Characterization of tannins and prevention of light-struck taste: the enofotoshield project

Simgi® platform pursues the need for dynamic in vitro simulation of the human gastrointestinal tract optimized and adapted to food safety and health fields. The platform has confirmed the model’s suitability since its first’s studies with the consistency between the simulated colonic metabolism of wine polyphenols and the metabolic evolution observed with the intake of wine in human intervention studies [1]. 

Consumer expectations are increasingly shifting towards “residue-free wines.” However, from an analytical standpoint, “zero” does not exist. Laboratories often use the quantification limits of analysis methods to signify ‘zero.’ Improved techniques now allow for the quantification of levels that were previously undetectable. This is why we prefer to use the term “unquantifiable residue” rather than “absence of residues.”

Yeast-derived aroma compounds are the result of different and complex biochemical pathways that mainly occur during alcoholic fermentation. Many of them are related -but not limited- to the availability of nutrients in the fermentation medium and linked to nitrogen metabolism and biomass produced. Besides, the metabolic phase of yeast also regulates the expression of many enzymes involved in the formation of aroma active compounds. The work investigates the overall effect of continuous supplementation of nutrients during alcoholic fermentation of a grape must on the volatile composition of wines.

The present work contributes by developing a rapid sensory-directed methodology for the screening and selection of high quality wines with different sensory profiles Therefore, Verdejo and Tempranillo musts were fermented with 50 different yeasts each under controlled laboratory conditions. Resulting samples were firstly categorized according to five levels of quality by a panel of wine professionals (Sáenz-Navajas, Ballester et al. 2013). Higher quality samples were described by flash profiling by a semi-trained panel
(Valentin, Chollet et al. 2012) and most distinctive samples were screened by gas chromatography-olfactometry (GC-O) (López, Aznar et al. 2002).

Targeted analytical methods can overlook compounds that are a priori unknown to play a role in the mouthfeel sensations. This limitation can be overcome with the information provided by untargeted metabolomic analysis using UPLC‐QTOF-MS. To this end, an untargeted metabolomic approach applied to 42 red wines has allowed development of a model with predictive capacity by cross-validation for the “dry”, “oily” and “unctuous” sensations perceived by a sensory panel. The optimal PLS model for “dry” retained compounds with positive regression coefficients (≥ 0.17) including a trimer procyanidin, a peptide, and four anthocyanins.