Effects of 0–12% soy proteins (four texturized and one isolate) on a lean hybrid meat system: cooking loss, texture, dynamic rheology, microstructure, and T2 NMR
Bibliographic record
Abstract
• SPI and four TSPs were evaluated in a lean meat system. • Increasing soy protein inclusion decreased cooking loss and increased hardness. • SPI increased the final storage modulus G'. • TSPs reduced the T 2 NMR relaxation time more than SPI. • Small-size TSP caused discontinuities in the meat matrix at 12 % inclusion. Meat and plant hybrid products have recently emerged as part of the global plant-forward movement. Soy proteins have been used at low levels (2–3 %), in meat products since the 1960s, mainly to enhance yield and sensory characteristics. This study assessed the structure-function relationship in products containing 0–12 % soy protein isolate (SPI) and four texturized soy proteins (TSPs: TA, TB, TC, and TD). Soy proteins significantly reduced cooking loss and increased hardness compared to the control (CL, no soy), with these effects intensifying as inclusion levels increased. At 12 %, TD resulted in lower hardness than the other soy proteins. Dynamic rheology revealed that at 6% inclusion, SPI increased the final storage modulus (G') compared to the CL, whereas TA, TB, and TC decreased it. The TD treatment exhibited a final G' similar to CL. Micrographs showed that 12 % TB (smallest texturized soy particles) caused discontinuity in the meat matrix, while TD (largest particles) confined meat components within its structure. T 2 NMR profiles revealed that all soy proteins restricted the water mobility of the meat batters, with TSPs showing a more pronounced effect than SPI. The relative order of T 21 values of cooked meat batters aligns with the cooking loss results. Overall, TSPs showed superior water binding to SPI. In this study, the larger size of TSPs likely had a favorable effect on their binding to the meat matrix at the high inclusion level (12 %). These findings provide insights into the selection of soy proteins for hybrid meat production.
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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".