Efficacy of green processing techniques on the recovery of phenolic compounds from canola meal
Bibliographic record
Abstract
The current study investigated how supercritical CO 2 (SC-CO 2 ) and micro-emulsion techniques (MT) used as green processing methods for the extraction of oil from canola press cake affected availability of phenolics from canola ( Brassica napus ). Canola press cakes obtained from expeller-pressed canola seeds at 60 ℃ from two different sources were subjected to various SC-CO 2 and MT protocols. The resultant partially defatted meals obtained from SC-CO 2 with various levels of residual oil and from MT had their phenolics extracted using ultrasound-assisted extraction (UAE). The phenolic-rich extracts were analysed and quantified by HPLC-DAD. The antioxidant potential of the extracts was also evaluated using in-vitro antioxidant assays (DPPH radical scavenging, metal ion chelation, and ferric reducing power). Results were compared with extracts from de-oiled canola meal prepared by solvent extraction. The results showed that MT defatted meal resulted in the lowest amount of sinapine (219 ± 5 µg/g DW) (M2) while SC-CO 2 defatted meal showed better total phenolic contents (4.65 ± 0.19 mg GAE/g) compared to MT (M2) (0.30 ± 0.04 mg GAE/g). The results of antioxidant activity indicated that extracts from MT defatted meal exhibited the highest metal chelating capacity (75.5%) while extracts from SC-CO 2 defatted meals generally showed increased potential for scavenging the DPPH free radical and reducing the ferric ion to the stable ferrous ion, both different aspects of lipid peroxidation. These findings provide insights on the use of green technologies that will enhance the utilization of canola meal protein for food applications.
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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".