Multivariate Statistical Analysis of the UV-Vis Profiles of Wine Polyphenolic Extracts during Vinification
Bibliographic record
Abstract
Multivariate Statistical data Analysis (MVA) was used to study the changes undergone by the UV-Vis spectral profiles of polyphenolic components during vinification. Two Sardinian wines, from the white cultivar “Vermentino” and from the red “Cannonau”, were studied. The wine samples were collected at different times from the fermentation vessels and the UV-Vis spectra of their solid phase extracts (SPE) were submitted to PCA (Principal Component Analysis) and to Orthogonal Projections to Latent Structures (OPLS) regression analysis in order to look for the main source of variability present in the spectra in terms of grape typology and winemaking times. In Vermentino, during the first weeks of vinification the prevalent modifications regarded the increase of the bands at 280 and 350 nm, attributed to HBA, stilbenes flavan-3-ols and glycosylated flavonols, respectively; at approximately 60 days a bathocromic shift of the band at 350 nm towards 380 nm took place, suggesting hydrolysis of flavonols. In Cannonau, the band at 536 nm, attributed to anthocyanins, that showed a consistent increase in the first days of vinification, after 11 days dropped significantly, exhibiting also a bathochromic shift. The band at 380 nm was linearly positively correlated with time. Our results support the potential use of this approach for qualitatively monitoring the winemaking process.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".