Legume starch and flour-based emulsion gels as adipose tissue mimetics in plant-based meat products
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it