Detection of Key Indicators of Ripening Quality in Table Grapes Based on Visible near-infrared Spectroscopy

Phenolic compounds play a crucial role in assessing the internal quality of grapes and hold significant importance in this regard. The capability of visible-near-infrared (Vis — NIR) spectroscopy combined with multivariate regression models was explored to detect the contents of total phenolics and tannins in grape skins and seeds. Reflectance spectra data of Muscat Kyoho grapes were collected within the wavelength range of 400 nm to 1 029 nm, and the samples were divided into correction set and prediction set by SPXY algorithm. Six commonly used preprocessing methods such as standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (ID), second derivative (2D), Savitzky — Golay smoothing (SG) and SG + ID were applied to the spectral data, and the competitive adaptive reweighted sampling algorithm (CARS) was utilized to select informative wavelengths. The quantitative models for comprehensive analysis of total phenolics and tannins in grape skins and seeds based on full spectra and effective wavelengths were established by partial least squares regression (PLSR), support vector machine regression (SVR), and convolutional neural network (CNN). The results showed that for the total phenolics in grape skins, total phenolics and tannins in grape seeds, the models on the basis of effective wavelengths performed better than those with full spectra data. While for the tannins in grape skins, the models constructed with full spectra yielded better results than the feature wavelength-selected models. The optimal models for the total phenolics and tannins in grape skins and seeds were RAW - CARS - SVR, ID - CARS - SVR, RAW - CNN and RAW - CARS -PLSR, respectively. The correlation coefficent of calibration set (Re) were 0. 96, 0. 99, 0. 96 and 0. 91, the correlation coefficent of prediction set (R) were 0. 95, 0. 99, 0. 83 and 0. 89, the residual predictive deviation (RPD) were 3.56, 7.30, 1.92 and 2.25, respectively. Therefore, the developed method could realize the non-destructive detection of the contents of total phenolics and tannins in grape skins and seeds. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.