Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data
| Autor: | Sajad Sabzi, Rouhollah Karimzadeh, Juan Ignacio Arribas, Elham Ilbeygi, Farzad Azadshahraki, Yousef Abbaspour-Gilandeh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Chlorophyll a
Spectroscopy Color features Non-destructive estimation Artificial neural network Regression Metaheuristic algorithms Mühendislik Plant Science chemistry.chemical_compound Engineering chemistry Metaheuristic algorithms Non destructive Content (measure theory) Animal Science and Zoology Color data Cultivar Biological system Agronomy and Crop Science Mathematics |
| Zdroj: | Volume: 26, Issue: 3 339-348 Journal of Agricultural Sciences |
| ISSN: | 2148-9297 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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