School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi, India.
Prep Biochem Biotechnol. 2022;52(5):549-560. doi: 10.1080/10826068.2021.1972426. Epub 2021 Sep 16.
L-asparaginase has proven itself as a potential anti-cancer drug and in the mitigation of acrylamide formation in the food industry. In the present investigation, a novel utilization of niger () de-oiled cake as the sole source for the cost-effective production of L-asparaginase was evaluated and compared with different agro-substrates in solid-state fermentation. The substrate provided a favorable C/N content for the L-asparaginase production as evident from the chemical composition (CHNS analysis) of the substrate. The influential process parameters viz; autoclaving time, moisture content, temperature and pH were optimized and modeled using machine-learning based artificial neural network (ANN) and statistical-based response surface methodology (RSM). The maximum enzyme activity of 34.65 ± 2.18 IU/gds was observed at 30.3 min of autoclaving time, 62% moisture content, 30 °C temperature and 6.2 pH in 96 h. A 1.36 fold improvement in enzyme activity was observed on utilizing optimized parameters. In comparison with RSM, the ANN model showed superior prediction with a low mean squared error of 0.072, low root mean squared error of 0.268 and 0.99 value of regression coefficient. The present study demonstrates the novel utilization of inexpensive and readily available agro-industrial waste for the development of cost-effective L-asparaginase production process.
L-天冬酰胺酶已被证明是一种有潜力的抗癌药物，并能减少食品工业中天冬酰胺的形成。在本研究中，评估了一种利用麻疯树（）脱油饼作为唯一来源，以经济高效的方式生产 L-天冬酰胺酶的新方法，并将其与固态发酵中的不同农业底物进行了比较。从底物的化学组成（CHNS 分析）可以看出，该底物提供了有利于 L-天冬酰胺酶生产的 C/N 含量。使用基于机器学习的人工神经网络（ANN）和基于统计的响应面法（RSM）对有影响的工艺参数（如高压灭菌时间、水分含量、温度和 pH 值）进行了优化和建模。在 96 小时内，观察到在 30.3 分钟的高压灭菌时间、62%的水分含量、30°C 的温度和 6.2 的 pH 值下，酶活达到最大值 34.65±2.18IU/gds。利用优化的参数，酶活提高了 1.36 倍。与 RSM 相比，ANN 模型显示出更好的预测能力，其均方误差较低，为 0.072，均方根误差较低，为 0.268，回归系数值为 0.99。本研究表明，可利用廉价且易得的农业工业废物来开发经济高效的 L-天冬酰胺酶生产工艺。
