The Castanea sativa bur as a new potential ingredient for nutraceutical and cosmetic outcomes: preliminary studies
Bibliographic record
Abstract
Chestnuts are a common food product in Mediterranean countries, being recognized also for their beneficial effects on human health. Nevertheless, during processing, these fruits generate a large amount of food by-products, such as shells and burs. In the present work, the macronutrient composition, vitamin E profile and amino acid content of the burs were determined in samples from three different Portuguese regions (Minho, Trás-os-Montes and Beira-Alta). The nutritional composition was similar for all samples, being characterised by a high moisture content and low fat amounts. All essential amino acids were present in considerable amounts. Concerning vitamin E, the predominant vitamer was α-tocopherol for the Minho and Beira-Alta samples. The total phenolic compounds were quantified, and the antioxidant activity evaluated in different extracts using two biochemical assays (DPPH˙ and FRAP). All bur extracts showed a high total phenolic content, the highest obtained being that for the Beira-Alta samples. The chestnut bur from Minho showed the highest antioxidant activity in both assays. This study aims to demonstrate the potential of the Castanea sativa bur as a cosmetic and nutraceutical ingredient.
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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 | 0.000 | 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) | 0.002 | 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".