Phenolic composition and bioactivities of sea buckthorn (<i>Hippophae rhamnoides</i> L.) fruit and seeds: an unconventional source of natural antioxidants in North America
Bibliographic record
Abstract
Abstract BACKGROUND Sea buckthorn ( Hippophae rhamnoides L.) was introduced into Canada in the early 2000s. This plant bears fruits with high commercial value in other countries due to its premium oil. Nevertheless, sea buckthorn berries are also a rich source of bioactives with nutraceutical potential, especially the variety grown in Newfoundland (Canada), which has not previously been characterized. As such, this study evaluated the composition of polyphenols in sea buckthorn pomace and seeds, as well as their prospective health‐promoting effects. RESULTS Polyphenolic identification by high‐performance liquid chromatography–ultraviolet–mass spectrometry–time of flight revealed the presence of 24 compounds in the seeds and 16 compounds in the pomace, including phenolic acids, flavonoids, and tannins, with ellagic acid derivative IV (pomace, 52.13 μg g −1 ) and (+)‐catechin (seeds, 690.8 μg g −1 ) being the most dominant. Sea buckthorn extracts displayed in vitro antidiabetic and anti‐obesity potential by inhibiting α‐glucosidase (71.52–99.31%) and pancreatic lipase (15.80–35.61%) enzymes, respectively. The extracts also protected low‐density‐lipoprotein cholesterol (50.97–89.67%) and supercoiled DNA (35.11–79.84%) from oxidative damage. CONCLUSION Sea buckthorn berries grown in Canada showed promising health benefits induced by their rich and diverse polyphenolic profile and need to be considered for further commercial expansion as a bioactive‐loaded superfruit. © 2024 Society of Chemical Industry.
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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".