Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Abstract

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 ( $\text{R}$ ) in estimated acidity, tissue firmness, and starch content properties were $\text{R}=0.930\pm 0.014$ , $\text{R}=0.851\pm 0.014$ , and $\text{R}=0.974\pm 0.006$ , respectively, using only the three most effective wavelengths from the acquired spectra.

Keywords: Acidity; Apple; Artificial neural network; Firmness; Fruit; Physicochemical properties; Starch.