An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

DI SCALA, KARINA; GUSTAVO MESCHINO; ANTONIO VEGA-GÁLVEZ; LEMUS MONDACA, ROBERTO; SARA ROURA; RODOLFO MASCHERONI

CIêNCIA E TECNOLOGIA DE ALIMENTOS

SOC BRASILEIRA CIENCIA TECNOLOGIA ALIMENTOS

Año: 2013 vol. 33 p. 411 - 416

0101-2061

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60 and 80°C) and at three air flow-rates (0.5, 1 and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Optimal values of quality indices were found at 62.9 ºC and 1.0 m/s by means of genetic algorithm.