通过箱形设计和人工神经网络研究绿色木霉羧甲基纤维素酶对麦秸转化为可发酵糖的影响

R. Nelofer, M. Nadeem, M. Irfan, Q. Syed, Sara Nawaz, A. Tahir
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引用次数: 2

摘要

本研究通过Box-Behnken设计对羧甲基纤维素酶的深层发酵进行了表征,并对糖化过程进行了优化。绿木霉产酶的最适pH为5℃,最适温度为50℃。以羧甲基纤维素为底物的粗酶Km和Vmax分别为1.5143µM和0.9253µM/min。以pH (X1)、培养温度(X2)和底物浓度(X3)为变量,采用Box Behnken设计和人工神经网络(ANN)对纤维素含量83%的麦秸糖化进行优化。结果表明,所建立的模型是显著的,这些参数的二次效应显著影响糖的产量。在RSM预测的pH(6.6)、孵育温度(50 oC)和底物浓度(6.2%)条件下,最大糖产量(28.87 mg/ml),而ANN预测的pH、温度和底物浓度分别为5、50 oC和3.5%。在这些水平下预测糖浓度为30.72 mg/mL。RSM和ANN预测水平下的观测值分别为25.52和29.95 mg/mL。
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CONVERSION OF WHEAT STRAW INTO FERMENTABLE SUGARS USING CARBOXYMETHYL CELLULASE FROM TRICHODERMA VIRIDE THROUGH BOX-BEHNKEN DESIGN AND ARTIFICIAL NEURAL NETWORK
In this study, carboxymethyl cellulase was produced in submerged fermentation characterized and saccharification was optimized through Box-Behnken design. The optima pH and temperature of enzyme produced by Trichoderma viride were 5 and 50 oC, respectively. The crude enzyme had Km and Vmax values of 1.5143 µM and 0.9253µM/min, respectively, using carboxymethyl cellulose as substrate respectively. Three variables including pH (X1), incubation temperature (X2) and substrate concentration (X3) with three levels were used to optimize saccharification of wheat straw having 83% cellulose content using Box Behnken design and Artificial Neural Network (ANN). Results reveal that the proposed model was significant and quadratic effect of these parameters significantly affects the sugar production. Maximum sugar production (28.87 mg/ml) was predicted at RSM predicted levels of pH (6.6), incubation temperature (50 oC) and (6.2%) substrate concentration, while the levels predicted for pH, temperature and substrate concentration were 5, 50 oC and 3.5 %, respectively, by ANN. The predicted sugar concentration at these levels was 30.72 mg/mL. The observed values at the predicted levels of RSM and ANN were 25.52 and 29.95 mg/mL respectively.
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