Taurasi DOCG Wines from the Campania Region: A Pilot Study with an AI Approach on a Local Grape Cultivar
Bibliographic record
Abstract
Background: The aging evolution of Taurasi, an Aglianico-based DOCG wine from southern Italy, has been comprehensively characterized here for the first time. Known for its high levels of bioactive metabolites with potential health benefits, this variety was analyzed using an integrated analytical and computational approach to explore the evolution of its composition during bottle aging. Methods: The analytical framework encompassed physicochemical profiling (density, pH, °Brix) and targeted spectrophotometric analyses for polyphenols (Folin–Ciocâlteu), antioxidant capacity (DPPH, ABTS), and anthocyanins (TAC). The phenolic and volatile fractions were analyzed in detail qualitatively and quantitatively using HPLC-UV-DAD and HS-SPME-GC/MS, respectively. Results: The aging process was characterized by a profound phenolic reorganization, with a significant decrease in monomeric phenols and an accumulation of key phenolic acids and stilbenes. The net result of these opposing trends was an overall increase in antioxidant capacity, a significant decrease in total anthocyanins, and an aromatic shift from primary fruity esters to a complex tertiary bouquet. Additionally, machine learning techniques were employed to predict aging-related variations in antioxidant activity and chemical parameters, establishing links between compositional shifts and potential bioactivity. Conclusions: This study marks the first-ever investigation of Taurasi Aglianico wine aging, offering a novel multidisciplinary approach that provides comprehensive insights into the wine’s chemical evolution. The findings emphasize the potential of Taurasi Aglianico in both enological and health-related contexts, highlighting its unique aging dynamics and bioactive potential.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".