Identification of Natural and Artificial Colorants in Industrial Products Marketed in Senegal
Bibliographic record
Abstract
Food colorants are widely used in the food industry to maintain or enhance product color. However, as the use of these colorants can have negative impacts on health, it is essential to analyze the risks associated with their consumption. This analysis requires, among other things, obtaining sufficient data on the presence of these colorants in foods, as well as their level of consumption. However, data on these colorants is often virtually non-existent in developing countries. The aim of this study was to determine the colorant profile of industrial products marketed in Senegal. Information on food additives was collected on 399 labels of different food product categories in shops located in Dakar. Data is recorded and processed using Excel software. Based on the Codex classification, analysis of the profile of additives identified on the labels of food samples revealed the presence of 31 colorants. The natural colorants identified are dominated by beta-carotene, widely present in beverages and dairy products, and paprika extract identified on cookies and industrial sauces. Artificial colors are dominated caramels present in several foods including bouillons, vinegars, sauces and hard candies. Secondly, there was a strong presence of the azo dye Sunset yellow FCF, widely found in samples of beverages, confectionery and cookies. The results of this case study enable us to appreciate the wide presence and diversity of colorants on the Senegalese market, and the importance of controlling them to guarantee consumer safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".