Improvement of L-asparaginase, an Anticancer Agent of Aspergillus arenarioides EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA)

The present study aimed to optimize the production of L-asparaginase from Aspergillus arenarioides EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors used included temperature (x(1)), pH (x(2)), incubation time (x(3)), and soybean concentration (x(4)). The coefficient of the predicted model using the Box-Behnken design (BBD) was R-2 = 0.9079 (p < 0.05); however, the lack of fit was significant indicating that independent factors are not fitted with the quadratic model. These results were confirmed during the optimization process, which revealed that the standard error (SE) of the predicted model was 11.65 while the coefficient was 0.9799, at which 145.35 and 124.54 IU mL(-1) of the actual and predicted enzyme production was recorded at 34 degrees C, pH 8.5, after 7 days and with 10 g L-1 of organic soybean powder concentrations. Compared to the RBFNN-GA, the results revealed that the investigated factors had benefits and effects on L-asparaginase, with a correlation coefficient of R = 0.935484, and can classify 91.666667% of the test data samples with a better degree of precision; the actual values are higher than the predicted values for the L-asparaginase data.

Automated summary

The present study optimized the production of L-asparaginase from Aspergillus arenarioides EAN603 using a combination of response surface methodology and radial basis function neural network with a genetic algorithm. Independent factors including temperature, pH, incubation time, and soybean concentration were considered. The results showed that these factors had significant effects on L-asparaginase production, with the actual values being higher than the predicted values.