Specialty seeds: Nutrients, bioactives, bioavailability, and health benefits: A comprehensive review
Bibliographic record
Abstract
Seeds play important roles in human nutrition and health since ancient time. The term "specialty" has recently been applied to seeds to describe high-value and/or uncommon food products. Since then, numerous studies have been conducted to identify various classes of bioactive compounds, including polyphenols in specialty seeds. This review discusses nutrients, fat-soluble bioactives, polyphenols/bioactives, antioxidant activity, bioavailability, health benefits, and safety/toxicology of commonly consumed eight specialty seeds, namely, black cumin, chia, hemp, flax, perilla, pumpkin, quinoa, and sesame. Scientific results from the existing literature published over the last decade have been compiled and discussed. These specialty seeds, having numerous fat-soluble bioactives and polyphenols, together with their corresponding antioxidant activities, have increasingly been consumed. Hence, these specialty seeds can be considered as a valuable source of dietary supplements and functional foods due to their health-promoting bioactive components, polyphenols, and corresponding antioxidant activities. The phytochemicals from these specialty seeds demonstrate bioavailability in humans with promising health benefits. Additional long-term and well-design human intervention trials are required to ascertain the health-promoting properties of these specialty seeds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".