Quantitation of low molecular weight sugars by chemical derivatization‐liquid chromatography/multiple reaction monitoring/mass spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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 it