Novel Applications for Biomaterials: The Case of Remediation of Wine Taints Using Poly-Lactic Acid Polymer
Bibliographic record
Abstract
Wine is the world’s oldest alcoholic beverage and the most consumed in at least 20 countries. However, taints detract from its quality and acceptance, significantly reducing the value of wine. In this study we investigated the capacity of a poly-lactic acid (PLA) biopolymer to reduce concentrations of four odorants responsible for tainting many red wines:isopropyl-methoxypyrazine (IPMP),isobutyl–methoxypyrazine (IBMP), 4-ethylphenol (4-EP) and 4-ethylguaiacol (4-EG). Red wine was spiked with either IPMP [20 ng/L] and IBMP [20 ng/L] (Study 1) or 4-EP [200 ug/L] and 4-EG [200 ug/L] (Study 2) and then treated with a PLA film [surface area 350 cm2/L]. Solid Phase Micro-Extraction Multi-Dimensional Gas Chromatography–Mass Spectrometry and Gas Chromatography-Mass Spectrometry were used to measure the methoxypyrazines and ethylphenols, respectively, both before and after treatment with PLA. Results showed significant reductions in all of the target odorants after 8hrs treatment: IPMP (51%), IBMP (26%), 4-EP (21%) and 4-EG (20%). Taken overall, the data suggest potential for the use of PLA in treating common wine faults, particularly ‘ladybug taint’, which is caused by elevated levels of IPMP.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".