j agr sci-tarim bili Journal of Agricultural Sciences 1300-7580 2148-9297 Ankara University 10.15832/ankutbd.523574 Engineering Mühendislik Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data https://orcid.org/0000-0002-9999-7845 Abbaspour-gilandeh Yousef University of Mohaghegh Ardabili https://orcid.org/0000-0003-2439-5329 Sabzi Sajad University of Mohaghegh Ardabili https://orcid.org/0000-0002-7261-7849 Azadshahraki Farzad Education and Extension Organization (AREEO) https://orcid.org/0000-0001-6646-6274 Karimzadeh Rouhollah Shahid Beheshti University Ilbeygi Elham Shahid Beheshti University https://orcid.org/0000-0002-7486-6152 Ignacio Arribas Juan University of Valladolid 09 04 2020 26 3 339 348 02 06 2019 05 18 2019 Copyright © 1995, Journal of Agricultural Sciences 1995 Journal of Agricultural Sciences

Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using non-destructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network - Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92±0.040 and 0.89±0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result.

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