Identification and Quantitation of Water-Soluble Synthetic Colors in Foods by Liquid Chromatography/Ultraviolet–Visible Method Development and Validation
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 simple and sensitive liquid chromatography method of analysis has been developed and validated for the simultaneous quantitative determination of food colors in a broad range of foods. The method of analysis applies specific extraction solutions to obtain optimal color extraction. The extraction solutions are composed of different proportions of methanol and ammonium acetate, as the ion-pairing agent. Analysis was performed on reverse-phase C18 Poroshell column with ammonium acetate, methanol, acetonitrile, and acetone gradient elution as the mobile phases. Multiple-specific wavelengths were used to monitor color additives in the visible range to provide higher sensitivity and expanded scope needed for a large number of analytes. All 27 color compounds showed good linearity with regression coefficients predominantly above 0.990. The limit of detection and limit of quantitation values ranged from 0.10 to 0.43 and from 0.34 to 1.45 μg/g, respectively. The precision of the method ranged from 1.4 to 15.9%, while recoveries averaged 72% across all food commodities tested. The method of analysis offers convenience and adequate sensitivity for the analysis of a wide variety of food matrices containing a broad range of colors.
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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 it