Department of Biotechnology, National Institute of Technology, Raipur, India.
Amity Institute of Biotechnology, Amity University Chhattisgarh, Raipur, India.
Prep Biochem Biotechnol. 2020;50(8):768-780. doi: 10.1080/10826068.2020.1737816. Epub 2020 Mar 20.
The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient () & mean squared error (MSE). The values of obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.
本研究展示了人工神经网络 (ANN) 和响应面法 (RSM) 作为预处理和酶解木质纤维素稻草的优化工具的比较分析。使用相关系数 () 和均方误差 (MSE) 评估了这两个过程的功效，即预处理和酶解。ANN 在训练、验证和测试后的 值分别为预处理的 1、0.9005 和 0.997，以及酶糖化的 0.962、0.923 和 0.9941。另一方面，RSM 得到的 值分别为纤维素回收率的 0.9965 和糖化效率的 0.9994。因此，ANN 和 RSM 共同成功地确定了稻草预处理和酶糖化的重要工艺条件。ANN 和 RSM 的误差百分比分别为纤维素回收率的 0.009 和 0.01，以及糖化效率的 0.004 和 0.005，这表明 ANN 在例证系统的非线性行为方面具有权威性。
