Health-Promoting Compounds in Pigmented Thai and Wild Rice
Bibliographic record
Abstract
Organic pigmented Thai rice and wild rice are commonly available in specialized Italian markets and food shops, and they are gaining popularity among consumers demanding healthy foods. Indeed, the typical colour of kernels, which is the unique characteristic of pigmented cereals, is due to the accumulation of pigments that are also responsible for a number of healthy effects. The aim of this study was to provide a portrait of two organic pigmented Thai rice varieties from Thailand and one wild rice variety from Canada, imported into Italy and at Italian consumers' disposal. To this end, the proximate composition and the content of health-promoting compounds, such as carotenoids, anthocyanins and phenolic compounds, were determined in Thai and wild rice. Moreover, the effect of cooking on phytochemicals was assessed, in order to provide reliable data on the dietary intake of bioactive compounds by samples under investigation. Results show that studied samples have a content of phytochemicals higher than white rice and comparable to other cereals. The cooking process determined a decrease of bioactive compounds in all varieties under investigation. However, some samples were found more resistant to cooking stress, and some phytochemicals were little affected by this process. Therefore, pigmented Thai and wild rice may represent a valuable source of healthy compounds and an alternative to other wholesome foods required by consumers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".