2026-09-15T04:53:32Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1548642025-04-30T20:51:07Zcom_10366_4613com_10366_4576com_10366_3823col_10366_4616

Pourdarbani, Razieh Sabzi, Sajad Arribas, Juan Ignacio 2024-01-29T10:05:01Z 2024-01-29T10:05:01Z 2021-09-07 2405-8440 http://hdl.handle.net/10366/154864 10.1016/j.heliyon.2021.e07942 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. Regression coefficients (R) in estimated acidity, tissue firmness, and starch content properties were R = 0.930 ± 0.014, R = 0.851 ± 0.014, and R = 0.974 ± 0.006 , respectively, using only the three most effective wavelengths from the acquired spectra. J. I. Arribas was supported by Proyectos de IþDþi «Retos Inves- tigacio n», Programa Estatal de IþDþi Orientada a los Retos de la Sociedad, Plan Estatal de Investigacio n Científica, T ecnica y de Innovacio n; Agencia Estatal de Investigacio n (AEI), Spain, and Fondo Europeo de Desarrollo Regional (FEDER), European Union (RTI2018- 098958-B-I00). eng Acidity Apple Artificial neural network Firmness Fruit Physicochemical properties Starch 3302.90 Ingeniería Bioquímica Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://doi.org/10.1016/j.heliyon.2021.e07942 info:eu-repo/semantics/openAccess Heliyon 7 9 e07942