Transforming oilseed blends: the impact of low-moisture extrusion on antinutritional factors, protein structure, and nutritional value
Bibliographic record
Abstract
Oilseed cakes from hemp, rapeseed, and flaxseed are protein-rich, sustainable sources but are limited in food applications by antinutritional factors. This study blended meals from these oilseeds with pea or hemp protein ingredients (50:50 w/w) and applied low moisture extrusion (10 % and 20 %) at 122 °C to investigate their impact on physicochemical characteristics of oilseeds blends. Extrusion preserved protein content, reduced protein solubility by up to 44.5 %, and improved in vitro digestibility by up to 13.5 %. Antinutritional factors, including polyphenols (-10.18 % to -52.80 %), saponins (-4.48 % to -21.31 %), condensed tannins (-20.37 % to -41.05 %), and trypsin inhibitors (-2.26 TIU/mg to -13.31 TIU/mg), were significantly reduced, though phytic acid content was less affected. Extrusion decreased surface hydrophobicity, disrupted protein-protein interactions, altered secondary structures, and retained protein profiles under reducing conditions. These findings provided valuable scientific insights into the application of extrusion in enhancing nutritional value and modifying structure of plant-based meat alternatives.
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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".