Abstract

Chitinase is responsible for the breaking down of chitin to N-acetyl-glucosamine units linked through (1–4)-glycosidic bond. The chitinases find several applications in waste management and pest control. The high yield with characteristics thermal stability of chitinase is the key to their industrial application. Therefore, the present work focuses on parameter optimization for chitinase production using fungus Thermomyces lanuginosus MTCC 9331. Three different optimization approaches, namely, response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) were used. The parameters under study were incubation time, pH and inoculum size. The central composite design with RSM was used for the optimization of the process parameters. Further, results were validated with GA and ANN. A multilayer feed-forward algorithm was performed for ANN, i.e., Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The ANN predicted values gave higher chitinase activity, i.e., 102.24 U/L as compared to RSM-predicted values, i.e., 88.38 U/L. The predicted chitinase activity was also closer to the observed data at these levels. The validation study suggested that the highest activity of chitinase as predicted by ANN is in line with experimental analysis. The comparison of three different statistical approaches suggested that ANN gives better optimization results compared to the GA and RSM study.

摘要

几丁质酶负责将几丁质分解为通过（1-4）-糖苷键连接的N-乙酰基-葡萄糖胺单元。几丁质酶在废物管理和害虫控制中发现了几种应用。几丁质酶具有热稳定性能的高产量是其工业应用的关键。因此，目前的工作集中在使用真菌嗜热霉菌（Thermomyces lanuginosus）的几丁质酶生产的参数优化上。MTCC9331。使用了三种不同的优化方法，即响应面方法（RSM），人工神经网络（ANN）和遗传算法（GA）。研究的参数是孵育时间，pH和接种量。使用带有RSM的中央复合设计来优化工艺参数。此外，结果用GA和ANN进行了验证。针对ANN执行了多层前馈算法，即Levenberg-Marquardt，贝叶斯正则化和缩放共轭梯度。与RSM预测值（即88.38 U / L）相比，ANN预测值提供了更高的几丁质酶活性（即102.24 U / L）。在这些水平上，预测的几丁质酶活性也更接近于观察到的数据。验证研究表明，ANN预测的几丁质酶的最高活性与实验分析一致。三种不同统计方法的比较表明，与GA和RSM研究相比，ANN提供了更好的优化结果。