Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy

Abstract

Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change in grapes’ quality parameters during ripening, a rapid and nondestructive method of visible–near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes in four different ripening stages were explored, with increasing color in redness/greenness (a*) and Chroma (C*) as well as soluble solids (SSC) content as ripening advanced, and decreasing values in color of lightness (L*), yellowness/blueness (b*), hue angle (h*), hardness, and total acid (TA) content. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected using the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectral data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The models of SSC and TA with full spectral data using PLSR after 1st derivative preprocessing both obtained the best results. For SSC, these yielded optimum results when the coefficients of determination of PLSR models for the calibration set (RCal2) and the prediction set (RPre2) were 0.97 and 0.93, respectively; the root mean square error for the calibration set (RMSEC) and the prediction set (RMSEP) were 0.62% and 1.27%, respectively; and the RPD was 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP, and RPD were 0.97, 0.94, 0.88 g/L, 1.96 g/L, and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage

“…Passion fruit is a leapfrog fruit, and its internal biological processes (e.g., respiration and transpiration) result in water loss during storage. Throughout the preservation process, the passion fruit pericarp endures substantial dehydration, with water loss occurring predominantly in the pericarp rather than in the pulp [ 13 ]. Plant cell break caused by this phenomenon is a key factor contributing to the variations in hardness during storage.…”

“…They evaluated two spectroscopic systems, and the findings suggested a somewhat improved measuring performance for NIRS system, with the best correlation [ 12 ]. Fengjiao Ping et al have effectively constructed excellent spectrum models by investigating grape hardness and other physicochemical properties, thus providing a valuable technique for quick, non-destructive detection in measuring grape maturity, enhancing quality control [ 13 ]. These case studies further support the viability and accuracy of NIRS technology in measuring fruit hardness.…”

Rapid Determination of Wine Grape Maturity Level from pH, Titratable Acidity, and Sugar Content Using Non-Destructive In Situ Infrared Spectroscopy and Multi-Head Attention Convolutional Neural Networks

“…Rouxinol et al [14] also report high R 2 values for • Brix (0.86) and TA (0.86) in red grape varieties using the 1100-2300 nm spectral range. Finally, Ping et al [16], using the table grape variety Kyoto, reported R 2 of 0.92 and RMSE of 1.01 for • Brix, and R 2 of 0.94 and RMSE of 1.78 for TA.…”

“…Along the same line, Daniels et al (2019) [15] explored the use of NIR spectroscopy to quantify total soluble solids (TSS), TA, TSS/TA, and pH non-destructively on intact bunches, achieving promising results using the Partial Least Squares (PLS) algorithm. Ping et al (2023) [16], by using the VNIR spectra, estimated the soluble solid content (SSC) and TA of the grapes, also recording the changes in the chemical composition at different maturity levels. The Vis, NIR, and SWIR spectroscopy was also explored by Meja-Correal et al (2023) [17] for grape TSS estimation by applying a PLS regression model and selecting the best spectral range to avoid complex and potentially overfitted regression models, concluding that the most suitable spectral range for TSS predictions was the NIR range (701-1000 nm).…”