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
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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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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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