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

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...

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Detalles Bibliográficos
Autores principales: Di Scala, Karina, Meschino, Gustavo, Vega-Gálvez, Antonio, Lemus-Mondaca, Roberto, Roura, Sara, Mascheroni, Rodolfo Horacio
Formato: Articulo
Lenguaje:Inglés
Publicado: 2013
Materias:
Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/85211
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-85211
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
spellingShingle Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
Di Scala, Karina
Meschino, Gustavo
Vega-Gálvez, Antonio
Lemus-Mondaca, Roberto
Roura, Sara
Mascheroni, Rodolfo Horacio
An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
topic_facet Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
description 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 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.
format Articulo
Articulo
author Di Scala, Karina
Meschino, Gustavo
Vega-Gálvez, Antonio
Lemus-Mondaca, Roberto
Roura, Sara
Mascheroni, Rodolfo Horacio
author_facet Di Scala, Karina
Meschino, Gustavo
Vega-Gálvez, Antonio
Lemus-Mondaca, Roberto
Roura, Sara
Mascheroni, Rodolfo Horacio
author_sort Di Scala, Karina
title An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_short An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_full An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_fullStr An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_full_unstemmed An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_sort artificial neural network model for prediction of quality characteristics of apples during convective dehydration
publishDate 2013
url http://sedici.unlp.edu.ar/handle/10915/85211
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