Quantitation of low molecular weight sugars by chemical derivatization‐liquid chromatography/multiple reaction monitoring/mass spectrometry
Bibliographic record
Abstract
A new method for the separation and quantitation of 13 mono- and disaccharides has been developed by chemical derivatization/ultra-HPLC/negative-ion ESI-multiple-reaction monitoring MS. 3-Nitrophenylhydrazine (at 50°C for 60 min) was shown to be able to quantitatively derivatize low-molecular weight (LMW) reducing sugars. The nonreducing sugar, sucrose, was not derivatized. A pentafluorophenyl-bonded phase column was used for the chromatographic separation of the derivatized sugars. This method exhibits femtomole-level sensitivity, high precision (CVs of ≤ 4.6%) and high accuracy for the quantitation of LMW sugars in wine. Excellent linearity (R(2) ≥ 0.9993) and linear ranges of ∼500-fold for disaccharides and ∼1000-4000-fold for monosaccharides were achieved. With internal calibration ((13) C-labeled internal standards), recoveries were between 93.6% ± 1.6% (xylose) and 104.8% ± 5.2% (glucose). With external calibration, recoveries ranged from 82.5% ± 0.8% (ribulose) to 105.2% ± 2.1% (xylulose). Quantitation of sugars in two red wines and two white wines was performed using this method; quantitation of the central carbon metabolism-related carboxylic acids and tartaric acid was carried out using a previously established derivatization procedure with 3-nitrophenylhydrazine as well. The results showed that these two classes of compounds-both of which have important organoleptic properties-had different compositions in red and white wines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".