Antioxidants: Science, Technology, and Applications
Bibliographic record
Abstract
Abstract Antioxidants are compounds that are used to enhance shelf life and preserve the quality of fats and oils and lipid‐containing foods by suppressing oxidation reactions of their unsaturated components. These compounds could be naturally present, deliberately added, or generated during processing. Antioxidants are needed in small quantities to participate or interfere in the lipid autoxidation reaction cascade via various mechanisms. They should be easy to handle and use, cost‐effective, and readily available. Antioxidants should not impart any undesirable flavor, odor, or color to the food; they should be stable and safe for use in food. Application of these antioxidants differs depending on the nature of the food, conditions of intended processing, desired shelf life, and storage. However, antioxidants that are deliberately added to foods are thoroughly scrutinized for their safety and toxicology aspects. Their usage in food is regulated under different legislatures in various countries. This article examines the purpose of antioxidant use, chemistry of antioxidant activity, and naturally derived and synthetic compounds that exert antioxidative activity in foods, as well as the relevant technological, toxicological, and regulatory considerations.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".