Effectiveness of different processing methods in reducing hydrogen cyanide content of flaxseed
Bibliographic record
Abstract
Abstract A study was conducted to determine the effectiveness of reducing the hydrogen cyanide (HCN) content of flaxseed (FS) by processing. FS was processed by oven heating, single or repeated pelleting alone or in a mix with corn or other ingredients, autoclaving, and microwave roasting. The comparative effectiveness in reducing HCN in FS by these processes was monitored through HCN measurements by alkaline titration. The HCN content was 377 mg kg−1 in raw feed‐grade FS and 139 mg kg−1 in a human food‐grade FS. All processing methods tested significantly (p < 0.05) reduced the HCN content of FS. Autoclaving FS reduced its HCN content by 29.7%. Microwave roasting of FS reduced the HCN content by 83.3%. Because of the 5.7% water loss recorded after 4 min of FS roasting, this reduction could be related to more evaporation of the newly formed HCN. Pelleting FS once reduced HCN content by 13.3%, and three and six repeated pelleting processes reduced HCN content by 29.0% and 54.9% respectively. When FS was pelleted in a mix with 50% corn, the HCN reduction was even greater. After pelleting six times, HCN reduction reached 63.8%. However, the greatest reduction in HCN content was 73.8%, and was obtained when FS was mixed with several ingredients and pelleted twice. The HCN reduction could be the result of deactivation of the glycosidase, or the evaporation of HCN formed from cyanogenic glycosides. The HCN reduction increased as the number of pelletings and the temperature of the pelleted product increased. The greater and prolonged exposure to a higher temperature by several pelletings seems to promote a greater HCN reduction. The appropriate processing of FS is essential for the use of this oilseed in animal feeding. Copyright © 2003 Society of Chemical Industry
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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) | 0.001 | 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".