Alternative Perspective on Rapid Wine Oxidation through Changes in Gas-Phase Volatile Concentrations, Highlighted by Matrix Component Effects.

A new perspective is presented to investigate the sensorially relevant gas-phase concentrations of volatile compounds in wine. This is achieved by measuring the partition coefficients and matrix-phase concentrations of volatiles using static headspace-gas chromatography-ion mobility spectrometry. Physicochemical properties that can contribute to the partition behaviors of 10 volatile esters, such as hydrophobicity and matrix temperature, are also discussed. Partition coefficients are then linked to quantitative measurements to obtain partial pressures, which describe the availability of volatile compounds in the gas phase. The concept of partition coefficients and partial pressure has then been applied to a time series of aroma changes due to oxidation in commercial wines. As a follow-up study, a full factorial design was devised to inspect the impact of three common wine matrix components, namely, copper, polyphenols, and ascorbic acid, on the partial pressure changes after 30-day oxidation treatment in either full-alcohol or low-alcohol simulated wine matrices. Interesting interactive effects between antioxidant behaviors and alcohol levels were elucidated, especially around the controversial use of ascorbic acid in winemaking. These results can guide winemakers who wish to minimize oxidative damage to wine aroma during wine storage or bulk transport, where ullage may be present or continual oxygen ingress may be occurring.

Validation Study on the Simultaneous Quantitation of Multiple Wine Aroma Compounds with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry.

A new quantitative method based on static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) is proposed, which enables the simultaneous quantitation of multiple aroma compounds in wine. The method was first evaluated for its stability and the necessity of using internal standards as a quality control measure. The two major hurdles in applying GC-IMS in quantitation studies, namely, nonlinearity and multiple ion species, were also investigated using the Boltzmann function and generalized additive model (GAM) as potential solutions. Metrics characterizing the model performance, including root mean squared error, bias, limit of detection, limit of quantitation, repeatability, reproducibility, and recovery, were investigated. Both nonlinear fitting methods, Boltzmann function and GAM, were able to return desirable analytical outcomes with an acceptable range of error. Potential pitfalls that would cause inaccurate quantitation, that is, effects of ethanol content and competitive ionization, were also discussed. The performance of the SHS-GC-IMS method was subsequently compared against that of a currently established method, namely, GC-MS, using commercial wine samples. These findings provide an initial validation of a GC-IMS-based quantitation method, as well as a starting point for further enhancing the analytical scope of GC-IMS.

Volatile-Based Prediction of Sauvignon Blanc Quality Gradings with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS) and Interpretable Machine Learning Techniques.

The analytical scope of static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) was applied to wine aroma analysis for the first time. The method parameters were first fine-tuned to achieve optimal analytical results, before the method stability was demonstrated, in terms of repeatability and reproducibility. Succinct qualitative identification of compounds was also realized, with the identification of several volatiles that have seldom been described previously in Sauvignon Blanc wine, such as methyl acetate, ethyl formate, and amyl acetate. Using the SHS-GC-IMS data in an untargeted approach, computer modeling of large datasets was applied to link aroma chemistry via prediction models to wine sensory quality gradings. Six machine learning models were compared, and artificial neural network (ANN) returned the most promising performance with a prediction accuracy of 95.4%. Despite its inherent complexity, the ANN model offered intriguing insights on the influential volatiles that correlated well with higher and lower sensory gradings. These findings could, in the future, guide winemakers in establishing wine quality, particularly during blending operations prior to bottling.

New precursor of 3-mercaptohexan-1-ol in grape juice: thiol-forming potential and kinetics during early stages of must fermentation.

Two volatile thiols, 3-mercaptohexan-1-ol (3MH) and 3-mercaptohexyl acetate (3MHA), are key aroma impact compounds in many young white wines, especially of the variety Sauvignon blanc (SB). Although great effort has been invested to identify their precursors in recent years, the origin of the majority of 3MH and 3MHA generated during wine fermentation still cannot be explained. Here we demonstrate that supplying an external source of hydrogen sulfide to grape juice hugely increases its thiol-forming potential. We further describe the discovery of (E)-2-hexen-1-ol as an additional new thiol precursor and demonstrate that it possesses, together with (E)-2-hexenal, an immense thiol-forming potential during fermentation. Both C6-compounds are extremely rapidly metabolized by yeast during the first hours after inoculation, even under commercial conditions, and can be interconverted during this phase depending on their initial concentration in the grape juice. Spiking grape juice with additional acetaldehyde greatly enhanced the (E)-2-hexen-1-ol to (E)-2-hexenal conversion rate. Delaying the metabolization of the two unsaturated C6-thiol precursors by yeast, at the same time as increasing hydrogen sulfide production early in fermentation, opens up a great opportunity to tap into this enormous potential 3MH and 3MHA source in grape juice and extends the possibility of thiol production to other non-grape-based alcoholic beverages as well.

Evaluation of key odorants in sauvignon blanc wines using three different methodologies.

In this study three different approaches were employed to identify key odorants in Sauvignon blanc wines. First, the concentrations of the odorants were compared to their respective aroma detection thresholds. The resulting odor activity values (OAV) were transformed into a normalized and weighted measure that allows the aroma profiles of different wines to be compared and the contribution of a single aroma in a complex mixture to be evaluated. Based on their OAV, 3-mercaptohexanol and 3-mercaptohexyl acetate were the two most important aroma compounds in many Marlborough Sauvignon blanc wines. Due to limitations with the OAV approach, the study was extended to include aroma extract dilution analysis (AEDA), which revealed that ß-damascenone, together with the varietal thiols, esters, and higher alcohols, are key odorants in Sauvignon blanc wines. The final approach undertaken was aroma reconstitution and omission tests using a deodorized wine base and the creation of a model Marlborough Sauvignon blanc. Single compounds and groups of compounds were omitted from the model to study their impact on the sensory properties of the model wine. Reconstitution and omission confirmed that varietal thiols, esters, terpenes, and ß-damascenone are all important contributors to Sauvignon blanc aroma. The methoxypyrazines showed an important but relatively low impact in all three of the approaches undertaken in this study.