Process-induced toxicants in food: an overview on structures, formation pathways, sensory properties, safety and health implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it