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.

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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.Abbreviations: RV (regression vector); PCA (principal component analysis); MDS (multidimensional scaling); CA (correspondence analysis); AHC (agglomerative hierarchical clustering); DA (descriptive analysis); CATA (check all that apply)

The production process of South African bottle-fermented sparkling wine, the Méthode Cap Classique (MCC), follows the traditional French method (méthode champenoise), although each cellar has its own unique additions to the method. South African winemakers use different techniques and blends to achieve their award-winning MCCs, but there have not been many scientific investigations of the science behind these wines. This project is one of the first scientific studies on MCC. MCC wines were made using Chardonnay and Pinot Noir grapes harvested over two vintages (2014 and 2015) from two regions (Robertson and Darling) and stored at 0°C, 10°C, 25°C and 30°C before processing. The study was aimed at investigating the effect of grape storage temperature on the sensory characteristics of MCCs. The aroma and taste of the final nine-month old MCCs were evaluated, with each region analysed separately. The study showed a grouping of the MCCs according to temperature treatments for both vintages. There were vintage differences in terms of the attributes cited and the frequency of citations. Based on the frequency of citation, the MCCs made 2014 from grapes stored at 0°C and 10°C were described by the judges as having a fruity, fresh and crisp aroma, whilst those made from grapes stored at 25°C and 30°C were described as having oxidised fruit, volatile acidity and solvent-like aromas. The judges perceived less oxidation and volatile acidity (VA) (in terms of the frequency of citation) in the aroma of the 2015 MCCs, although treatments at higher temperatures were still associated with less desirable attributes compared to treatments at lower temperature. This study shown that the temperature of the grape at the time of processing has a significant effect on the aroma of MCCs aged nine months, and not so much of an effect on the taste.

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.