Application of plastic polymers in remediating wine with elevated alkyl-methoxypyrazine levels
Bibliographic record
Abstract
3-Alkyl-2-methoxypyrazines (MPs) are odour-active compounds that elicit atypical green aromas and flavours in some wines, and are resilient to removal using traditional wine-making approaches. They originate either as contaminants from Coccinellidae beetles inadvertently introduced during wine processing ("ladybug taint") or as grape-derived constituents that are undesirable at elevated levels. In this study we investigated the capacity of a selection of plastic polymers to reduce concentrations of three MPs: isopropyl methoxypyrazine (IPMP), secbutyl methoxypyrazine (SBMP) and isobutyl methoxypyrazine (IBMP). In Trial 1, red wine was spiked with IPMP (20 ng/l), SBMP (20 ng/l) and IBMP (20 ng/l), then separately treated with 13 plastic polymers (surface area 350 cm(2)/l). Three polymers were then identified for further testing based on the results from Trial 1: silicone, ethylene and vinyl acetate (EVA) and a poly-lactic acid-based biodegradable polymer. In Trial 2, the efficacy of these selected polymers to reduce MP levels in red wine was tested as a function of contact time. Solid-phase micro-extraction multi-dimensional GC-MS was used to measure MP levels before and after treatment with the polymers. Results showed significant reductions in all target odorants after 24 h treatment: silicone reduced IPMP and IBMP by 96% and 100%, respectively, while the biodegradable polymer decreased IPMP and IBMP concentrations by 52% and 36%, respectively. EVA was less effective in lowering MP levels (7% IPMP and 23% IBMP after 24 h). Taken overall, the data suggest the potential for the use of poly-lactic acid and silicone in treating wines contaminated by ladybug taint, as well as in reducing high levels of grape-derived MPs.
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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) | 0.000 | 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".