Rapid Method for the Discrimination of Red Wine Cultivars Based on Mid-Infrared Spectroscopy of Phenolic Wine Extracts
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- 作者：Andrea Edelmann ; Josef Diewok ; Kurt Christian Schuster ; and Bernhard Lendl
- 刊名：Journal of Agricultural and Food Chemistry
- 出版年：2001
- 出版时间：March 2001
- 年：2001
- 卷：49
- 期：3
- 页码：1139 - 1145
- 全文大小：130K
- 年卷期：v.49,no.3(March 2001)
- ISSN：1520-5118
文摘
Mid-infrared spectroscopy and UV-vis spectroscopy combined with multivariate data analysis havebeen applied for the discrimination of Austrian red wines, including the cultivars CabernetSauvignon, Merlot, Pinot Noir, Blaufränkisch (Lemberger), St. Laurent, and Zweigelt. Both authenticwines and their phenolic extracts were investigated by attenuated total reflectance (ATR)-mid-infrared spectroscopy. Phenolic extracts were also investigated by UV-vis spectroscopy. The wineextracts were obtained by solid-phase extraction with C-18 columns and elution by methanolcontaining 0.01% hydrochloric acid. Hierarchical cluster analysis was performed with mid-infraredspectra of both wines and extracts, as well as with UV-vis spectra of the phenolic extracts. Dataprocessing involved vector normalization and derivation of the spectra. Due to varying concentrationsof main components including sugar and organic acids, satisfactory classification of untreated wineswas not achieved. However, when using mid-infrared spectra of the phenolic extracts, almostcomplete discrimination of all cultivars investigated was achieved. The use of UV-vis spectroscopyfor cultivar discrimination was found to be limited to the authentication of the Burgundy speciesPinot Noir. In addition, soft independent modeling of class analogy was applied to the mid-infraredspectra of the extracts. It was possible to establish class models for five different wine cultivarsand to classify test samples correctly.Keywords: Wine; polyphenols; anthocyanin; flavonoids; mid-infrared spectroscopy; classification;authentication; cluster analysis; UV-vis spectroscopy; solid-phase extraction; SIMCA; patternrecognition; hierarchical clustering