A theoretical model to predict the amounts of sugar and water in a binary mixture and its experimental validation by time domain nuclear magnetic resonance
Bibliographic record
Abstract
Abstract Time-domain nuclear magnetic resonance (TD-NMR) enables quick, non-invasive, and cost-effective quantification of sugar in a solution. This study proposed a theoretical model to quantify the amounts of sugar and water in a binary sugar solution (a solution where a single type of sugar, either a monosaccharide or disaccharide, is dissolved in water) using TD-NMR. The mathematical model was developed by focusing on the mass balance equation and the acquisition signal (relating to the total hydrogen content) to predict the individual masses of sugar and water. To validate the model, a monosaccharide (fructose) and a disaccharide (saccharose) were used. In order to predict the individual masses of sugar and water, the experimental value of the total hydrogen content in the solution was a prerequisite, which depended on the conversion factor (k). The value of k was obtained using three methods: (a) varying the amount of water, (b) varying the amount of sugar in water, and (c) changing the source of hydrogen (replacing the hydrogen of water with the hydrogen of sugar). The k obtained solely from pure water were not feasible for quantifying sugar due to its high mean absolute relative error (MARE ≈ 30%). The model achieved a MARE of 2%–5% for both sugar solutions when k was sourced from the individual sugar solution. Furthermore, linear regression showed a strong fit (R2 ≥ 0.97) between the predicted and actual masses of sugar and water, indicating the model’s reliability and feasibility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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 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".