Prediction of foods freezing and thawing times: artificial neural networks and genetic algorithm approach

S.M. GOÑI; S. ODDONE; J.A. SEGURA; R.H. MASCHERONI; V.O. SALVADORI

JOURNAL OF FOOD ENGINEERING

Elsevier

Año: 2008 vol. 84 p. 164 - 178

0260-8774

Abstract In this work a feedforward neural network, trained and validated using experimental values of freezing and thawing times of foods and test substances of different geometries, is developed. A total of 796 experimental times of both processes were collected from reported works. The database used covered a wide range of operative conditions as well as size, shape and type of material. The input layer had seven elements: shape factor, characteristic dimension, Biot number, thermal diffusivity, initial, ambient and final temperatures. The output layer had one element: the process time. The total number of hidden layers and the number of neurons in each hidden layer were chosen by trial and error. For each topology, a simple based genetic algorithm search technique was applied to obtain the initial training parameters of the neural network that improve its generalization capacity. Three particular networks were evaluated: one for freezing times, another one for thawing times, and a third one for both freezing and thawing times. The final topologies has one or two hidden layers with 4 nodes in each one. Our results show that the neural network had an average absolute relative error of less than 10%, suggesting that ANN provide a simple and accurate prediction method for freezing and thawing times, valid for wide ranges of food types, sizes, shapes and working conditions. 2007 Elsevier Ltd. All rights reserved.2007 Elsevier Ltd. All rights reserved. Keywords: Freezing time; Thawing time; Food; Artificial neural networkFreezing time; Thawing time; Food; Artificial neural network