RT info:eu-repo/semantics/article
T1 Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data
A1 Pourdarbani, Razieh
A1 Sabzi, Sajad
A1 Arribas Sánchez, Juan Ignacio
K1 Acidity
K1 Acidez
K1 Artificial neural networks
K1 Redes neuronales artificiales
K1 Physicochemical properties
K1 Propiedades físico-químicas
AB Nondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool.
PB Elsevier
SN 2405-8440
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/48677
UL https://uvadoc.uva.es/handle/10324/48677
LA eng
NO Heliyon, 2021, vol. 7, n. 9, e07942
NO Producción Científica
DS UVaDOC
RD 26-abr-2024