Profiling the phenolic acids, flavonoids and tannins in skunk currants (Ribes glandulosum) of Northern Québec, Canada
Bibliographic record
Abstract
BACKGROUND: Skunk currant is widely dispersed across North America and a feature of some traditional North American indigenous diets. Whereas many wild and cultivated berries have attracting interest related to their antioxidant phenolic metabolites and putative health benefits in humans, very few data are available concerning skunk currant phytochemistry. OBJECTIVE: Provide the first metabolic profile of skunk currant fruits with a focus on phenolic and polyphenolic compounds, owing to their emerging implications in human health. METHODS: Skunk currants were harvested in Nunavik, Québec. Flavonols, flavan-3-ols, and phenolic acids were characterized using a targeted approach with reverse-phase ultra-high pressure liquid chromatography coupled with tandem mass spectrometry. Ellagitannins and anthocyanins were measured using reverse-phase HPLC following acid hydrolysis and employing diode array detection. Proanthocyanidins and sugars were detected with normal-phase HPLC. RESULTS: A total of 11 phenolic acids and 11 flavonoids, including three cyanidins and three quercetin glycosides were identified. Both condensed (proanthocyanidins) and hydrolysable (ellagitannins) tannins were also detected at 162 mg and 75 mg per 100 g extract, respectively. The cumulative amount of detected phenolic and polyphenolic metabolites totaled 622.6 mg/100 g extract (63.4 mg/100 g berry FW). CONCLUSIONS: Skunk currant is a source of many bioactive phenolic and polyphenolic compounds. Appearing richer in phenolics than some cultivated varieties, the wild northern varieties of North America warrant additional study.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".