Structuring Plant-Based Foods Using Less Refined Plant Proteins and High Moisture Extrusion
Bibliographic record
Abstract
Meat-eating consumers would prefer products that strongly resemble real meat, which triggers the search for understanding mechanisms behind anisotropic textures using more sustainable plant proteins with closer textures to animal meat. In this study, the first chapter aimed to understand the effect of protein isolation technologies (fractionation) on the compositional, colloidal, and nutritional properties of the protein fractions from a risk resilient protein source (hemp). The second chapter aimed to evaluate potential principles leading to anisotropy during high moisture extrusion using less-refined protein fractions. Results of these studies indicated that the food structuring of plant protein fractions during thermomechanical processing is related to both molecular and colloidal mechanisms acting in concert and involving proteins, polysaccharides, and multivalent ions. Although the specimens possessed unique and promising textures that were obtained without the need of using ultra-pure plant protein, the Warner-Bratzler force of our developed prototypes, common textural parameters inversely correlated with the tenderness of meats, was still lower than those from animal meats. Thus, the third chapter focused on investigating the potential of multifunctional protein fillers and the combination of top down (extrusion) and bottom-up (fibrillation) approaches to close the tenderness gap between animal meat and plant-based foods. For this purpose, the effect of incorporating low concentrations of two proteins with different network forming properties, the polar gelatin or the non-polar zein, in particulate and nano-fibrillated forms, on the mechanical properties of high moisture meat analogue prototypes was investigated. The addition of 0.1 % fibrillated gelatin or particulate/fibrillated zein increased up to 44% or 22% the Warner-Bratzler force of soy extrudates, respectively. The last chapter of this thesis aimed to understand the in vivo significance of instrumental textural parameters. Bi-component blends from high performing plant proteins were individually extruded into plant-based meat analogue prototypes that were for instrumental texture and in vivo Sensory Descriptive Analysis and benchmarked with chicken breast, pork cutlets and calf steak. The sensory evaluation revealed that hemp-based samples resembled pork and calf steak, whereas the sensory descriptors of pumpkin samples were closer to chicken breast. Water distribution (LF-NMR) in all plant-based and animal samples was correlated (r > 0.85) to after-taste mouthdrying, mothwatering, and mouthcoating. Tensile strength showed a significant correlation (r > 0.7) with hardness, chewiness, and compactness determined in vivo.
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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".