A multivariate approach using attenuated total reflectance mid-infrared spectroscopy to measure the surface mannoproteins and β-glucans of yeast cell walls during wine fermentations

Must oxidation is a complex process involving multiple enzymatic transformations, including the oxidation of phenolics containing an ortho-diphenol function. The latter process has a primary influence on wine aroma characteristics and stability, due to the central role of ortho-diphenols in the non-enzymatic oxidative reactions taking place during winemaking and in finished wine. Although oxidation of must is traditionally avoided, in recent years its contribution to wine quality has been revisited, and in some cases improvements to wine aroma have been observed with the application of controlled must oxidation. Nowadays there is a great interest in the wine industry towards the identification of specific markers or patterns to characterize and classify the response of grape must to oxidation.

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.

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.

Nitrogen is an important nutrient of yeast and its low content in grape must is a major cause for sluggish fermentations. To prevent problems during fermentation, a supplementation of the must with ammonium salts or more complex nitrogen mixtures is practiced in the cellar. However this correction seems to improve only partially the quality of wine [1]. In fact, yeast is using nitrogen in many of its metabolic pathways and depending of the sort of the nitrogen source (ammonium or amino acids) it produces different flavor active compounds. A limitation in amino acids can lead to a change in the metabolic pathways of yeast and consequently alter wine quality.

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.