Descriptor Fingerprints and Their Application to WhiteWine Clustering and Discrimination.


This study continues the attempt to use the statistical process for a large-scale analytical data. A group of 3898 white wines, each with 11 analytical laboratory benchmarks was analyzed by a fingerprint similarity search in order to be grouped into separate clusters. A characterization of the wine’s quality in each individual cluster was carried out according to individual laboratory parameters.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1]. Ebeler, S., Flavor Chemistry - Therty Years of Progress, Kluwer Academic Publishers, 1999, 409-422.

  • [2]. Legin, A. A.; Rudnitskaya, L.; Luvova, Y.; Vlasov C., Natale and D'Amico, A., Evaluation of Italian wine by the electronic tongue: recognition, quantitative analysis and correlation with human sensory perception, Analytica Chimica Acta2003, 484(1), 33-34.

  • [3]. Cortez, P.; Cerdeira, A.; Almeida, F.; Matos, T.; Reis, J., Modeling wine preferences by data mining from hysicochemical properties,

  • [4]. Bangov, I.; Moskovkina, M.; Stojanov, B.; Descriptor Fingerprints and Their Application to Red Wine Clustering and Discrimination, Acta Scientifica Naturalis,2017, 1, 29-34.

  • [5]. Butina, D., Unsupervised data base clustering based on Daylight’s fingerprint and Tanimoto similarity: A fast and automated way to cluster small and large data sets, J. Chem. Inf. Comput. Scie.,1999. 39, 747 - 750.


Journal + Issues