Pre-treatment and extraction techniques for improving the recovery of canolol and related phenolics in mustard and canola co-products
Bibliographic record
Abstract
The objectives of the thesis were to study convective heating (air frying) for improving the extraction of phenolics from mustard and canola co-products, and to assess how micro-emulsion (ME) and supercritical CO2 (SC-CO2) processing techniques affect phenolics contents in canola co-products. Mustard and canola seeds were air-fried at temperature-time combinations of 160, 170, 180, or 190 °C for 5, 10, 15 or 20 min. Oil was extracted using the Soxhlet method and the de-oiled meal was air-dried at room temperature. Oil-soluble phenolics were extracted from the oil by hexane/70% methanol mixture, while meal-derived phenolics were isolated using ultrasound-assisted extraction (UAE) with 70% (v/v) methanol. Phenolics were quantified by high-performance liquid chromatography-diode array detection (HPLC-DAD). The antioxidant potential of both the oil and defatted meal extracts was evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and metal ion-chelation (MIC) assays. The extraction of major oil-soluble and meal-derived phenolics in all the mustard and canola co-products showed a temperature-time dependency. The highest canolol content in the oil was obtained after air frying the seeds at 170 and 190 °C for 15 min, for mustard and canola oil, respectively. Oil extracts from both mustard and canola showed improved antioxidant activities (DPPH and FRAP) but poor MIC. On the other hand, canola meal extracts showed better MIC. Canola press cake (CPC) from two different sources was treated with different SC-CO2 and ME protocols to remove varying levels of oil. The partially de-oiled meals were extracted using UAE-70% (v/v) methanol. The phenolic-rich extracts were analyzed and quantified by HPLC-DAD. The results were compared with phenolic extracts from canola meal de-oiled using standard industrial hexane extraction (HE). ME was effective in sinapine removal, while SC-CO2 showed better TPC values than HE and ME. Also, ME extracts showed better MIC while SC-CO2 extracts exhibited better DPPH and FRAP values. The outcomes of the thesis will contribute to value-added processing strategies for canola and mustard to generate antioxidants and produce functional canola meal proteins with potential applications in the food and agro-allied industries.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".