Multiresponse analysis of microbiological parameters affecting the production of pectolytic enzymes by Aspergillus niger: a statistical view

Abstract: Pectinases are a complex group of enzymes that degrade various pectic substances present in plant tissues. Pectinases have potential applications in fruit, paper and textile industries. Apart from these industrial applications, these enzymes possess biological importance in protoplast fusion technology and plant pathology. Since applications of pectinases in various fields are widening, it is important to understand the nature and properties of these enzymes for efficient and effective usage. For the past few years, vigorous research has been carried out on isolation and characterization of pectinases. New affinity matrices with improved characteristics and affinity-precipitation techniques have been developed for purification of pectinases. Recently much attention has been focused on chemical modification of pectinases and their catalytic performance by various researchers. These studies are helpful in determining key amino acid residues responsible for substrate binding, catalytic action, and physico-chemical environmental conditions for maximum hydrolysis. This short review highlights progress on purification and understanding the biochemical aspects of microbial pectinases.

Abstract: Radial basis function (RBF) artificial neural network (ANN) and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (pH, temperature, inoculum volume) for extracellular protease production from a newly isolated Pseudomonas sp. The optimum operating conditions obtained from the quadratic form of the RSM and ANN models were pH 7.6, temperature 38 °C, and inoculum volume of 1.5 with 58.5 U/ml of predicted protease activity within 24 h of incubation. The normalized percentage mean squared error obtained from ANN and RSM models were 0.05 and 0.1%, respectively. The results demonstrated an higher prediction accuracy of ANN compared to RSM. This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for fermentation monitoring and control.

Abstract: A two-step optimization procedure using central composite design with four factors (concentrations of maltrin and corn steep liquor (CSL), agitation speed and inoculation ratio) was used in order to investigate the effect of these parameters on the polygalacturonase (PG) enzyme activity, mycelia growth (biomass) and morphology (pellet size) of Aspergillus sojae ATCC 20235. According to the results of response surface methodology (RSM), initial concentrations of maltrin and CSL and agitation speed were significant (p < 0.05) on both PG enzyme production and biomass formation. As a result of this optimization, maximum PG activity (13.5 U/ml) was achievable at high maltrin (120 g/l), at low CSL (0 g/l), high agitation speed (350 rpm) and high inoculation ratio (2 × 107 total spore). Similarly, maximum biomass (26 g/l) could be obtained under the same conditions with only the difference for higher level of CSL requirement. The diameter of pellets in all optimization experiments ranged between 0.05 and 0.76 cm. The second optimization step improved the PG activity by 74% and the biomass by 40%.

Abstract: A comparative evaluation of three Aspergillus species according to their pectinase production in solid-state fermentation was performed. Solid-state fermentation offers several potential advantages for enzyme production by fungal strains. Utilization of agricultural by-products as low-cost substrates for microbial enzyme production resulted in an economical and promising process. The pectinolytic enzyme activities of two Aspergillus sojae strains were compared to a known producer, Aspergillus niger IMI 91881, and to A. sojae ATCC 20235, which was re-classified as Aspergillus oryzae. Evaluation of polymethylgalacturonase and polygalacturonase activity was performed as well as exo- vs. endo-enzyme activity in the crude pectinase enzyme-complex of the mentioned strains. Furthermore, a plate diffusion assay was applied to determine the presence and action of proteases in the crude extracts. A. sojae ATCC 20235 with highest polymethylgalacturonase activity and highest polygalacturonase activity both exo- and endo-enzyme activity, is a promising candidate for industrial pectinase production, a group of enzymes with high commercial value, in solid-state fermentation processes. Beside the enzymatic assays a protein profile of each strain is given by SDS-PAGE electrophoresis and in addition species-specific zymograms for pectinolytic enzymes were observed, revealing the differences in protein pattern of the A. sojae strains to the re-classified A. oryzae.

Abstract: The effect of solid substrates, inoculum and incubation time were studied using response surface methodology (RSM) for the production of polygalacturonase enzyme and spores in solid-state fermentation using Aspergillus sojae ATCC 20235. Two-stage optimization procedure was applied using D-optimal and face-centered central composite design (CCD). Crushed maize was chosen as the solid substrate, for maximum polygalacturonase enzyme activity based on D-optimal design. Inoculum and incubation time were determined to have significant effect on enzyme activity and total spore (p < 0.01) based on the results of CCD. A second order polynomial regression model was fitted and was found adequate for individual responses. All two models provided an adequate R2 of 0.9963 (polygalacturonase) and 0.9806 (spores) (p < 0.001). The individual optimum values of inoculum and incubation time for maximum production of the two responses were 2 × 107 total spores and 5–6 days. The predicted enzyme activity (30.55 U/g solid) and spore count (2.23 × 107 spore/ml) were very close to the actual values obtained experimentally (29.093 U/g solid and 2.31 × 107 spore/ml, respectively). The overall optimum region considering the two responses together, overlayed with the individual optima. Solid-state fermentation provided 48% more polygalacturonase activity compared to submerged fermentation under individually optimized conditions.

Abstract: The work described is the result of a study extending over the past few years by a chemist and a statistician. Development has come about mainly in answer to problems of determining optimum conditions in chemical investigations, but we believe that the methods will be of value in other fields where experimentation is sequential and the error fairly small.

Abstract: Introduction to response surface methodology matrix algebra, least squares, the analysis of variance, and principles of experimental design first-order models and designs second-order models and designs determining optimum conditions methods of estimating response surfaces that rival least squares based on the integrated mean squared error criterion analysis of multiresponse experiments response surface models with block effects mixture designs and analyses nonlinear response surface models Taguchi's robust parameter design additional topics and some directions for future research. Appendix: solutions to selected exercises.

Abstract: Introduction to response surface methodology matrix algebra, least squares, the analysis of variance, and principles of experimental design first-order models and designs second-order models and designs determining optimum conditions methods of estimating response surfaces that rival least squares based on the integrated mean squared error criterion analysis of multiresponse experiments response surface models with block effects mixture designs and analyses nonlinear response surface models Taguchi's robust parameter design additional topics and some directions for future research. Appendix: solutions to selected exercises.

Abstract: Suppose that a relationship $\eta = \varphi(\xi_1, \xi_2, \cdots, \xi_k)$ exists between a response $\eta$ and the levels $\xi_1, \xi_2, \cdots, \xi_k$ of $k$ quantitative variables or factors, and that nothing is assumed about the function $\varphi$ except that, within a limited region of immediate interest in the space of the variables, it can be adequately represented by a polynomial of degree $d$. A $k$-dimensional experimental design of order $d$ is a set of $N$ points in the $k$-dimensional space of the variables so chosen that, using the data generated by making one observation at each of the points, all the coefficients in the $d$th degree polynomial can be estimated. The problem of selecting practically useful designs is discussed, and in this connection the concept of the variance function for an experimental design is introduced. Reasons are advanced for preferring designs having a "spherical" or nearly "spherical" variance function. Such designs insure that the estimated response has a constant variance at all points which are the same distance from the center of the design. Designs having this property are called rotatable designs. When such arrangements are submitted to rotation about the fixed center, the variances and covariances of the estimated coefficients in the fitted series remain constant. Rotatable designs having satisfactory variance functions are given for $d = 1, 2$; and $k = 2, 3, \cdots, \infty$. Blocking arrangements are derived. The simplification in the form of the confidence region for a stationary point resulting from the use of a second order rotatable design is discussed.