Robust, automated quantitation of proline in wine
Bibliographic record
Abstract
Proline is an abundant wine metabolite shown to impart sweetness, viscosity and increase flavour intensity. While several techniques are available to quantitate proline in wine, they are either costly, laborious, or prone to severe interferences. Proton nuclear magnetic resonance ( 1 H NMR) analysis of wine has provided a method for rapidly measuring several important wine metabolites, including proline. In this paper, we present a method for the automated quantitative measurement of proline by 1 H NMR. In the thousand wine samples analysed, the majority (84.9 %) were processed automatically, with the remainder requiring visual assessment by the analyst using a parallel workflow. Repeatability of the NMR method was shown to be excellent in achieving intraday and interday coefficients of variation of less than 1.2 %. Additionally, the approach significantly reduced analyst time requirements and increased reliability and repeatability across a wide range of concentrations and wine styles. The workflow is freely available at https://github.com/AWRIMetabolomics/nmr-pro-quant .
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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".