Determination and identification of polyphenols in wine using mass spectrometry techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it