Implementation of hyperspectral image analysis for evaluating table grape quality on bunch and berry level

The aromatic perception is one of the main factors that influence the
consumer when determining the wine’s quality and acceptance. Numerous factors (soil, climate,
winemaking style, cultivar) can influence the volatile compounds. Some of these compounds are released directly from the grape berries while others are formed during the fermentation and aging processes. However, little is known about the quality and aromatic formation of Syrah variety in the winter cycle cultivated in São Paulo.

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Nitrogen (N) nutrition of the vineyard strongly influences the must and the wine compositions. Several chemical markers present in wine (i.e., proline, succinic acid, higher alcohols and phenolic compounds) have been proposed for the cultivar Chasselas, as indicators of N deficiency in the grape must at harvest [1]. Grape genetics potentially influences the impact of N deficiency on grape composition, as well as on the concentration of potential indicators in the wine. The goal of this study was to evaluate if the che- mical markers found in Chasselas wine can be extended for other white wines to indicate N deficiency in the grape must.

For characterization, sensory designing and authentication rapid analytical technologies have become available. Some, like Proton Transfer Reaction Mass Spectrometry allow a rapid spectrum of the volatile compounds of wines. Combined with chemometrics wines can be characterized. The same approach can be used to calculate the results of virtual mixtures and allow formulation of constant quality blends. Other new techniques and portable devices based on spectroscopy allow measurements on production sites and in grocery stores, even for the smart consumer. We will present some examples of the application of these techniques for authentication of wines, both in the laboratory and on site.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).