The use of epifluorescence versus plating to monitor the effect of different parameters on microorganisms in wine

Malolactic fermentation (MLF)¹, the decarboxylation of L-malic acid into L-lactic acid, is performed by lactic acid bacteria (LAB). MLF has a deacidifying effect that may compromise freshness or microbiological stability in wines² and can be inhibited by fumaric acid [E297] (FA). In wine, can be added at a maximum allowable dose of 0.6 g/L³. Its inhibition with FA is being studied as an alternative strategy to minimize added doses of SO₂⁴. In addition, wine yeasts are capable of metabolizing and storing small amounts of FA and during alcoholic fermentation (AF).

Validation of the phloroglucinolysis and RP-HPLC method showed selectivity and repeatability within acceptable limits for all investigated matrices. Recovery of polymeric phenols by SPE was also acceptable.

To study the copigmentation between different wine flavonols (myricetin, quercetin, kaempferol, isorhamnetin and syringetin 3-O-glucosides) and malvidin

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.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.