Legume starch and flour-based emulsion gels as adipose tissue mimetics in plant-based meat products
Bibliographic record
Abstract
• Adipose tissue mimetics were fabricated with pea starch and chickpea flour containing 40 % oil. • The mechanical properties, melting behavior and oil retention of the adipose tissue mimetics matched those of pork and beef adipose tissue. • Incorporation of adipose tissue mimetics into extra lean ground beef resulted in a burger with similar textural properties as medium ground beef. • Adipose tissue mimetics retain oil and provide texture to ground meat analogue products. Fat in meat analogues is present in free form; however, in animal tissue it is located within the collagen scaffolds of adipose tissue. The absence of adipose tissue in plant-based meat analogues is partially responsible for some negative textural sensorial properties of these products, including a lack of hardness, chewiness, juiciness, and oil binding. Pea starch, a sustainable and cost-effective gelling agent, provides structure and oil retention at high temperatures. X-ray micro-computed tomography shows that pea starch emulsion gels form a starch network containing oil in pockets, resembling adipocytes. We also determined that addition of chickpea flour reduces oil loss without sacrificing texture, and an optimal 6 % pea starch, 4 % chickpea flour, 40 % oil (7:3 coconut: sunflower oil) gel was fabricated. The thermal behaviour of the emulsion gels are shown replicate that of beef adipose tissue from 5–85°C . The texture profile of lean ground beef can be matched to medium ground beef when emulsion gels are added to achieve the same fat content. Pea starch/chickpea flour-based emulsion gels can effectively replicate the functional properties of beef adipose tissue.
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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".