Occurrence, types, properties and interactions of phenolic compounds with other food constituents in oil-bearing plants
Bibliographic record
Abstract
Phenolic phytochemicals have become of interest due to their therapeutic potential, particularly with regards to their anti-cancer, anti-inflammatory, hypolipidemic, and hypoglycemic properties. An evolving area of research involving phenolics in foods and their products pertains to the functional, biological, and nutritional consequences resulting from the binding between certain phenolic compounds and the macronutrient and micronutrient constituents of foods. The goal of this review is to provide a summary of studies investigating endogenous phenolic interactions with major components in food systems, including carbohydrates, proteins, lipids, minerals and vitamins, with a focus on the phenolic compounds and nutrients in oil-bearing plants. Another major objective is to provide a comprehensive overview of the chemical nature of phenolic interactions with food constituents that could affect the quality, nutritional and functional properties of foods. Such information can assist in the discovery and optimization of specific phenolic complexes in plant-based foods that could be utilized towards various applications in the food, nutraceutical and pharmaceutical industries.
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 | 0.000 | 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 | 0.000 |
| 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".