Extraction techniques for the development of protein-enriched extracts from canola meal
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».