Phenol-Explorer: an online comprehensive database on polyphenol contents in foods
Bibliographic record
Abstract
A number of databases on the plant metabolome describe the chemistry and biosynthesis of plant chemicals. However, no such database is specifically focused on foods and more precisely on polyphenols, one of the major classes of phytochemicals. As antioxidants, polyphenols influence human health and may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, some cancers or type 2 diabetes. To determine polyphenol intake in populations and study their association with health, it is essential to have detailed information on their content in foods. However this information is not easily collected due to the variety of their chemical structures and the variability of their content in a given food. Phenol-Explorer is the first comprehensive web-based database on polyphenol content in foods. It contains more than 37,000 original data points collected from 638 scientific articles published in peer-reviewed journals. The quality of these data has been evaluated before they were aggregated to produce final representative mean content values for 502 polyphenols in 452 foods. The web interface allows making various queries on the aggregated data to identify foods containing a given polyphenol or polyphenols present in a given food. For each mean content value, it is possible to trace all original content values and their literature sources. Phenol-Explorer is a major step forward in the development of databases on food constituents and the food metabolome. It should help researchers to better understand the role of phytochemicals in the technical and nutritional quality of food, and food manufacturers to develop tailor-made healthy foods. Database URL: http://www.phenol-explorer.eu.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".