France is the largest exporter of wine in the world. The export turnover is estimated at 8.7 billion euros in 2017 for 13 million hectoliters sold. This lucrative business pushes scammers to increase the value of some low-end wines by cheating on their appellations, quality or even their origins. These facts lead to losing 1.3 billion euros each year to the European Union’s wine and spirits companies.

During Champagne or sparkling wine tasting, gas-phase CO2 and volatile organic compounds invade the headspace above glasses [1], thus progressively modifying the chemical space perceived by the consumer. Gas-phase CO2 in excess can even cause a very unpleasant tingling sensation perturbing both ortho- and retronasal olfactory perception [2]. Monitoring as accurately as possible the level of gas-phase CO2 above glasses is therefore a challenge of importance aimed at better understanding the close relationship between the release of CO2 and a collection of various tasting parameters.

It is quite common nowadays to carry out analyses which allow to control the ageing of spirits that are aged in wood casks. Many control parameters have been previously studied, such as the concentration of different phenolic compounds or the Total Polyphenol Index, in order to better understand the ageing process of wood aged spirits. On the other hand, it is frequent to analyse as a physical parameter the colour of those spirit samples, by stating them as an array of three coordinates from various colour spaces as CIE L*a*b* or CIE L*C*H*.

The assessment of wine authenticity is of great importance for consumers, producers and regulatory agencies to guarantee the geographical origin of wines and grape variety as well. Since mid-infrared (MIR) spectroscopy with chemometrics represent a suitable tool to ascertain the wine composition, including features associated with the polyphenolic compounds, the aim of this study was to generate MIR spectra of red wines to be exploited for classification of red wines based on the relationship between grape variety and wine composition. Several multivariate data analyses were used, including Principal Component Analysis (PCA), Discriminant Analysis (DA), Support Vector Machine (SVM), and Soft Intelligent Modelling of Class Analogy (SIMCA).