Analysis of polyphenols in wines: correlation between total polyphenolic content and antioxidant potential from photometric measurements. Prediction of cultivars and vintage from capillary zone electrophoresis fingerprints using artificial neural network

• Published in: Journal of chromatography 2005-07, Vol.1081 (1), p.48-54
• Main Authors: Pazourek, J, Gajdosova, D, Spanila, M, Farkova, M, Novotna, K, Havel, J
• Format: Article
• Language: English
• Subjects: antioxidant activity; Antioxidants - analysis; Biological and medical sciences; capillary zone electrophoresis; cultivar identification; Electrophoresis, Capillary - methods; Fermented food industries; Flavonoids - analysis; Flavonoids - isolation & purification; food composition; Food industries; Fundamental and applied biological sciences. Psychology; neural networks; Neural Networks (Computer); Phenols - analysis; Phenols - isolation & purification; photometers; photometry; Photometry - methods; Polyphenols; total antioxidant potential; total polyphenolic content; Wine - analysis; Wine - classification; wine cultivars; wines; Wines and vinegars
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2005.02.056

The polyphenols (some of them are also called phytoalexins, flavonols, flavanons, flavanonols, flavons, flavanols, and anthocyanines) are usually marked as potent antioxidants or radical scavengers which assist the body cells against oxidation. Polyphenols in wine are also considered to explain so called French paradox (long life aging and low number of coronary diseases despite of high alcohol and fat consumption). The total polyphenolic content (TPC) and total antioxidant potential (TAP) were determined by photometry and found strongly correlated. This finding suggests that the determination of TAP can be replaced by a more simple procedure of TPC determination. Capillary zone electrophoresis (CZE) with preconcentration by solid phase extraction (SPE) was applied for some polyphenols determination and for obtaining electropherograms of the SPE extracts (fingerprints). From mathematical evaluation of the fingerprints, prediction of cultivars and vintage using artificial neural networks (ANN) was done with more than 90% correct prediction. The study was performed on a set of 47 samples of young wines (vintage 1999-2002) from south Moravia (Czech Republic) and New South Wales (Australia).