Dispersion of low concentration of particulate or fibrillated protein fillers into plant protein melts and their impact on the elasticity and tenderness gap of plant-based foods
Bibliographic record
Abstract
The development of plant-based foods has emerged as a promising strategy to decrease meat consumption. Using predominantly globular plant proteins challenge the crafting of cohesive, viscoelastic, and anisotropic structures similar to animal meat. In this study, we investigated the effect of incorporating low concentrations (<1%, w/w) of the polar gelatin or the non-polar zein on the mechanical properties of high moisture meat analogue prototypes. These proteins were studied in both particulate (bulk) and nano-fibrillated forms. Building on our previous work, soy protein (68% protein) and mung bean protein (81% protein) concentrates were chosen based on their distinct protein purity and high propensity to rearrange into new ordered secondary structural elements upon extrusion. Gelatin and zein were incorporated into the extrusion process via aqueous dispersion in particulate form or previously converted into nanofibers (210–440 nm diameter) using food-grade electrospinning. The incorporation of both fillers (either particulate or fibrillate) decreased the elasticity and Warner-Bratzler force (WBSF, known to be inversely correlated to meat tenderness) of mung bean extrudates. However, the addition of 0.1% fibrillated gelatin or particulate/fibrillated zein increased up to 44% or 22% the WBSF of soy extrudates, respectively. In all cases, the incorporation of fillers caused dramatic concentration-dependent changes in the secondary structures (FTIR) of soy proteins and the water mobility (LF-NMR) of extrudates. This work highlights the potential of multifunctional fillers and the combination of top-down (extrusion) and bottom-up (fibrillation) approaches to close the tenderness gap between animal meat and structured foods made with less-refined plant-protein fractions.
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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".