Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage

Alexandra A. Crook; Diana Zamora-Olivares; Fatema Bhinderwala; Jade Woods; Michelle Winkler; Sebastian Rivera; Cassandra E. Shannon; Holden R. Wagner; Deborah L. Zhuang; Jessica E. Lynch; Nathan R. Berryhill; Ron C. Runnebaum; Eric V. Anslyn; Robert Powers

doi:10.1016/j.foodchem.2021.129531

Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage

Alexandra A. Crook, Diana Zamora-Olivares, Fatema Bhinderwala, Jade Woods, Michelle Winkler, Sebastian Rivera, Cassandra E. Shannon, Holden R. Wagner, Deborah L. Zhuang, Jessica E. Lynch, Nathan R. Berryhill, Ron C. Runnebaum, Eric V. Anslyn, Robert Powers

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. ‘Pinot noir’ wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D ¹H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

Original language	English (US)
Article number	129531
Journal	Food Chemistry
Volume	354
DOIs	https://doi.org/10.1016/j.foodchem.2021.129531
State	Published - Aug 30 2021

Keywords

Chemometrics
Differential sensing
Metabolomics
NMR
Pinot noir
Wine

ASJC Scopus subject areas

Analytical Chemistry
Food Science

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10.1016/j.foodchem.2021.129531

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Crook, A. A., Zamora-Olivares, D., Bhinderwala, F., Woods, J., Winkler, M., Rivera, S., Shannon, C. E., Wagner, H. R., Zhuang, D. L., Lynch, J. E., Berryhill, N. R., Runnebaum, R. C., Anslyn, E. V., & Powers, R. (2021). Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage. Food Chemistry, 354, [129531]. https://doi.org/10.1016/j.foodchem.2021.129531

Crook, AA, Zamora-Olivares, D, Bhinderwala, F, Woods, J, Winkler, M, Rivera, S, Shannon, CE, Wagner, HR, Zhuang, DL, Lynch, JE, Berryhill, NR, Runnebaum, RC, Anslyn, EV & Powers, R 2021, 'Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage', Food Chemistry, vol. 354, 129531. https://doi.org/10.1016/j.foodchem.2021.129531

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title = "Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage",

abstract = "Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. {\textquoteleft}Pinot noir{\textquoteright} wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D 1H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.",

keywords = "Chemometrics, Differential sensing, Metabolomics, NMR, Pinot noir, Wine",

author = "Crook, {Alexandra A.} and Diana Zamora-Olivares and Fatema Bhinderwala and Jade Woods and Michelle Winkler and Sebastian Rivera and Shannon, {Cassandra E.} and Wagner, {Holden R.} and Zhuang, {Deborah L.} and Lynch, {Jessica E.} and Berryhill, {Nathan R.} and Runnebaum, {Ron C.} and Anslyn, {Eric V.} and Robert Powers",

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year = "2021",

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doi = "10.1016/j.foodchem.2021.129531",

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AU - Bhinderwala, Fatema

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AU - Winkler, Michelle

AU - Rivera, Sebastian

AU - Shannon, Cassandra E.

AU - Wagner, Holden R.

AU - Zhuang, Deborah L.

AU - Lynch, Jessica E.

AU - Berryhill, Nathan R.

AU - Runnebaum, Ron C.

AU - Anslyn, Eric V.

AU - Powers, Robert

PY - 2021/8/30

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N2 - Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. ‘Pinot noir’ wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D 1H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

AB - Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. ‘Pinot noir’ wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D 1H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

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KW - Metabolomics

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Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage

Abstract

Keywords

ASJC Scopus subject areas

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