Application of nuclear magnetic resonance spectroscopy in food adulteration determination: the example of Sudan dye I in paprika powder
Bibliographic record
Abstract
Abstract Carcinogenic Sudan I has been added illegally into spices for an apparent freshness. 1H solution and solid-state (SS) nuclear magnetic resonance (NMR) spectroscopies were applied and compared for determination of Sudan I in paprika powders (PPs). For solution NMR, PPs spiked with Sudan I were extracted with acetonitrile, centrifuged, rotor-evaporated, and re-dissolved in DMSO-d6 for spectral collection. For SSNMR, Sudan I contaminated PPs were mixed with DMSO-d6 solution and used for spectral collection. Linear regression models constructed for quantitative analyses resulted in the average accuracies for unknown samples as 98% and 105%, respectively. Limits of detection for the solution NMR and SSNMR spectrometers were 6.7 and 128.6 mg kg−1, while the limits of quantification were 22.5 and 313.7 mg kg−1. The overall analysis time required by both methods was similar (35 and 32 min). Both NMR techniques are feasible for rapid and accurate determination of Sudan I adulteration in PPs.
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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 | 0.000 | 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 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".