Bioactive Compounds of Indigenous Canadian Small Fruits: UHPLC-HRMS-Based Phytochemical Characterization, Mineral Composition and Their Antioxidant Activity
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Canadian prairie small fruits have attracted great interest for their potential health benefits, including cardiovascular and anticancer effects. This study examined 14 species of prairie small fruits using a targeted ultra high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) metabolomics approach to quantify 66 phenolic compounds alongside moisture, fat, mineral content, total flavonoid and phenolic content (TPC). Antioxidant capacity was assessed using ferric reducing antioxidant power (FRAP) and radical scavenging assays. Anthocyanins dominated most berries, exceeding 80% of total phenolics in chokeberries and blueberries. Redcurrants and gooseberries contained higher proportions of isoflavones and flavonols, while chokecherries showed notable contributions from procyanidins and hydroxycinnamic acids. Saskatoon berries had the highest TPC (2100 mg/kg) with a balanced flavonoid profile. Principal component analysis revealed Saskatoon berries clustering distinctly from other species, reflecting their unique metabolomic signature. Nannyberries had the strongest FRAP activity, whereas gooseberries and highbush cranberries had the lowest. These findings highlight the phytochemical diversity of prairie berries and their potential for functional food and nutraceutical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".