Effect of Extraction Method on the Phenolic and Cyanogenic Glucoside Profile of Flaxseed Extracts and their Antioxidant Capacity
Bibliographic record
Abstract
Abstract The application of flaxseed extracts as food ingredients is a subject of interest to food technologists and nutritionists. Therefore, the influence of the extraction method on the content and composition of beneficial compounds as well as anti‐nutrients is important. In the study, the effects of two solvent extraction methods, aqueous and 60 % ethanolic, on phenolic and cyanogenic glucoside profiles of flaxseed extract were determined and compared. The impact of extracted phenolic compounds on the antioxidant capacity of the extracts was also investigated. Defatted meals from brown and golden flax varieties were used as extraction material. The ethanolic extraction was more selective for phenolics (100.8–131.7 mg g−1) than the aqueous one (11.5–15.7 mg g−1). However, the contribution of particular phenolic compounds to total phenolics was much more dependent on flax variety than extraction method. A strong relationship was observed between both radical scavenging and ferric reducing activity and the content of phenolics (particularly secoisolariciresinol diglucoside). The correlation between extract chelating ability and phenolics was moderate suggesting that other flaxseed compounds are involved in this activity. The extraction method strongly affected cyanogenic glucoside content of flaxseed extracts; the aqueous extraction caused 96 % reduction in cyanogenic glucoside content (0.56–0.62 mmol g−1) when compared to the content in defatted meal (9.1–11.6 mmol g−1). On the contrary, ethanolic extraction resulted in the high cyanogenic glucoside content in the extracts (71–89 mmol g−1). The results reveals that ethanolic extraction gives extracts rich in antioxidant lignans; aqueous extracts have lower antioxidant activity than ethanolic but cyanogenic glucosides are significantly reduced.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".