Enzymatic Hydrolysis of Flaxseed to Produce Alpha-Linoleic and Linolenic Acid
Bibliographic record
Abstract
Microbial lipases have numerous potential applications in the bioprocessing industry due to their variety and versatility. Canada, in particular Saskatchewan is one of the worldwide leaders in the production of flaxseed crops accounting for 40% of crop production. Flaxseed oil is a viable source of plant based α-linoleic and linolenic acids (omega-6 and omega-3). Flaxseed oil contains high amounts of linolenic acid and moderate amounts of linoleic acid. The concentration of these essential free fatty acids in flaxseed oil can be increased via the process of enzymatic hydrolysis. Alpha-linoleic and linolenic acid are high value nutritional supplements in great demand to the pharmaceutical and health industries. Humans cannot synthesize these fatty acids within the body and must consume them in their diets or in the form of supplements thus, increasing the desired free fatty acid content within flaxseed oil is a viable solution to this need. The focus of the present work is to produce α- linoleic and linolenic free fatty acids catalyzed by microbial lipases. In addition, the optimization of the enzymatic hydrolysis reaction conditions and use of flax seed oil as feedstock and growth medium will be studied in the present work. Initial experiments showed an increase of 78 wt. % of free fatty acid yield following optimized hydrolysis using lipase from Aspergillus niger and 96% wt. % increase using lipase from Candia rugosa.
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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 | 0.001 |
| 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 | 0.001 |
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".