Accurate and nondestructive detection of apple brix and acidity based on visible and near-infrared spectroscopy

Rapid, nondestructive and accurate detection of internal qualities of the apple is an important research interest. In this study, the brix, acidity and brix/acidity ratio of the apple were rapidly detected by visible and near-infrared spectroscopy (VIS-NIRS). By scanning spectra and measuring the reference values of brix and acidity of apple samples, the relationship models between the spectra and brix, acidity, brix/acidity ratio were, respectively, established. Sample division, characteristic wavelength optimization, and modeling methods were compared systematically, and the optimal prediction model of each quality index was determined. The experimental results show that the competitive adaptive reweighted sampling method can effectively select characteristic wavelengths, which not only improves the prediction speed, but also greatly enhances the prediction accuracy. The established partial least squares models based on these selected characteristic wavelengths all have high accuracy and robustness for the three quality indices. The determination coefficients of the models are 0.9899, 0.9615, 0.9535, and the relative percent deviation are 9.9269, 5.0987, 4.6374, respectively. All this work proves that VIS-NIRS can be used for rapid and nondestructive detection of the internal qualities of an apple.