Hemp (Cannabis sativa L.) protein: Impact of extraction method and cultivar on structure, function, and nutritional quality
Bibliographic record
Abstract
L.) is increasingly gaining traction as a novel and sustainable source of plant protein. Accordingly, the aim of this study was to investigate the effectiveness of two protein extraction methods, alkaline extraction coupled with isoelectric precipitation (AE-IEP) and salt extraction coupled with ultrafiltration (SE-UF) in producing hemp protein isolates (pH-HPI and salt-HPI) with high purity and yield. Structural characterization as impacted by extraction method and cultivar was performed and related to functional performance and nutritional quality. Both extraction methods, with carefully selected parameters, resulted in HPI with high purity (86.6-88.1% protein) and protein extraction yields (81.6-87.3%). All HPI samples had poor solubility (∼9-20%) at neutral pH compared to commercial soy protein and pea protein isolates (cSPI, cPPI). A relatively high surface hydrophobicity and low surface charge contributed to such poor solubility of HPI. However, HPI demonstrated similar solubility at acidic pH (50-67%) and comparable gel strength (up to 24 N) to cSPI. Comparing experimental amino acid composition to the theoretical amino acid distribution in hemp protein provided insights to the functional performance of the protein isolates. While pH-HPI demonstrated better functionality than salt-HPI, minimal structural, functional, and nutritional differences were noted among the pH-HPI samples extracted from four different cultivars. Overall, results from this work could be used to guide future attempts to further develop successful protein extraction processes, and to provide valuable insights to propel breeding efforts that target enhanced hemp protein characteristics 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) | 0.000 | 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) | 0.001 | 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".