CXMZDBPG

2418

6 janvier 2021 16:56

6 janvier 2021 16:56

Non-destructive assessment of the physicochemical properties of food products, especially fruits, makes it possible to examine the internal quality without any damage. This is applicable at different stages of fruit growth, harvesting stage, and storage as well as at the market stage. In this regard, the present study aimed to estimate the total chlorophyll content using three types of data: color data, spectral data, and spectral data related to the most effective wavelengths. The most important steps of the proposed algorithms include extracting spectral and color data from each sample of Fuji cultivar apple, selecting the most effective wavelengths at the range of 660–720 nm using hybrid artificial neural network–particle swarm optimization (ANN-PSO), non-destructive assessment of the chemical property of total chlorophyll content based on color data, and spectral data using hybrid artificial neural network-Imperialist competitive algorithm (ANN-ICA). In order to assess the reliability of the hybrid ANN-ICA, 1000 iterations were performed after selecting the optimal structure of the artificial neural network. According to the results, in the best training mode and using spectral data and the most effective wavelength, total chlorophyll content was predicted with the R2 and RMSE of 0.991 and 0.0035, 0.997 and 0.001, 0.997 and 0.0006, respectively.

None

• AB - Transversal
• FREDO authentification et traçabilité
• FREDO conservation des productions
• FREDO qualité des produits
• FREDO technologie et innovation
• GEO Iran
• GEO Mexique
• pomme
• verger

• ANN
• ICA algorithm
• PSO algorithm
• apples
• non-destructive estimation
• spectroscopy

Plants

9

1547

2020/11

24 novembre 2020

10.3390/plants9111547 Le DOI est une URL unique de référencement d'une publication. Il est donc plus fiable et permanent qu'une URL classique