Reprint of: In-field and non-destructive monitoring of grapes maturity by hyperspectral imaging

Monitoring the quality attributes of grapes is a practice that allows the state of ripeness to be checked and the optimal harvest time to be identified. A non-destructive method based on hyperspectral imaging (HSI) technology was developed. Analyses were carried out directly in the field on a 'Sangiovese' (Vitis vinifera L.) vineyard destined for wine production, by using a Vis/NIR (400-1000 nm) hyperspectral camera. One vineyard row was analysed on 13 different days during the pre-harvest and harvest time. The soluble solids content (SSC) expressed in terms of degrees Brix was measured by a portable digital refractometer. Afterwards, the grape samples were split in two classes: the first one composed by the samples characterised by a degrees Brix lower than 20 (not-ripe), while the second one by the samples with a degrees Brix higher than 20 (ripe). Grape mean spectra were extracted from each hyperspectral image and used to predict the SSC by partial least squares regression (PLS), and to classify the samples into the two classes by PLS discriminant analysis (PLS-DA). SSC was predicted with a R2 = 0.77 (RMSECV = 0.79 degrees Brix), and the samples were correctly classified with a percentage from 86 to 91%. Even if the number of wavelengths was limited, the percentages of correctly classified samples were again within the above-mentioned range. The present study shows the potential of the use of HSI technology directly in the field by proximal measurements under natural light conditions for the prediction of the harvest time of the 'Sangiovese' red grape.(c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study developed a non-destructive method based on hyperspectral imaging (HSI) technology to monitor and predict the ripeness and optimal harvest time of grapes. By analyzing grape samples from a 'Sangiovese' vineyard, using a Vis/NIR hyperspectral camera and a portable digital refractometer, the study predicted the soluble solids content (SSC) and classified the samples into ripe and not-ripe categories using partial least squares regression (PLS) and discriminant analysis (PLS-DA) respectively.