Effects of bioactive molecules on the concentration of biogenic amines in foods and biological systems
Bibliographic record
Abstract
Biogenic amines (BAs) are a group of molecules naturally present in foods that contain amino acids, peptides, and proteins as well as in biological systems. In foods, their concentrations typically increase during processing and storage because of exposure to microorganisms that catalyze their formation by releasing amino acid decarboxylases. The concentrations of BAs above certain values are indicative of unsafe foods due to associate neuronal toxicity, allergenic reactions, and increase risks of cardiovascular diseases. There are therefore various strategies that focus on the control of BAs in foods mostly through elimination, inactivation, or inhibition of the growth of microorganisms. Increasingly, there are works on bioactive compounds that can decrease the concentration of BAs through their antimicrobial activity as well as the inhibition of decarboxylating enzymes that control their formation in foods or amine oxidases and N -acetyltransferase that control the degradation in vivo. This review focusses on the role of food-derived bioactive compounds and the mechanism by which they regulate the concentration of BAs. The findings are that most active molecules belong to polyphenols, one of the largest groups of plant secondary metabolites, additionally other useful +compounds are present in extracts of different herbs and spices. Different mechanisms have been proposed for the effects of polyphenols depending on the model system. Studies on the effects in vivo are limited and there is a lack of bioavailability and transport data which are important to assess the importance of the bioactive molecules.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".