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

Abstract

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.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

“…Jiang et al [259] investigated the impact of apple size on its spectrum On the other hand, recent attention is also directed at further improving the feasibility of these techniques in a practical sense. Abbaspour-Gilandeh et al reported a cost-effective approach to the estimation of the chlorophyll b content of red delicious apples using color and NIR spectral data combined with hybrid ANN [243]. The authors concluded that the cost of the Vis/NIR spectroscopy system setup is important for real-time applications, and a small window of around 680 nm wavelength could be used to reduce the expenditure of the analysis.…”

Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm

“…The task of the imperialist competitive algorithm (ICA) is to optimally adjust these parameters. ICA is an algorithm based on cultural, social, and political evolution in which all countries are looking for the optimal public point to solve the optimization problem (Atashpaz-Gargari and Lucas [ 35 ], Abbaspour-Gilandeh et al [ 36 ]). After selecting the optimal structure of the artificial neural network, 1000 iterations were performed due to the reliability of the ANN.…”

Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

“…Generally, the spectral characteristics of banana fruit skin at different ripening degrees demonstrate significant changes in the SRIs values. This may be due to changes in physiological status, cell structure, fruit pigments, physical and chemical, and organic substances [9,11,14,24,25]. The mean value for each parameter with the same letter is not statistically different under different ripening degrees according to Duncan's multiple range test at a 0.05 significance level.…”

“…Several studies have evaluated the biochemical characteristics of various fruit species using non-destructive proximal reflectance sensors and spectroscopic techniques [9,15,[21][22][23][24][25][26]. For example, Elsayed et al [25] found that spectral reflectance indices (SRIs) revealed close and extremely significant relationships with Chl and soluble solids content (SCC) of orange, guava, and mandarin fruits under different ripening degrees, with determination coefficients reaching 0.87.…”