Effect of nanofiltration on the chemical composition and wine quality

During the tasting of a fine, old wine, the aromas generated in the glass are intertwined in an intimate, complex manner, expressing the fragrance of the aging bouquet. This aging bouquet, which develops during bottle storage through a complex transformation process, may result in a broad palette of nuances. Among these, undergrowth, truffle, toasted, spicy, licorice, fresh red- and black-berry fruit and mint descriptors were recently identified as features of its olfactory representation for red Bordeaux wines. Although a targeted chemical approach focusing on volatile sulfur compounds revealed the role played by dimethyl sulfide, 2-furanmethanethiol, and 3-sulfanylhexanol as molecular markers of the typicality of the wine aging bouquet of red Bordeaux wines, its chemical transcription has only partially been elucidated.

The use of multivariate statistics to correlate chemical data to spectral information seems as a valid alternative for the quantification of red wine phenolics. The advantages of these techniques include simplicity and cost effectiveness together with the limited time of analysis required. Although many
publications on this subject are nowadays available in the literature most of them only reported feasibility
studies. In this study 400 samples from thirteen fermentations including five different cultivars plus 150
wine samples from a varying number of vintages were submitted to spectrophotometric and chromatographic phenolic analysis.

The interactions among aromatic compounds and proteins is an important issue for the quality of foods and beverages. In wine, the loss of flavor after vinification is associated to bentonite treatment and this effect can be the result of the removal of aroma compounds which are bound wine proteins. This phenomenon was recently demonstrated for long chain fatty acids and their ethyl esters (1). Since these latter compounds are spectroscopically silent, their association with proteins is not easy to measure.

Multiple Factor Analysis (MFA) According to the Chenin blanc Association of South Africa, there are three recognized dry wine styles, Fresh and Fruity (FF), Rich and Ripe Unwooded (RRU), and Rich and Ripe Wooded (RRW), classically attributed with the help of sensory evaluation. One of the “rapid methods” has drawn our attention for the purpose of simplifying and making style attribution for large sample sets, evaluated during different sessions, more robust. Polarized Projective Mapping (PPM) is a hybrid of Projective Mapping (PM) and Polarised Sensory Positioning (PSP). It is a reference-based method in which poles
(references) are used for the evaluation of similarities and dissimilarities between samples.

Leaf removal in the bunch zone is a common viticultural practice with several objectives, yet it has been difficult to conclusively link the physiological mechanism(s) and metabolic berry impact to this widely practiced treatment. We used a field-omics approach1 in a Sauvignon blanc high altitude model vineyard, showing that the early leaf removal in the bunch zone caused quantifiable and stable responses (over years) in the microclimate where the main perturbation was increased exposure. We provide an explanation for how leaf removal leads to the shifts in grape metabolites typically linked to this treatment and confirm anecdotal evidence and previous reports that leaf removal treatment at an early stage of berry development affects “quality-associated” metabolites (monoterpenes and norisoprenoids).