Preservative Additives in Food Products Sold in Dakar Markets: Frequency and Diversity
Bibliographic record
Abstract
The use of food additives in industrial production has the advantage of improving sensory properties, technological quality and extending the shelf life of foods.Among the most widely used additives are preservatives, which were added to food products to inhibit, slow down or destroy various types of microorganisms.However, the strong presence of these additives on the market is not without risks for human health, and should be controlled to guarantee food safety.Analysis of the risks associated with consumption of foods containing these preservatives requires, among other things, information on the frequency of use of these additives in various consumer products.The aim of this study is therefore to identify the preservatives present in industrial food products distributed in Dakar.The methodology adopted consists of a qualitative analysis based on the identification of additives from food labels.Investigations were carried out in 9 stores, 4 superettes and 2 supermarkets located in different districts of Dakar.The results revealed the presence of 10 preservative dominated by potassium sorbate (25%) and sodium benzoate (24%).These preservatives are of natural or industrial origin, and are most often used in combination in industrial products.For some identified preservatives such as sodium nitrite and potassium metabisulfite, health risks are associated with their consumption.It has also been noted that 2 to 6 preservative additives can be combined in the same food product to reinforce antimicrobial effects.This work shows the need for regular sanitary quality control of food products distributed in markets.The results of this study open up prospects for the development of information databases on food additives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".