Modeling the tryptic hydrolysis of pea proteins using an artificial neural network

Experimentally determined values for the degree of hydrolysis (DH) were used with an artificial neural network (ANN) model to predict the tryptic hydrolysis of a commercially available pea protein isolate at temperatures of 40, 45, and 50 °C. Analyses were conducted using the STATISTICA Neural Networks software on a personal computer. Input data were randomized to two sets: learning and testing. Differences between the experimental and calculated DH% were slight and ranged from 0.06% to 0.24%. The performance of the educated ANN was then tested by inputting temperatures ranging from 35 to 50 °C. Very strong correlations were found between calculated DH% values obtained from the ANN and those experimentally determined at all temperatures; the determination coefficients (R²) varied from 0.9958 to 0.9997. The results so obtained will be useful to reduce the time required in the design of enzymatic reactions involving food proteins.