Frequency and Diversity of Stabilizers, Thickeners and Gelling Agents Used as Food Additives in Food Products Sold on Dakar Markets
Bibliographic record
Abstract
The industrial use of food additives is growing rapidly worldwide.These additives include stabilizers, thickeners and gelling agents.These substances help to improve texture and protect against food modification.The result is food products with improved sensory quality, acceptable to consumers and with increased profits for companies.However, the use of these substances must comply with standards to guarantee food safety.These standards are regularly revised to take account of any new safety data.This implies the need to obtain information on the presence and level of use of these additives in foodstuffs sold in distribution chains.This study therefore set out to identify the profile and frequency of stabilizers, gelling agents and thickeners in various food categories sold on Dakar markets.The methodology adopted is based on a collection of labels from food samples sold in various trading venues.Food additives as well as the functions indicated on the labels are listed, recorded and classified based on Codex Alimentarius standards.The results of this study showed the predominance of stabilizers (59%), made up largely of plant hydrocolloids, particularly guar gum and cellulose gum.Of the 4 substances used as thickeners, most were xanthan gum and acetylated diamidon adipate.As for additives indicated as gelling agents, the presence of pectin and gelatin was noted.Generally speaking, most of the additives encountered are of natural origin and can be extracted from local plant resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".