Physicochemical and functional characterization of plant protein isolates and their influence on plant-based mozzarella cheese performance
Bibliographic record
Abstract
Consumer demand for plant-based cheese alternatives has highlighted major gaps in existing products, particularly regarding functionality and protein content. This study examined the properties of commercial plant protein isolates from pea (PP1, PP2, PP3), lentil (LP1), faba (FP1), and soy (SP1), which were then incorporated into cheese analogs (∼8.0% w/w) with coconut oil and waxy maize starch. Protein isolates were characterized through protein solubility, water-holding capacity, emulsion stability, and ζ-potential measurements. Protein secondary structures and particle size distributions were determined by Fourier-transform infrared (FTIR) spectroscopy and static light scattering, respectively. Viscoelastic properties, thermo-rheological behavior, and results from functionality experiments ( e.g. , TPA hardness, melt, oil loss) were compared to commercial dairy and plant-based mozzarella products. Differences in tan δ (G’/G”) at 95°C were relatively minor between protein isolate cheese analogs (0.48-0.68), as thermo-reversibility was predominantly influenced by the waxy starch component. Analogs made with PP3, FP1, and SP1 showed good melting behavior, with modified Schreiber test spreads of 108-114%. However, oil loss varied significantly between samples. Synchrotron-radiation X-ray microcomputed tomography (SR-μCT) revealed differences in oil droplet size distribution affecting oil loss and other functional properties, with LP1 and FP1 showing higher densities of small oil droplets correlating to reduced oil expulsion. PP3 analogs exhibited optimized performance, with PP3 proteins possessing the highest water-holding capacity (2.8 g/g), good emulsion stability, and the widest size distribution. Ball milling was also explored for modifying protein particle size distributions and evaluating the influence of structural changes on various protein properties and cheese analog performance. Analog functionality was found to be highly dependent on protein interactions, starch gelatinization, and oil body distribution. All plant-based cheese analogs were found to outperform commercial plant-based mozzarella, showing promise for the enhancement of functionality in dairy mozzarella mimetics using low-solubility protein isolates. • Insoluble proteins act passively to improve plant-based cheese functionality. • Physicochemical properties of plant proteins impact melt behavior and oil loss in cheese analogs. • Waxy starch networks maintain adequate structure while enabling thermo-reversibility. • Ball-milling alters protein structure, affecting starch and oil interactions.
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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".