In this study, 1H-NMR spectroscopy coupled with chemometrics was applied to study the wine metabolome and to classify wines according to different grape varieties and different terroirs. By obtaining the metabolomic fingerprinting and profiling of the wines, it was possible to assess the metabolic biomarkers leading the classification (i.e., phenolic compounds, aroma compounds, amino acids, and organic acids). Moreover, information about the influence of the soil in shaping wine metabolome was obtained. For instance, the relationship between the soil texture and the content of amino acids and organic acids in wines was highlighted. The analysis conducted in this study allowed extraction of relevant spectral information not only from the most populated and concentrated spectral areas (e.g., aliphatic and carbinolic areas), but also from crowded spectral areas held by lowly concentrated compounds (i.e., polyphenols). This may be due to a successful combination between the parameters used for data reduction, preprocessing and elaboration. The metabolomic fingerprinting also allowed exploration of the H-bonds network inside the wines, which affects both gustatory and olfactory perceptions, by modulating the way how solutes interact with the human sensory receptors. These findings may have important implications in the context of food traceability and quality control, providing information about the chemical composition and biomolecular markers from a holistic point of view.

Bambina, P., Spinella, A., Lo Papa, G., Chillura Martino, D.F., Lo Meo, P., Cinquanta, L., et al. (2024). 1H-NMR Spectroscopy Coupled with Chemometrics to Classify Wines According to Different Grape Varieties and Different Terroirs. AGRICULTURE, 14(5), 1-21 [10.3390/agriculture14050749].

1H-NMR Spectroscopy Coupled with Chemometrics to Classify Wines According to Different Grape Varieties and Different Terroirs

Bambina, Paola;Spinella, Alberto;Lo Papa, Giuseppe;Chillura Martino, Delia Francesca;Lo Meo, Paolo;Cinquanta, Luciano;Conte, Pellegrino
2024-05-11

Abstract

In this study, 1H-NMR spectroscopy coupled with chemometrics was applied to study the wine metabolome and to classify wines according to different grape varieties and different terroirs. By obtaining the metabolomic fingerprinting and profiling of the wines, it was possible to assess the metabolic biomarkers leading the classification (i.e., phenolic compounds, aroma compounds, amino acids, and organic acids). Moreover, information about the influence of the soil in shaping wine metabolome was obtained. For instance, the relationship between the soil texture and the content of amino acids and organic acids in wines was highlighted. The analysis conducted in this study allowed extraction of relevant spectral information not only from the most populated and concentrated spectral areas (e.g., aliphatic and carbinolic areas), but also from crowded spectral areas held by lowly concentrated compounds (i.e., polyphenols). This may be due to a successful combination between the parameters used for data reduction, preprocessing and elaboration. The metabolomic fingerprinting also allowed exploration of the H-bonds network inside the wines, which affects both gustatory and olfactory perceptions, by modulating the way how solutes interact with the human sensory receptors. These findings may have important implications in the context of food traceability and quality control, providing information about the chemical composition and biomolecular markers from a holistic point of view.
11-mag-2024
Settore AGR/13 - Chimica Agraria
Settore AGR/15 - Scienze E Tecnologie Alimentari
Settore CHIM/02 - Chimica Fisica
Settore CHIM/06 - Chimica Organica
Bambina, P., Spinella, A., Lo Papa, G., Chillura Martino, D.F., Lo Meo, P., Cinquanta, L., et al. (2024). 1H-NMR Spectroscopy Coupled with Chemometrics to Classify Wines According to Different Grape Varieties and Different Terroirs. AGRICULTURE, 14(5), 1-21 [10.3390/agriculture14050749].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/636373
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