Impact of Five Soy Proteins on Lean Chicken Breast Systems with Varying Moisture Contents: Cooking Loss, Texture, Microstructure, and T2 NMR
Bibliographic record
Abstract
With increasing global meat consumption, meat–plant hybrid products have gained interest as a sustainable alternative. Soy proteins have been used in small quantities (2–3%) as meat extenders, yet limited data exist on their use at higher levels. Here, five commercial soy proteins (four isolates: SPI-A to -D; one concentrate: SPC) were used for meat replacement in lean meat batters with 0/40/80% added water. Cooking loss, texture, light micrographs, and T2 relaxation were analyzed. At 33% and 66% meat replacement, soy protein treatments maintained comparable or reduced cooking loss; SPI-D and SPC were the least and most effective, respectively. Complete replacement eliminated cooking loss in 0% and 40% water systems but failed to form self-supporting gels in the 80% system. At 33% replacement, SPI-A to -C generally increased hardness, whereas increasing the replacement level further to 66% decreased it. In the 0% and 80% systems, SPI-A treatments exhibited hardness comparable to controls, SPI-D treatments drastically reduced hardness, and SPC treatments maintained greater hardness than the controls even at 66% replacement. Micrographs offered potential explanations for these macroscopic measurements. NMR T2 data indicated that soy proteins restricted water mobility both pre- and post-cooking. Specifically, in the 40% and 80% systems, the T22 peaks (expelled liquid) of the hybrid samples containing 33% SPI-A were ~350 ms and 760 ms, compared to ~570 ms and 1170 ms for the meat controls, respectively. In conclusion, most soy proteins (except SPI-D) enhanced water binding, with SPI-A showing optimal texture and SPC showing promise as a more economical alternative.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".