Characterization of Canadian Black Currant (Ribes nigrum L.) Seed Oils and Residues
Bibliographic record
Abstract
The seeds from five black currant (Ribes nigrum L.) cultivars grown in western Canada were evaluated for their oil content, fatty acid and triacylglycerol (TAG) composition, and tocopherol and phytosterol profiles and contents. Moreover, polyphenolic compounds and antioxidant activity in the seed extracts remaining after oil extraction were determined. Oil contents of black currant seeds ranged from 27 to 33%. The gamma-linolenic acid content varied significantly among the cultivars (from 11% for Ben Conan to 17% for Ben Tirran). Among the 44 TAGs identified, LLalphaLn, alphaLnLgammaLn, and PLgammaLn (where L = linoleoyl, alphaLn = alpha-linolenoyl, gammaLn = gamma-linolenoyl, and P = palmitoyl) were the predominant ones. Black currant seed oil was a good source of tocopherols (1143 mg/100 g of oil on average) and phytosterols (6453 mg/100 g of oil on average). Quercetin-3-glucoside and p-coumaric acid were the main phenolic components in the seed residues. The high concentration of flavonols and phenolic acids was correlated with a high antioxidant activity of seed residue (average ABTS value of 1.5 mM/100 g and DPPH value of 1.2 mM/100 g). The data obtained from this study indicate that Canadian black currant seed oil is a good source of essential fatty acids, tocopherols, and phytosterols. Extraction of phenolic antioxidants from the seed residues even allows the recovery of additional valuable components from the byproduct of fruit processing.
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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 | 0.000 | 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) | 0.000 | 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".