Identification and quantitative measurement of pyroglutamic acid in 1H NMR spectra of wine
Bibliographic record
Abstract
Proton NMR is one of the key analytical technologies in the field of metabolomics, as it allows one to combine untargeted, targeted, and quantitative metabolite measurements. One of NMR's greatest strengths is the ability to unambiguously identify compounds when present at mg/L concentrations, without the use of expensive or hard-to-source reference compounds. Furthermore, identification can be performed non-destructively on complex samples without the need for further sample preparation and isolation. Here, we describe a series of NMR experiments and data processing techniques to unambiguously identify the metabolite pyroglutamic acid (pGlu) in wine samples, without prior enrichment or separation from matrix compounds and other metabolites typically present in wine. Subsequently, the concentration of pGlu in 100 Australian wines was determined using standard NMR protocols. Statistical analysis demonstrated that occurrence of pGlu is associated with glutamic acid, is linked to vintage conditions and accumulated heat over the growing season, and is negatively associated with rainfall during the growing season. Overall, the results establish the presence and typical concentrations of the amino acid metabolite pGlu in Australian wine. • 2D NMR experiments provided essential structural information with advantages over selective acquisition techniques. • Pyroglutamic acid was quantified in 100 Australian wines, the largest survey of this wine metabolite to date. • Significant drivers of pyroglutamic acid concentration in wine were determined through statistical analysis.
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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 | 0.001 | 0.001 |
| 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".