Merging fast sensory profiling with non-targeted GC-MS analysis for multifactorial experimental wine making

Copper and iron are known to substantially impact wine stability through oxidative, reductive or colloidal phenomena. However, the binding of metal ions to different wine components under wine conditions, and the impact of this binding on the ability of the metal ions to induce spoilage processes, is not well understood. This study surveyed a range of red and white wines for an understanding of the variability of broad metal categories within the wines. The techniques utilized included an electrochemical constant current stripping potentiometry technique (ccSP), and solid phase extraction (SPE) fractionation of wine with subsequent analysis of the metal content of each fraction by inductively coupled plasma – optical emission spectroscopy (ICP-OES).

Bioactive polyphenols from grapes and wines, like stilbenes and flavonols (SaF), are often determined to nutritional evaluation, but also for many other purposes. The objective of this study was to quantify SaF in red wines from “Campanha Gaúcha”, a large and young viticultural region from South Brazil. Moreover, through statistical analysis, evaluate the influence of these compounds according to varieties, production process, harvest years and micro-regions of cultivation. A total of 58 samples of red wines were analyzed by high-performance liquid chromatography coupled to diode array detector (HPLC-DAD) for determination of trans-resveratrol (R), quercetin (Q), myricetin (M), kaempferol (K), trans-e-viniferin (V) and their precursor, cinnamic acid (C).

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.

“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.

Colour is an important quality parameter in wines and is the result of a complex mixture of pigments
(including anthocyanins and their derivatives, quinones, xanthyllium compounds, etc.). Red wine colour changes over time as pigments react between themselves and with other wine macromolecules
(particularly polyphenols). During wine tasting, colour is normally assessed on the outer rim of the wine profile in a tilted glass, since most wines are too opaque to be analysed in the middle of the glass. Therefore, depending on the depth of observation considered, the perception of wine colour can be different.