Abstract

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 (Guizotia abyssinica) 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.

摘要

L-天冬酰胺酶已被证明是一种潜在的抗癌药物，并能减轻食品工业中丙烯酰胺的形成。在目前的调查中，尼日尔的一种新用途（Guizotia abyssinica) 脱油饼作为低成本生产 L-天冬酰胺酶的唯一来源进行了评估，并与固态发酵中的不同农业基质进行了比较。从底物的化学组成（CHNS 分析）可以看出，底物为 L-天冬酰胺酶的生产提供了有利的 C/N 含量。有影响的工艺参数，即；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方法 (RSM) 对高压灭菌时间、水分含量、温度和 pH 值进行了优化和建模。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，96 小时内观察到最大酶活性为 34.65 ± 2.18 IU/gds。在使用优化参数时观察到酶活性提高了 1.36 倍。与 RSM 相比，人工神经网络模型显示出优异的预测，均方误差为 0.072，均方根误差为 0.268，回归系数值为 0.99。本研究证明了利用廉价且容易获得的农工业废料开发具有成本效益的 L-天冬酰胺酶生产工艺的新方法。