Process-induced toxicants in food: an overview on structures, formation pathways, sensory properties, safety and health implications
Bibliographic record
Abstract
Abstract With the rapid advances in ready-to-eat food products and the progress of food processing industries, concerns about food security and investigating food safety as well as sensory quality have intensified. Many food safety concerns are attributed to the toxic components, which can be produced during food processing as process-induced toxicants (PITs). The thermal processing of food (e.g., baking, cooking, grilling, roasting, and toasting) may lead to the formation of some highly hazardous PITs for humans and animals. These include acrolein, acrylamide, benzene, ethyl carbamate, chlorinated compounds, heterocyclic organic compounds (HOCs), polycyclic aromatic hydrocarbons (PAHs), heterocyclic aromatic amines (HAAs), biogenic amines (BAs), N -nitrosamines, Maillard reaction products (MRPs), and several newly identified toxicants such as 3-monochloropropane-1,2-diol. The occurrence of these contaminants is often accompanied by distinguishing odor, taste, and color. The severity of the sensory attributes can vary depending on the compound concentration. Knowledge about the biochemical and chemical mechanisms of PITs generation is necessary for expanding feasible approaches to limit and control their amounts in food products. This contribution introduces the most significant PITs, highlighting their formation mechanisms, impact on sensory characteristics of foods, analytical methods to detection, risk assessments, and food safety/adverse health effects of ultra-processed foods. Graphical Abstract
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".