Usage Status and Comparison Analysis of the Food Colour in Some Countries (Regions)
Bibliographic record
Abstract
Food colour was divided into three categories,that was tar pigments,natural pigments and others. Edible tar pigment was classified to azo,triarylmethane,xanthene,fluorescent ketone,quinoline derivatives and indigo dye,and natural pigment was classified to tetrapyrroles ( porphyrins) derivatives,isoprene derivatives,anthocyanin derivatives, ketone derivatives,quinone derivatives and the others according to their chemical structure. There are INS,E-number, C. I. and the code about synthetic pigment in some countries (regions) in food color. The regulations and the use varieties about food colour of China,CAC,Russia,EU,US,Canada,Japan,HongKong China,Macao China and Taiwan China were introduced respectively. Comparison analysis about differences in formulation of food additicve standard,prohibition about colour variety,attitude about usage of edible tar colour,and colour variety (tar colour,natural colour and others), scope and limits about colour usage were performed with comparative analysis. Colour usage in the produce of export food in accordance with standard of destination,focus on variety and limit of colour in import food according to their sources was proposed,and trends about usage of food colour was Prospected also.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".