Bioactive Phenolic and Fatty Acid Composition of Canadian Wild Gooseberry and Blackcurrant
Bibliographic record
Abstract
Wild berry varieties are widely distributed in Canada and mainly used by the indigenous population as food. Gooseberries and blackcurrant are the two tart berries with strong taste that are used to make preserves such as jams, jellies, and wine. Phenolic compounds and fatty acids are the predominant bioactive metabolites present in these berries. Recently, researchers are more interested in exploring the fruits such as berries for functional properties that benefit human health. Although wild berries are known for their health benefits, research evidence related to profiling of bioactive molecules in these wild berries such as Canadian gooseberry and blackcurrant is limited. In this study we assessed and compared the bioactive phenolic and fatty acid composition in these two berries using liquid and gas chromatography and mass spectrometry. The cyanidine glucoside, which is a polyphenolic anthocyanin present in blackcurrant is significantly higher (832 μg/g Dry wt.) compared to that in Canadian gooseberry (150 μg/g Dry wt.). Linolenic acid (20.3 %) and linoleic acid (18.2 %) were the most abundant fatty acids in blackcurrant while linoleic acid (15.0 %) and palmitic acid (11.2 %) were abundant in gooseberry. Other fatty acids such as oleic acid, linoleic, and alpha linolenic acid, which are important in cardiovascular health, were present in both blackcurrant and gooseberry.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".