Influence of food matrix and food processing on the chemical interaction and bioaccessibility of dietary phytochemicals: A review
Bibliographic record
Abstract
Consumption of phytochemicals-rich foods shows the health effect on some chronic diseases. However, the bioaccessibility of these phytochemicals is extremely low, and they are often consumed in the diet along with the food matrix. The food matrix can be described as a complex assembly of various physical and chemical interactions that take place between the compounds present in the food. Some studies indicated that the physiological response and the health benefits of phytochemicals are resultant in these interactions. Some food substrates inhibit the absorption of phytochemicals via this interaction. Moreover, processing technologies have been developed to facilitate the release and/or to increase the accessibility of phytochemicals in plants or breakdown of the food matrix. Food processing processes may disrupt the activity of phytochemicals or reduce bioaccessibility. Enhancement of functional and sensorial attributes of phytochemicals in the daily diet may be achieved by modifying the food matrix and food processing in appropriate ways. Therefore, this review concisely elaborated on the mechanism and the influence of food matrix in different parts of the digestive tract in the human body, the chemical interaction between phytochemicals and other compounds in a food matrix, and the various food processing technologies on the bioaccessibility and chemical interaction of dietary phytochemicals. Moreover, the enhancing of phytochemical bioaccessibility through food matrix design and the positive/negative of food processing for dietary phytochemicals was also discussed in this study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".