High-moisture extrusion modifies texture and improves nutritional value of sunflower meal-pea protein meat analogues
Bibliographic record
Abstract
Incorporating protein-rich food industry by-products, such as sunflower meal (SFM), into foods aligns with the United Nations Sustainable Development Goals by promoting environmentally less resource-intensive food alternatives for a more secure food future. In this study, SFM and pea protein isolate (PPI) were selected for high-moisture meat analogue (HMMA) production due to their complementary amino acid profiles, low-cost, and relatively low allergenicity compared to common plant proteins like soy and wheat. HMMAs made from two blends of expeller-pressed SFM and PPI (40:60 and 50:50, w/w) were extrusion cooked at two different feed moisture contents (FMC) (48 % and 58 %, w.b.) and three different extruder barrel temperature profiles (60–80–115–125 °C, 80–100–125–135 °C, and 100–120–135–145 °C). The physical (texture and color) and nutritional (protein quality and anti-nutritional factors) quality of the resulting HMMAs were examined. While all HMMAs studied were harder and darker than cooked chicken, they became softer and lighter in color as FMC increased, and extrusion temperature decreased. Overall, all HMMAs studied had comparable protein quality to cooked chicken and beef, with 70–74 % (d.b.) protein content and 86–89 % in-vitro protein digestibility, with tryptophan being the first limiting amino acid. Moreover, extrusion processing significantly ( p < 0.05) reduced the levels of anti-nutritional compounds including phytic acid, trypsin inhibitors, and chlorogenic acid. These findings highlight SFM’s potential as a novel protein source in meat analogue formulations, demonstrating a sustainable way to add value to an underutilized by-product in the food industry.
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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".