RESEARCH JOURNAL OF FOOD SCIENCE AND NUTRITION
Integrity Research Journals
ISSN: 2536-7080
Model: Open Access/Peer Reviewed
DOI: 10.31248/RJFSN
Start Year: 2016
Email: rjfsn@integrityresjournals.org
Application of artificial neural network for estimating the qualitative characteristics of cantaloupe melon and comparison with the regression model
https://doi.org/10.31248/RJFSN2017.023 | Article Number: ADE2B1481 | Vol.2 (1) - April 2017
Received Date: 20 February 2017 | Accepted Date: 08 March 2017 | Published Date: 30 April 2017
Authors: Mahdi Ghasemi-Varnamkhasti* , Davood Ghanbarian , Zahra Shojaei , Abbasali Yadollahi , Ayat Mohammad Razdari , Reza Goli and Seyedeh Hoda Yoosefian
Keywords: Quality, color, artificial neural network, cantaloupe melon.
In this paper, the quality characteristics of melon was estimated by using color parameters and neural networks for three fertilizing stages (no fertilizer, fertilizing 5 and 10 ton/ha). For this purpose, chemical parameters including fructose, glucose and sucrose, and color parameters such as L*, a*, b* were studied. Physical characteristics under study were specific weight, mean firmness and skin. Chemical compositions include Brix, moisture content, titratable acidity, pH and ash. Results showed that the best determination coefficient for approximated equation of Brix is R2 = 0.96 and the least standard error was found to be SE = 0.52 related to glucose equation. According to the analyses done on the modeling data using neural network in Neuro Solutions software, the most appropriate network for fructose prediction was Feed for Multilayer perceptron with architecture of 4-12-12-1 and R2 = 0.99, MSE = 0.000324. Therefore, for the estimation of quality characteristics for cantaloupe, neural network can be used successfully.
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