NIR based sensometric approach for consumer preference evaluation

The increasing interest in enhancing groundbreaking sensory profile of wines determined the need to select novel strains of lactic acid bacteria (LAB). Metabolic processes characterizing malolactic fermentation (MLF) lead to the production of several organic compounds that significantly impact the oenological and sensory characteristics of wines.

Most consumers enjoy wine in moderation, however, there remains a minority that may develop risky drinking habits, potentially harming themselves and those around them. For the last fifteen years, a prime objective of the wine in moderation programme has been to educate and empower the wine sector and now for the first time, a central education tool has been developed, integrating the topic of moderate consumption horizontally in all wine activities. The entire wine value chain – from the producer to the salesperson to the restaurant service staff – can contribute to reduce harmful consumption and encourage responsible drinking patterns.

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).

Pour prévoir et gérer chaque année les principales caractéristiques de la maturation en Champagne, le CIVC (Comité Interprofessionnel du Vin de Champagne) a développé un ensemble de moyens de prévision et d’information très performants qui permettent aux différents acteurs de la filière viti-vinicole de prendre en compte ces informations à l’échelle de chaque terroir communal pour la recherche d’une qualité optimale.

In some white wines, malolactic fermentation (MLF) is very interesting, and for low pH wines this process is particularly difficult. Although MLF is generally not recommended for sparkling white wine, some winemakers prefer to promote MLF to contribute to organoleptic complexity. Oenococcus oeni is generally the bacterium of choice for MLF.