Extraction techniques for the development of protein-enriched extracts from canola meal
Bibliographic record
Abstract
Canola is considered the second most important oil-producing plant. Although canola meal (CM) has an excellent nutrition profile, its application is limited to animal feed industries. With protein content up to 39 % per dry biomass, CM contains a substantial amount of vitamins, minerals, sugars, and digestible fibres. The extraction of nutrients for the development of low-volume, high-value products—such as canola meal extract—offers promising applications across diverse fields. Bypassing processing steps involving protein isolation and removal of undesirable components for food application, its use in non-food applications, such as microbial media supplement, is one of such opportunities. Microorganisms require an adequate supply of nitrogen for optimal growth, which is currently met through synthetic nitrogen sources or conventional extracts derived from plants, animals, or microbes (e.g., yeast extract). However, these conventional nitrogen sources are costly and contribute significantly to the operational expenses of biotechnology industries. Canola meal, an abundant agro-industrial byproduct in Canada with minimal market value, presents an untapped opportunity. With an appropriate extraction technique, it may be possible to develop a novel, value-added product that not only enhances the utility of canola processing but also mitigates the cost burden on microbial production industries. The current study compares pH and temperature-based extraction methods and their combinatorial effect on solvent-extracted CM. Alkaline extractions were conducted at pH ranging from 6 to 12 and temperature from 25 ° C to 160 ° C. A response surface method was used to analyze the effect of pH and temperature on the yield of solid extract and protein content. Effects of further defatting of CM and its treatment with acidic ethanol (30 % 1 M HCl in ethanol) were evaluated. Biocatalysis was performed in solid-state fermentation with Aspergillus oryzae and submerged fermentation with inherent microbes present on canola meal. Central composite design shows that alkaline pH had a greater effect on protein extraction as compared to temperature. Although yields of solid extracts (397 ± 8 mg/ g of CM) and protein (51.8 % of extract) were significantly higher (P < 0.05) during subcritical temperature (160 ° C) at alkaline pH (11.5), amino acid profile and contents were better in bio-catalyzed extracts. The highest protein content in the solid extracts was obtained when A. oryzae pretreated CM was extracted at an alkaline pH. Additional defatting did not make significant changes in the yield, and compared to chemical pretreatment with organic solvents, the microbial method resulted in a higher yield. • The effect of alkaline extraction had higher effect on protein extraction from canola meal (CM) as compared to temperature. • Subcritical extraction (SE) of CM at 160 °C in an alkaline condition resulted in highest extract yield of 396.7 ± 08 mg/g of CM with maximum protein recovery of 51 %. • High temperature during SE had a negative effect on total amino acid composition. • Protein percentage and total amino acids per gram of extract was 59.9 ± 3 % and 383.9 ± 1.2 mg respectively, when treated with Aspergillus oryzae prior to alkaline extraction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".