Strain improvement, artificial intelligence optimization, and sensitivity analysis of asparaginase-mediated acrylamide reduction in sweet potato chips.

ABSTRACT: In recent times, L-asparaginase has emerged as a potential anti-carcinogen through hydrolysis of L-asparagine in the blood for anti-leukemic application, and in carbohydrate-based foods, for acrylamide reduction applications. In this study, Aspergillus sydowii strain UCCM 00124 produced an L-asparaginase with a baseline acrylamide reduction potential of 64.5% in sweet potato chips. Plasma mutagenesis at atmospheric pressure and room temperature (ARTP) was employed to improve L-asparaginase production while artificial neural network embedded with genetic algorithm (ANN-GA) and global sensitivity analysis were used to identify and optimize process conditions for improved acrylamide reduction in sweet potato chips. The ARTP mutagenesis generated a valine-deficient mutant, Val^-Asp-S-180-L with 2.5-fold L-asparaginase improvement. The ANN-GA hybrid evolutionary intelligence significantly improved process efficiency to 98.18% under optimized conditions set as 118.6 °C, 726.37 g/L asparagine content, 9.92 µg/mL L-asparaginase, 4.54% NaCl, and soaking time of 15 h without significant changes in sensory properties. The sensitivity index revealed initial asparagine content as the most sensitive parameter to the bioprocess. The enzyme demonstrated significant thermo-stability with Arrhenius deactivation rate constant, K_d, of 0.00562 min^-1 and half-life, t_1/2, of 123.35 min at 338 K. These conditions are recommended for sustainable healthier, and safer sweet potato chips processing in the food industry.

Supplementary information

The online version contains supplementary material available at 10.1007/s13197-023-05757-5.

SUBMITTER: Akwagiobe E 

PROVIDER: S-EPMC10326208 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature