Biostimulatory Activity of Pyroligneous Acid Enhanced Metabolites Accumulation in Grape Wine
Bibliographic record
Abstract
Pyroligneous acid (PA) is a known biostimulant in agriculture, but its effects on metabolite accumulation in grape berries and wine are not well understood. This study investigated the impact of varying PA concentrations (0%, 2%, 4%, 8%, and 12%) on metabolite profiles in grape wine (Vitis vinifera cv. KWAD7-1). Using a randomized complete block design, PA was applied to grape leaves at 14-day intervals. Wine samples were analyzed using NMR spectroscopy, identifying 52 metabolites across seven compound groups. The 12% PA treatment resulted in the highest o Brix content: 0.14-fold higher than the control. This treatment significantly (p<0.05) altered the concentrations of organic acids, increasing most except for malic and acetic acids, which decreased by 0.16-fold and 0.60-fold, respectively. Notably, 12% PA increased total amino acid content by 5.96- fold compared to the control and enhanced glucose and fructose contents by 0.25- and 1.40-fold, respectively. A 0.53-fold increase in myo-inositol was also observed with 12% PA, suggesting potential improvements in nutritional value. Principal component analysis revealed distinct metabolic profiles for grapes treated with 12% PA, characterized by elevated levels of phenolics, alcohols, volatiles, and carbohydrates. These findings suggest that PA application can be used to manipulate grape wine metabolites, potentially enhancing sensory attributes and nutritional value. This study provides insights into the use of PA as a tool for modulating wine quality to meet consumer preferences.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".