Uncorking white wine liking: Combining analytical chemistry and chemometrics with crowd-sourced data to predict quality ratings
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View abstract on PubMed
Summary
This summary is machine-generated.This study links wine
Area Of Science
- Enology and Viticulture
- Food Chemistry
- Consumer Science
Background
- Consumer perception of wine quality is complex, often influenced by extrinsic factors.
- Objective chemical analysis is needed to complement subjective consumer ratings.
- Crowd-sourced data, like Vivino ratings, offers a large-scale perspective on consumer liking.
Purpose Of The Study
- To investigate the correlation between the chemical composition of German white wines and consumer-perceived quality.
- To utilize Vivino ratings as a proxy for consumer liking and quality endpoints.
- To develop a predictive model for wine quality based on chemical profiles.
Main Methods
- Analysis of German white wines using gas chromatography-mass spectrometry (GC-MS) and Fourier transform infrared spectroscopy (FTIR).
- Application of mid-level data fusion and variable selection techniques.
- Construction of a Partial Least Squares (PLS) regression model to predict Vivino quality ratings.
Main Results
- Significant correlations were found between specific chemical properties and consumer-perceived quality.
- The developed PLS model successfully predicted wine quality ratings based on chemical profiles.
- Chemical composition is a valid predictor of consumer liking in wines.
Conclusions
- Wine's chemical makeup can be used to predict consumer-perceived quality.
- This research validates the use of chemical analysis for objective wine quality assessment.
- Further development of predictive models holds promise for the wine industry.
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