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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.

L-asparaginase production in solid-state fermentation using Aspergillus niger : process modeling by artificial neural network approach