Modeling of Basil Leaves Drying by GA–ANN-Reference-Cited by-同舟云学术

Modeling of Basil Leaves Drying by GA–ANN

Published:2013-10-16 Issue:4 Volume:9 Page:393-401
ISSN:1556-3758
Container-title:International Journal of Food Engineering
language:
Short-container-title:

Author:

Taheri-Garavand Amin¹,Rafiee Shahin²,Keyhani Alireza²,Javadikia Payam³

Affiliation:

1. 1Department of Mechanical Engineering of Agricultural Machinery, Lorestan University, Khorramabad, Iran and Department of Agricultural Machinery Engineering, University of Tehran, Karaj, Iran

2. 2Department of Agricultural Machinery Engineering, University of Tehran, Karaj, Iran

3. 3Department of Agricultural Machinery Engineering, Razi University of Kermanshah, Kermanshah, Iran

Abstract

AbstractIn this research, the experiment is done by a dryer. It could provide any desired drying air temperature between 20 and 120°C and air relative humidity between 5 and 95% and air velocity between 0.1 and 5.0 m/s with high accuracy, and the drying experiment was conducted at five air temperatures of 40, 50, 60, 70 and 80°C and at three relative humidity 20, 40 and 60% and air velocity of 1.5, 2 and 2.5 m/s to dry Basil leaves. Then with developed Program in MATLAB software and by Genetic Algorithm could find the best Feed-Forward Neural Network (FFNN) structure to model the moisture content of dried Basil in each condition; anyway the result of best network by GA had only one hidden layer with 11 neurons. This network could predict moisture content of dried basil leaves with correlation coefficient of 0.99.

Publisher

Walter de Gruyter GmbH

Subject

Engineering (miscellaneous),Food Science,Biotechnology

Link

https://www.degruyter.com/document/doi/10.1515/ijfe-2012-0224/pdf

Reference50 articles.

1. Effect of drying conditions on drying kinetics and quality of aromatic Pandanus amaryllifolius leaves;Rayaguru;J Food Sci Technol,2010

2. Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit;Fathi;Food Bioprocess Technol,2009

3. Use of genetic algorithms for neural networks to predict community-acquired pneumonia;Heckerling;Artif Intell Med,2004

4. Influence of the moistening and drying of wheat grain on its hardness;Mis;Int Agrophys,2004

5. Influence of the moistening and drying of wheat grain on its hardness;Mis;Int Agrophys,2004

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