Effect of concentration and competition between different fungicide residues on the adsorption efficiency of activated vegetal fibres for treatment of wine

Red grape pomace, a waste from wine production, can be valorised by extracting polyphenols, high-added value compounds used in cosmetics or oenology. For use at an industrial level, using green extraction techniques, pomace need to be stored before being processed. The aim of this study is to test various storage conditions in order to maintain high level of polyphenols over 180 days, while keeping storage cost economically interesting. In a first step, different storage conditions (ambient temperature or cooled (4°C) temperature, anaerobic (saturation with N2) or aerobic conditions, and addition of sulphur dioxide (SO2)) were compared on small samples (1 kg) packed in plastic pockets. The quality of storage was assessed by following the optical density of the pomace extract at 280 nm (DO 280 expressed as mg/l eq gallic acid), which is an indication of the amount of remaining extractable polyphenols.

All techniques usually used to assay proteins was not reliable in vegetable extract due to interferences with the components included in extracts like polyphenols, tanins, pectines, aromatics compounds. Absorbance at 280nm, Kjeldhal assay, Biuret and Lowry methods, Acid Bicinchonique technique and Bradford assay give the results depending on the composition of extract, on the presence or not of detergent and on the raw material (Marchal, 1995). Another difficulty in these extracts for the quantification of proteins comes from the large amount of water included in vegetable and the low concentration of proteins. Thus in red wines, proteins are usually not taken into account due to their low concentration (typically below 10 mgL-1) and to the presence of anthocyanis and polyphenols.

“The Human being has an excellent ability to detect and discriminate odors but typically has great difficulty in identifying specific odorants”(1). Furthermore, “from a cognitive point of view the mechanism used to judge wines is closer to pattern recognition than descriptive analysis.” Therefore, when one wants to reveal the volatile “wine-like feature” pattern recognition techniques are required. Sensomics is one of the most recent “omics”, i.e. a holistic perspective of a complex system, which deals with the description of substances originated from microorganism metabolism that are “active” to human senses (2). Depicting the relevant volatile fraction in wines has been an ongoing task in recent decades to which several research groups have allocated important resources. The most common strategy has been the “target approach” in order to identify the “key odorants” for a given wine varietal.

Primary and secondary metabolites are major components of grape quality and wine typicity. Their accumulation is interconnected through a complex metabolic network, which is still not well understood. This study aims to investigate how the enzymes of central carbon metabolism interact with anthocyanin biosynthesis during grape berry development: does the accumulation of anthocyanins, which represents a non-negligible diversion of carbon metabolic fluxes, require reprogramming of central enzymes or is it controlled downstream of central metabolism? To this end, 23 enzymes involved in central carbon metabolism pathways have been analyzed in the berries of 3 grape cultivars, which have close genetic background but distinct temporal dynamics of anthocyanin accumulation.

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can consistently stimulate (MLF+ phenotype) or inhibit (MLF- phenotype) malolactic bacteria and the MLF process as a function of numerous winemaking practices, but the molecular evidence behind still remains a mystery. In this study, such evidence was elucidated by the direct comparison of extracellular metabolic profiles of MLF+ and MLF- yeast phenotypes. Untargeted metabolomics combining ultrahigh-resolution FT-ICR-MS analysis, powerful machine learning methods and a comprehensive wine metabolite database, discovered around 800 putative biomarkers and 2500 unknown masses involved in phenotypic distinction.