard deviation of the residuals to be 2.24351. The mean absolute error (MAE) of 2.50531 is the average value of the residuals. The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file. Since the P-value is less than 5.0%, there is an indication of possible serial correlation at the 5.0% significance level. Where the values of the variables are specified in their original units.

Optimized response
Optimum value = 48.921 (U/mL) Factor Low High Optimum pH 5.6 8.4 6.15718 Temperature 25.9 54.1 49.9589 This table shows the combination of factor levels which maximizes activity over the indicated region.

The StatAdvisor
The ANOVA table partitions the variability in Activity into separate pieces for each of the effects. It then tests the statistical significance of each effect by comparing the mean square against an estimate of the experimental error. In this case, 5 effects have P-values less than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level.
The lack of fit test is designed to determine whether the selected model is adequate to describe the observed data, or whether a more complicated model should be used. The test is performed by comparing the variability of the current model residuals to the variability between observations at replicate settings of the factors. Since the P-value for lack-of-fit in the ANOVA table is greater or equal to 0.05, the model appears to be adequate for the observed data at the 95.0% confidence level.
The R-Squared statistic indicates that the model as fitted explains 96.9378% of the variability in Activity. The adjusted R-squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 95.5245%. The standard error of the estimate shows the standard deviation of the residuals to be 1.86167. The mean absolute error (MAE) of 1.56887 is the average value of the residuals. The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file. Since the P-value is greater than 5.0%, there is no indication of serial autocorrelation in the residuals at the 5.0% significance level.

The StatAdvisor
The ANOVA table partitions the variability in Activity into separate pieces for each of the effects. It then tests the statistical significance of each effect by comparing the mean square against an estimate of the experimental error. In this case, 4 effects have P-values less than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level.
The lack of fit test is designed to determine whether the selected model is adequate to describe the observed data, or whether a more complicated model should be used. The test is performed by comparing the variability of the current model residuals to the variability between observations at replicate settings of the factors. Since the P-value for lack-of-fit in the ANOVA table is greater or equal to 0.05, the model appears to be adequate for the observed data at the 95.0% confidence level.
The R-Squared statistic indicates that the model as fitted explains 97.6047% of the variability in Activity. The adjusted R-squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 96.4992%. The standard error of the estimate shows the standard deviation of the residuals to be 4.01705. The mean absolute error (MAE) of 2.62582 is the average value of the residuals. The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file. Since the P-value is greater than 5.0%, there is no indication of serial autocorrelation in the residuals at the 5.0% significance level.