Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

• Published in: Agronomy (Basel) Vol. 9; no. 11; p. 735
• Main Authors: Abbaspour-Gilandeh, Yousef, Sabzi, Sajad, Hernández-Hernández, Mario, Hernández-Hernández, Jose Luis, Azadshahraki, Farzad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09.11.2019
• Subjects: Algorithms; Apples; Artificial neural networks; Biogeography; Chlorophyll; Coefficients; Color; color features; effective spectra; Evolutionary algorithms; Evolutionary computation; Food; Food industry; Food quality; Fruits; hybrid artificial neural network; Hybrids; Light; Methods; Network reliability; Neural networks; non-destructive; Nutrients; Optimization; Optimization algorithms; Reliability analysis; Spectra; spectral analysis; spectral data; Spectrum analysis; Tomatoes; vegetables; visible light/near-infrared; United States > US; Japan
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy9110735

Nondestructive estimation of the various physicochemical features of food such as fruits and vegetables will create a dramatic development in the food industry. The reason for this development is that the estimation is non-destructive, online, and most importantly fast. Regarding the advantages, various researchers have focused on how to undertake non-destructive estimation of the physicochemical features of various nutrients. Three main goals were pursued in this article. These are: 1. Nondestructive estimation of the chlorophyll b content of red delicious apple using color features and hybrid artificial neural network-cultural algorithm (ANN-CA), 2. Nondestructive estimation of chlorophyll b content of red delicious apple using spectral data (around a range of 680 nm) and hybrid Artificial Neural Network-biogeography-based algorithm (ANN-BBO), 3. Nondestructive estimation of the chlorophyll b content of red delicious apple using different groups of selective spectra by the hybrid artificial neural network-differential evolution algorithm (ANN-DA). In each of these methods, 1000 replications were performed to evaluate the reliability of various hybrids of the artificial neural network. Finally, the results indicated that the average determination coefficient in 1000 replications for the hybrid artificial neural network, the cultural algorithm, and the hybrid artificial neural network, the biogeography-based optimization algorithm, was 0.882 and 0.932, respectively. Also, the results showed that the highest value of the coefficient of determination among the different groups of effective features is related to the group of features with 10 spectra. The coefficient of determination in this case was 0.93.