Hemp (Cannabis sativa L.) protein concentrates from wet and dry industrial fractionation: Molecular properties, nutritional composition, and anisotropic structuring
Bibliographic record
Abstract
Hemp seeds stand as a rich source of globulins and albumins with all the essential amino acids and a balanced amino acid profile. Nevertheless, the potential of dry- and wet-extracted hemp protein concentrates to create plant-based, fibrous High Moisture Meat Analogues (HMMAs) remains unknown. In this study, five distinct hemp seed protein concentrates (55.9–76.4% protein, d.b.) produced at industrial scale using dry or wet fractionation were investigated and compared for their functionality and protein molecular properties. Furthermore, non-proteinaceous components were also analysed to elaborate on the underlying mechanisms for the structuring behaviour of hemp protein concentrates during wet extrusion. Although dry fractionation resulted in lower protein concentration, hemp proteins retained their native oligomeric state and their albumin fraction, thus showing higher surface hydrophobicity and solubility, and lower gelation concentration than wet-extracted counterparts. Furthermore, hemp samples were richer in bound polyphenols (>1800 mg/100 g), presumably phenolic acids, than the control pea sample (1363 mg/100 g), which resulted in dark colours in wet-extracted samples. Selected HMMA prototypes were developed and investigated for anisotropy, viscoelasticity, and proton NMR relaxometry. Visual and instrumental anisotropy dramatically increased with the use of hemp protein concentrates, from 0.69% using pea, to 0.98% and 1.41% using dry- and wet-fractionated fractionated hemp, respectively. Nonetheless, dry-fractionated hemp HMMA showed the highest proportion of free water in the system (T23 proton relaxation time), and an intermediate viscoelasticity between pea and wet-fractionated hemp HMMAs. Interestingly, SDS-PAGE revealed a significant contribution of disulphide bonds on hemp protein aggregation during processing.
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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 | 0.000 |
| 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) | 0.000 | 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".