Preparation, analysis and toxicity characterisation of the redox metabolites of the azo food dye tartrazine
Bibliographic record
Abstract
Tartrazine (E102, FD&C Yellow 5) is a vibrant yellow azo dye added to many processed foods. The safety of this ubiquitous chemical has not been fully elucidated, and it has been linked to allergic reactions and ADHD in some individuals. In our study, bacterial species isolated from human stool decolourised tartrazine and, upon exposure to air, a purple compound formed. Tartrazine is known to undergo reduction in the gut to sulfanilic acid and 4-amino-3-carboxy-5-hydroxy-1-(4-sulfophenyl)pyrazole (SCAP). These metabolites and their derivatives are relevant to the toxicology of tartrazine. The toxicity of sulfanilic acid has been studied before, but the oxidative instability of SCAP has previously prevented full characterisation. We have verified the chemical identity of SCAP and confirmed that the purple-coloured oxidation derivative is 4-(3-carboxy-5-hydroxy-1-(4-sulfophenyl)-1H-pyrazol-4-yl)imino-5-oxo-1-(4-sulfophenyl)-4,5-dihydro-1H-pyrazole-3-carboxylic acid (purpurazoic acid, PPA), as proposed by Westöö in 1965. A yellow derivative of SCAP is proposed to be the hydrolysed oxidation product, 4,5-dioxo-1-(4-sulfophenyl)-4,5-dihydro-1H-pyrazole-3-carboxylic acid. Both SCAP and PPA are moderately toxic to human cells (IC50 89 and 78 μM against HEK-293, respectively), but had no apparent effect on Escherichia coli and Bacillus subtilis bacteria. These results prompt further analyses of the toxicology of tartrazine and its derivatives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".