Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece.
School of Science and Technology, International Hellenic University, 14th km Thessaloniki-N. Moudania, 57001 Thermi, Greece.
Sensors (Basel). 2023 Jan 17;23(3):1065. doi: 10.3390/s23031065.
Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR-SWIR spectrum (350-2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR-SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.
光谱学是一种广泛使用的技术，可以以简单且廉价的方式促进食品质量评估。特别是在葡萄生产中，可见光和近红外（VNIR）以及短波红外（SWIR）区域非常重要，它们可用于在成熟的各个阶段监测和控制果实质量。本工作的目的是通过使用覆盖整个 VNIR-SWIR 光谱（350-2500nm）的高精度接触式探头光谱仪，对四个不同的酿酒葡萄品种进行定量估计。研究的四个品种是霞多丽、马拉加祖亚、长相思和西拉，所有样本均于 2020 年和 2021 年收获期以及预收获物候期（对应 BBCH 量表的 81 至 89 阶段）从位于希腊北部的 Ktima Gerovassiliou 葡萄园采集。所有测量均在现场进行，折射计用于测量葡萄的总可溶性固形物含量（°Brix），提供地面实况数据。在开发葡萄光谱库之后，应用了四种不同的机器学习算法，即偏最小二乘回归（PLS）、随机森林回归、支持向量回归（SVR）和卷积神经网络（CNN），以及几种预处理方法，根据 VNIR-SWIR 高光谱数据预测 °Brix 含量。通过使用三种指标（决定系数（R2）、均方根误差（RMSE）和性能与四分位距之比（RPIQ））的交叉验证策略评估不同模型的性能。使用 CNN 学习算法开发的最佳模型对马拉加祖亚、长相思和西拉的精度很高（R2>0.8，RPIQ≥4），而 SVR 对霞多丽品种的拟合度也很好（R2=0.63，RMSE=2.10，RPIQ=2.24），证明通过使用便携式光谱仪，可以提供葡萄酒葡萄成熟度的现场估计。该方法可以为葡萄酒生产者提供有价值的工具，使其能够实时做出收获时间的决策，并采用无损的方式。
