Wine varietal thiols are important contributors to wine aroma. The chemical nature of thiols makes them difficult to measure due to low concentrations, high sensitivity to oxidation, and low ionization. Methods for the measurement of thiols usually consist of multiple steps of sample preparation followed by instrumental measurement. Studies have collected large datasets of thiols in white wine but not in red wine, due to the lack of availability of suitable methods. In this study, for the first time, convergence chromatography was used to measure thiols in red wine at ultratrace levels with improved sensitivity compared to previous methods. Performance parameters (selectivity, linearity, limits of detection, precision, accuracy) were tested to demonstrate the suitability of the method for the proposed application. Red wine thiols were measured in South African Pinotage, Shiraz, and Cabernet Sauvignon wines (n = 16 each). Cultivar differentiation using the thiol profile was demonstrated.

Enological evaluations capture the chemical and sensory space of wine using different techniques; many sensory methods as well as a variety of analytical chemistry techniques contribute to the amount of information generated. Data fusion, especially integrating data sets, is important when working with complex systems. The success reported when trying to integrate different modalities is generally low and has been attributed to the lack of statistically considerate strategies focusing on the data handling process. Multiple stages of data handling must be carefully considered when dealing with multi-modal data. In this review, the different stages in the data analysis process were examined. The study revealed misconceptions surrounding the process and elucidated rules for purpose-driven approaches by examining the complexities of each stage and the impact the decisions made at each stage have on the resulting models. The two major modeling approaches are either supervised (discrimination, classification, prediction) or unsupervised (exploration). Supervised approaches were emphatic on the pre-processing steps and prioritized increasing performance. Unsupervised approaches were mostly used for preliminary steps. The review found aspects often neglected when it came to the data collection and capturing which in the end contributed to the low success in combining sensory and chemistry data.

In oenology, statistical analyses are used for descriptive purposes, mostly with separate sensory and chemistry data sets. Cases that combine them are mostly supervised, usually seeking to optimize discrimination, classification, or prediction power. Unsupervised methods are used as preliminary steps to achieving success in supervised models. However, there is potential for unsupervised methods to combine different data sets into comprehensive, information-rich models. This study detailed stepwise strategies for creating data fusion models using unsupervised techniques at different levels. Principal component analysis (PCA) and multiple factor analysis (MFA) were used to combine five data blocks (four chemistry and one sensory). The model efficiency and configurational similarity were evaluated using eigenvalues and regression vector (RV) coefficients, respectively. The MFA models were less efficient than PCA, having gradual distributions of eigenvalues across model dimensions. The MFA models were more representative than PCA, as indicated by high RV coefficients between MFA and each individual block. Therefore, MFA approaches were better suited for multi-modal data than PCA. This work approached data fusion systematically and showed the type of decisions that must be made and how to evaluate their consequences. Proper integration of data sets, instead of concatenation, is an important aspect to consider in multi-modal data fusion.

Although South African vineyards are still young by European standards, there is a belief in the industry that vines aged 35 or more years produce grapes and wines with specific characteristics (“old vine wines”). The aim of this study was to investigate the existence of the concept of old vine Chenin blanc wines using a typicality rating and sorting tasks. Chenin blanc wines were made from grapes harvested from vines aged five to 45 years old. Winemaking was standardised, with no wood contact. Typicality rating and sorting tasks were performed on young (first-stage) and two-year bottle-aged (second-stage) wines. Principal component analysis (PCA) on rating data demonstrated judge consensus, but no correlation was found between vine age and typicality rating. Sorting results were submitted to agglomerative hierarchical clustering (AHC) performed on the correspondence analysis (CA) and multidimensional scaling (MDS) results for grouping and attributes resulting from the sorting task. The clusters were different for the young wines and two-year bottle-aged wines. The verbal aspect of the sorting demonstrated the judges’ agreement on the concept of old vine Chenin blanc, shown by the annotation of the old vine group as ‘complex’, ‘balance’, ‘rich’ and ‘good mouthfeel’. However, because the judges did not sort the wines according to vine age, the perceptual aspect of the concept could not be confirmed, its features could not be tested further, and the sensory space could not be built.