Determination and identification of polyphenols in wine using mass spectrometry techniques
Bibliographic record
Abstract
Mass spectrometry is crucial for analysing physicochemical and sensory properties, including colour, astringency, taste, and flavour, predicting ageing characteristics, and addressing stability issues in wine. Polyphenols are key chemical constituents in wine that are associated with health benefits and improve circulatory conditions. Advances in mass spectrometry ionisation techniques such as matrix-assisted laser desorption and ionisation and direct analysis in real-time offer high sensitivity for identifying important polyphenolic constituents in wine. High-resolution mass spectrometry, in combination with liquid chromatography, accurately identify and quantify polyphenolic compounds, even at low concentrations, and provides the possibility for further retrospective analysis and non-targeted analysis using statistical methods of data analysis. Ambient mass spectrometry techniques such as paper spray and low-temperature plasma allow solventless analysis, determining the geographical origin, authentication, and quality control of wine samples. This review will explore the potential benefits of using mass spectrometry to identify various polyphenols and polymeric polyphenols in wine, as well as recent developments and applications. Additionally, we will discuss determining antioxidant activity and total polyphenol content in wine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".