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

Terroir leaves its mark on the accumulation of flavours in grape berries, triggering biochemical re-actions and ultimately shaping wine styles.

Measuring the effect of oxygen consumption on the color of wines as the level of dissolved oxygen decreases over time is very useful to know how much oxygen a wine can consume without significantly altering its color. The changes produced in wine after being exposed to high oxygen concentrations have been studied by different authors, but in all cases the wine has been analyzed once the oxygen consumption process has been completed. This work presents the results obtained with the use of an equipment designed and made to measure simultaneously the level of dissolved oxygen and the spectrum of the wine, during the oxygen consumption process from saturation levels with air to very low levels, which indicate the total consumption of the dosed oxygen[1,2].

After bottling, the wine continues to evolve during storage. The choice of the stopper is an important factor in this evolution. In addition to the oxygen permeability of the closure, the migration of stopper compounds into the wine can also have an impact on the wine organoleptic properties. Many studies have shown that transfers of volatile compounds from the stoppers into the wine can happen depending on the type of closure used (1). Moreover, when cork-made stoppers are used, the migration of phenolic compounds from the stopper into the wine can also occur (2, 3).

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).

The increasing demand for healthier, more transparent, and sustainable wine products has prompted the need for innovative solutions to optimize the wine health value chain.