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Profiling complex chemical mixtures provides a unique opportunity for outreach and education. Here, we develop a teaching module using one-dimensional 1H nuclear magnetic resonance (NMR) spectroscopy to study wine. The demonstration is delivered at the Georgia Tech NMR Center during the Atlanta Science Festival to 127 visitors from diverse backgrounds, including families with K–12 children. Participants interact with wine samples through cognitive and sensory experiences. Visitors engage with a live demonstration of a high-field NMR spectrometer, including real-time measurement and automated analysis of 1H NMR spectra for 14 wines. The module enables instructors to showcase how an NMR spectrometer works, quantify 70 wine metabolites, and discuss how chemical composition relates to wine characteristics. Practical examples highlight how 1H NMR can detect wine fraud. An exit survey suggests that the module increases public interest and excitement for research. Adults report learning factors affecting wine characteristics and the basics for how NMR is applied to research. K–12 students retain information about wine composition and enjoy sensory aspects of the activity. A description of the demonstration’s design and implementation is provided to facilitate its adoption in outreach efforts and introductory chemistry courses.
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Unraveling the Regional Specificities of Malbec Wines from Mendoza, Argentina, and from Northern California
This study explores the relationships between chemical and sensory characteristics of wines in connection with their regions of production. The objective is to identify whether such characteristics are significant enough to serve as signatures of a terroir for wines, thereby supporting the concept of regionality. We argue that the relationships between characteristics and regions of production for the set of wines under study are rendered complicated by possible non-linear relationships between the characteristics themselves. Consequently, we propose a new approach for performing the analysis of the wine data that relies on these relationships instead of trying to circumvent them. This new approach follows two steps: We first cluster the measurements for each characteristic (chemical, or sensory) independently. We then assign a distance between two features to be the mutual entropy of the clustering results they generate. The set of characteristics is then clustered using this distance measure. The result of this clustering is a set of sub-groups of characteristics, such that two characteristics in the same group carry similar, i.e., synergetic information with respect to the wines under study. Those wines are then analyzed separately on the different sub groups of features. We have used this method to analyze the similarities and differences between Malbec wines from Argentina and California, as well as the similarities and differences between sub-regions of those two main wine producing countries. We report detection of groups of features that characterize the origins of the different wines included in the study. We note stronger evidence of regionality for Argentinian Malbec wines than for Californian wines, at least for the sub regions of production included in this study.
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- Award ID(s):
- 1719582
- PAR ID:
- 10096133
- Date Published:
- Journal Name:
- Agronomy
- Volume:
- 9
- Issue:
- 5
- ISSN:
- 2073-4395
- Page Range / eLocation ID:
- 234
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/agronomy9050234
