Biochemical Basis for Functional Ingredient Design from Fruits
Bibliographic record
Abstract
Functional food ingredients (nutraceuticals) in fruits range from small molecular components, such as the secondary plant products, to macromolecular entities, e.g., pectin and cellulose, that provide several health benefits. In fruits, the most visible functional ingredients are the color components anthocyanins and carotenoids. In addition, several other secondary plant products, including terpenes, show health beneficial activities. A common feature of several functional ingredients is their antioxidant function. For example, reactive oxygen species (ROS) can be oxidized and stabilized by flavonoid components, and the flavonoid radical can undergo electron rearrangement stabilizing the flavonoid radical. Compounds that possess an orthodihydroxy or quinone structure can interact with cellular proteins in the Keap1/Nrf2/ARE pathway to activate the gene transcription of antioxidant enzymes. Carotenoids and flavonoids can also exert their action by modulating the signal transduction and gene expression within the cell. Recent results suggest that these activities are primarily responsible for the health benefits associated with the consumption of fruits and vegetables.
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 | 0.001 |
| 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 | 0.001 |
| 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".