Exploring the Potential of Lupin ( <i>Lupinus angustifolius</i> ) Flour‐Based Ingredients in Developing High Moisture Meat Analogues
Bibliographic record
Abstract
The rising demand for sustainably and ethically produced alternatives to animal protein-rich foods has driven interest in plant-based meat analogues. This study evaluated the potential of lupin flour (LF), protein isolate (LPI), and their blends with soy protein isolate (SPI) to produce high-moisture meat analogues (HMMAs) through extrusion cooking. Six SPI-LF-LPI blends, with protein contents ranging from 64.5% to 80.5%, were extruded under three feed moisture contents (FMC) of 60%, 65%, and 70%. Increasing LF content affected the textural attributes of the HMMAs, reducing their hardness, chewiness, and gumminess. The peak force to cut the HMMAs in longitudinal and transverse directions ranged from 3.3 to 10 N, with the softest textures observed for blends containing relatively higher LF and LPI and at the higher FMC level of 70%. In vitro protein digestibility of the HMMAs improved with increasing FMC, reaching a maximum proteolysis degree of 51.5% for the blend containing 55% SPI and 45% LF produced at 70% FMC. Although extrusion reduced the antioxidant capacity of the HMMAs compared to their raw counterparts, the antioxidant capacity of the HMMAs increased as the FMC level increased. These findings highlight the feasibility of using lupin ingredients to produce nutritionally rich and texturally appealing plant-based meat analogues when extrusion conditions are fine-tuned.
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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".