Publications Repository - Gdańsk University of Technology

The potential of ultra-fast gas chromatography (GC) combined with chemometric analysis for classification of wine originating from Poland according to the variety of grape used for production was investigated. A total of 44 Polish wine samples differing in the type of grape (and grape growth region) used for the production as well as parameters of the fermentation process, alcohol content, sweetness, and others which characterize wine samples were analysed. The selected features coming from ultra-fast GC analysis were subsequently used as inputs for both principal component analysis (PCA) and supervised machine learning. Using the proposed classification algorithm, it was possible to classify white and red wines according to the variety of grape used for production with a 98.7 and 98.2% accuracy, respectively. The model was characterized by good recall and area under receiver operating characteristic which was 1.000 for white wines and 0.992 for red wines. Cuveé wines (made from various types of grapes) were also successfully classified which leads to the conclusion that the proposed classification method can be used not only to differentiate between wines made from different grapes but also to detect possible adulterations, provided known; non-adulterated samples are available as a reference. The model was also used to classify wine samples based on other features, such as the geographic region in which the vineyard is situated, type of yeast used, the temperature of fermentation, sweetness, etc. In all cases, a high classification accuracy (in most cases > 90%) was achieved. The obtained results could be applied in the wine industry.

Authors

• dr inż. Tomasz Majchrzak link open in new tab ,
• dr inż. Wojciech Wojnowski link open in new tab ,
• dr hab. inż. Justyna Płotka-Wasylka link open in new tab