<i>In Situ</i> Determination of Fructose Isomer Concentrations in Wine Using <sup>13</sup>C Quantitative Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
A practical method for simultaneously quantifying fructose and ethanol contents in wines using (13)C quantitative nuclear magnetic resonance (qNMR) spectroscopy is reported. Less than 0.6 mL of wine is needed, and the method leaves an unmodified sample available for subsequent testing or additional analyses. The relative ratios of the five known fructose isomers in ethanolic solutions at different pH and their variations with the temperature are also reported. The data are correlated with the sweetness of wines. The technique was applied to commercially available wines, and the results are compared to other methods. Sugar levels above 0.6 g/L can also be measured. A simple adaptation of the method permits measurement of different carbohydrates using integration of single peaks for each compound, in combination with an external reference (13)C qNMR spectrum of a sample with a known concentration. The method can be applied at all stages of wine production, including grape must, during fermentation, and before and after bottling.
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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".