Influence of food matrix and food processing on the chemical interaction and bioaccessibility of dietary phytochemicals: A review
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
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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