An artificial neural network approach to predict lactose hydrolysis by beta-galactosidase

CUELLAS, ANAHÍ; MAMMARELLA, ENRIQUE; RUBIOLO, AMELIA; IAMMARINO, MARCELO; ODDONE, SEBASTIÁN; CORRADINI, MARÍA

Anaheim, CA, Estados Unidos

Otro; IFT Annual Meeting; 2009

Institute of Food Technologists

The disposal of whey permeate remains a significant environmental problem for the dairy industry because of its high lactose content. Lactose transformation to value added products such as sweetening syrups or ethanol constitutes a feasible strategy if conversion rates and yields are optimized. The hydrolysis of lactose is a key step in any conversion process and it can be accomplished using β-galactoside. The objective of this work was to predict the performance of enzyme reactors at various operational conditions using an artificial neural network (ANN) approach. The ANN technique enables us to investigate the effects of the operational factors simultaneously instead of evaluating their effects separately. For that purpose, 280 experimental data sets were obtained using several substrate concentrations (0.07, 0.15, 0.22, 0.29 M), enzyme concentrations (0.08-3 mg/ml), reaction media (lactose solution, whey permeate & whey permeate with proteins) and enzyme conditions (free vs. immobilized – covalently bound to chitosan beads).  All experiments were carried out in triplicate. A back propagation three-layer neural network model was developed.  The input layer had six elements: time, enzyme condition, enzyme concentration, initial substrate concentration, reaction media and protein concentration. The number of neurons in the hidden layer was adjusted to a final value of 13, and the results were analyzed for over- and under-fitting. The output layer had only one element; the residual lactose concentration. The experimental dataset was divided into two groups by random selection, the first group (150 observations) was used for network training and the remaining data (130 observations) was used for validation.