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Application of bivariate statistics to full wine bottle diamagnetic screening data
S J Harley1, V Lim, M P Augustine
1Department of Chemistry, One Shields Avenue, University of California, Davis, CA 95616, USA.
Talanta
|January 31, 2012
Summary
This study uses bivariate statistics on wine diamagnetic screening data to identify patterns. The method shows potential for detecting counterfeit wines by analyzing transport and storage effects.
Area of Science:
- Analytical Chemistry
- Statistical Modeling
Background:
- Diamagnetic screening measurements of wine bottles can reveal subtle differences.
- Previous studies suggested clustering of similar wines based on principal component analysis of screening data.
Purpose of the Study:
- To apply a bivariate correlated Student distribution to a larger dataset of wine diamagnetic screening measurements.
- To investigate the influence of wine transport and storage on diamagnetic screening data.
- To assess the potential of this method for counterfeit wine identification.
Main Methods:
- Full bottle diamagnetic screening of sixty wines.
- Principal component analysis (PCA) for data reduction.
- Application of bivariate correlated Student distribution for statistical analysis.
Main Results:
- The study successfully applied bivariate statistics to a large wine dataset.
- Observed effects of wine transport and storage on diamagnetic screening patterns.
- Demonstrated clustering of like wines in principal component score plots.
Conclusions:
- Bivariate correlated Student distribution is a viable statistical tool for analyzing wine diamagnetic screening data.
- The method shows promise for identifying counterfeit wines.
- Further research is warranted to refine the technique for authentication purposes.
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