Structural characterization of phenolic compounds in canola meal: impact of high pressure and temperature
Bibliographic record
Abstract
Canola is the major oilseed crop of Canada. After extraction of the oil, a substantial amount of meal is generated. To date, however, the meal has received little attention from both industry and researchers. Despite its nutrition and functional potential, utilization of canola meal has been limited by its high fiber content and antinutritional components. Nevertheless, it has a rich phenolic profile, that could be a valuable source of nutraceuticals. The presence of large amounts of sinapine and sinapic acid in canola meal and its conversion to canolol has received a lot of attention because of its powerful antioxidant and anticancer properties. What is desired, however, is an effective method for their extraction. The use of pressurized (1,500 psi) high-temperature processing is ideal for the extraction of phenolic compounds from such by-products including the meal. This study investigated the application of accelerated solvent extraction (ASE) as a method for extracting phenolic compounds from canola meal. Different extraction temperatures, solvents, and solvent concentrations were used to establish the optimum extraction conditions for major sinapates and other phenolic compounds. Changes in total phenolics, total flavonoids, and antioxidant capacity were quantified as a measure of extraction efficiency. The ASE method is an efficient method for extracting phenolic compounds from the meal. In addition to ASE, RapidOxy®100, an automated commercial instrument, was adapted as a solvent-free pre-treatment of canola meal prior to the ultrasonic extraction of phenolic compounds. It also proved to be a novel and versatile method for enhancing the extraction of phenolic compounds. Both techniques have the potential for the mainstream production of phenolic-rich extracts as a valuable source of natural bioactive compounds and nutraceuticals from underutilized agricultural by‐products such as canola meal.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".