Cava is a sparkling wine obtained by a secondary fermentation in its own bottle. Grape skin contains several compounds, such as polyphenols, which act like natural protectors and provide flavor and color to the wines. In this paper, a previously optimized method based on reversed phase high performance liquid chromatography (HPLC) with ultraviolet/visible (UV/Vis) detection has been applied to determine polyphenols in cava wines. Compounds have been separated in a C₁₈ core-shell column using 0.1% formic acid aqueous solution and methanol as the components of the mobile phase. Chromatograms have been recorded at 280, 310 and 370 nm to gain information on the composition of benzoic acids, hidroxycinnamic acids and flavonoids, respectively. HPLC-UV/vis data consisting of compositional profiles of relevant analytes has been exploited to characterize cava wines produced from different base wine blends using chemometrics. Other oenological variables, such as vintage, aging or malolatic fermentation, have been fixed over all the samples to avoid their influence on the description. Principal component analysis and other statistic methods have been used to extract of the underlying information, providing an excellent discrimination of samples according to grape varieties and coupages.

Samples from the different processing stages in the elaboration of sparkling wine (cava)—including must, base wine, and sparkling wine—of Pinot Noir and Xarel·lo grape varieties from different vineyard qualities (A, B, C, D) have been analyzed by inductively coupled plasma (ICP) techniques to determine their elemental composition. The resulting data has been used to characterize these products according to oenological features and product qualities. For this purpose, box plot diagrams, bar charts, and principal components analysis (PCA) have been used. The study of the behavior of each given species has pointed out the relevance of some elements as markers or descriptors of winemaking processes. Among others, Cu and K are abundant in musts and their concentrations progressively decrease through the cava production process. S levels suddenly increase at the base wine step (and further decay) due to the addition of sulfites as preserving agents. Finally, concentrations of Na, Ca, Fe, and Mg increase from the first fermentation due to the addition of clarifying agents such as bentonite. PCA has been applied to try to extract solid and global conclusions on trends and chemical markers within the groups of samples more easily and efficiently than more conventional approaches.

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.

The biogenic amine (BA) content in wines is dependent on the fermentation processes and other oenological practices, as well as on grape quality. These compounds can participate in different cellular functions in humans; however, the intake of high amounts can provoke some toxicological effects. For that reason, controlling the evolution of biogenic amines in wine production processes is of extreme importance. This work aims to assess the occurrence of biogenic amines in sparkling wines and related samples, including musts, base wines, stabilized wines, and three-month and seven-month aged sparkling wines obtained from Pinot Noir and Xarel lo grape varieties. The determination of BA content relies on liquid chromatography with fluorescence detection (HPLC–FLD) with precolumn derivatization of analytes with dansyl chloride. The analysis has shown that putrescine is the most abundant amine in these types of samples. Ethanolamine, tyramine, spermine, and histamine concentrations are also remarkable. Principal component analysis has been applied to try to extract featured information concerning overall patterns dealing with wine production steps and qualities. Interesting conclusions have been drawn on BA formation depending on different factors. BA concentrations are quite low in must but rise, especially after the first alcoholic fermentation. Moreover, BA levels are much lower in the range of products elaborated with grapes of the best qualities while they significantly increase when using grapes of lower qualities. The results obtained pointed out the analytical potential of using BAs to control the quality of wine and its production processes, thus providing valuable information for both wineries and consumers.

This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.