Factors affecting the antioxidant potential and health benefits of plant foods
Bibliographic record
Abstract
Li, H., Tsao, R. and Deng, Z. 2012. Factors affecting the antioxidant potential and health benefits of plant foods. Can. J. Plant Sci. 92: 1101–1111. Phytochemicals, such as carotenoids, phenolics and glucosinolates, in plants and plant-based foods are able to reduce the incidence of non-communicable diseases such as cardiovascular diseases, diabetes, stroke and different types of cancer due to their antioxidant potential. A brief summary of the in vitro and in vivo evidence and how post-harvest storage and processing conditions can affect the health benefits is presented in this review. Genetic, environmental conditions and physiological factors may modify the composition and amounts of these compounds present in plants, thus affecting the in vitro antioxidant activities. Such changes in combination with degradations after digestion can significantly affect the bioaccessibility and bioavailability of phytochemicals. The main objective of this review is to provide updated information on the effects of various factors such as genetics, environment, post-harvest storage and processing on the bioactive components, and knowledge of these factors can lead to multidisciplinary strategies to maximise the bioavailability and health potential of foods.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".