Chemical and Sensory Evaluation of Silicone and Polylactic Acid-Based Remedial Treatments for Elevated Methoxypyrazine Levels in Wine
Bibliographic record
Abstract
Alkylmethoxypyrazines (MPs) are a class of compounds that can elicit undesirable aroma and flavor characteristics in wine, and resist remediation using traditional wine making approaches. MPs are grape-derived constituents as well as contaminants from Coccinellidae beetles present during wine processing; the latter eliciting an off-flavor referred to as 'ladybug taint'. In this study we investigated the capacity of two plastic polymers-one silicone-based, the other polylactic acid-based-applied with varying surface areas to reduce concentrations of isopropylmethoxypyrazine (IPMP), sec-butylmethoxypyrazine (SBMP) and isobutylmethoxypyrazine (IBMP) in a Merlot wine using multi-dimensional gas chromatography coupled with mass spectrometry and headspace solid phase microextraction (SPME-MDGCMS). The impact of treatments on the sensory characteristics of the wine (descriptive analysis) and volatile aroma compounds (VOCs) (SPME-MDGCMS) was also investigated. Results showed substantial reductions for all of the target odorants: up to 38%, 44% and 39% for IPMP, SBMP and IBMP, respectively, for the silicone polymer, and up to 75%, 78% and 77% for IPMP, SBMP and IBMP, respectively, for the polylactic acid polymer. These polymers had no or minimal effect on VOCs at applications of 200 cm²/L for silicone or for all polylactic acid treatments. Sensory impacts were less clear, but generally showed minimal effect from the treatments. Taken overall, the data confirm the utility of both polylactic acid and silicone polymers in reducing elevated levels of grape-derived MPs, as well as potentially improving wine contaminated by ladybug taint.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".