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 °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 °Brix lower than 20 (not-ripe), while the second one by the samples with a °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 °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.

监视葡萄的质量属性是一种可以检查成熟状态并确定最佳收获时间的做法。提出了一种基于高光谱成像（HSI）技术的无损检测方法。直接在田间对“ Sangiovese”（葡萄）进行了分析。L.）用Vis / NIR（400-1000 nm）高光谱相机注定用于葡萄酒生产的葡萄园。在收获前和收获期的13个不同日期对一排葡萄园进行了分析。用便携式数字折光仪测量以白利糖度表示的可溶性固形物含量（SSC）。之后，将葡萄样品分为两类：第一类由特征在于白利糖度低于20（未成熟）的样品组成，第二种由具有高于20的白利糖度（成熟）的样品组成。从每个高光谱图像中提取葡萄平均光谱，并通过偏最小二乘回归（PLS）来预测SSC，并通过PLS判别分析（PLS-DA）将样品分为两类。SSC的预测值为R ² = 0.77（RMSECV = 0.79°Brix），并且样品的分类正确率从86％到91％。即使波长的数量受到限制，正确分类的样品的百分比也再次在上述范围内。本研究显示了在自然光条件下通过近端测量直接在田间使用HSI技术的潜力，以预测“桑吉维斯”红葡萄的收获时间。