Exploring the synergistic potential of pH and ultrasonication on the functional properties of pea and lentil protein isolates and its formulation in food product
Bibliographic record
Abstract
The substitution of meat proteins with plant-based proteins from various sources is often motivated by nutritional considerations. However, the inherent limited solubility of plant proteins, which results in suboptimal techno-functional properties, remains a persistent challenge in food formulation. The purpose of this study was to utilize unique properties of pea (Pisum sativum L.) and lentil (Lens culinaris) through ultrasonication and pH variation in order to develop a stable and consumable protein-based product. For this purpose, pea and lentils powders were subjected to salt extraction (SE) and proceeded to further treatment with ultrasonication and pH shifting at pH 2, 4, 10 and 12 with ultrasound assisted extraction (UAE) at 20 kHz at 20 °C for 5 min for each treatment. The developed protein isolates (PIs) after these treatments were analyzed for functional properties including surface hydrophobicity, solubility, foaming capacity and stability, emulsifying activity and stability indices, turbidity and molecular weight. Among these treatments, the pH-12 treated samples showed optimal values for the functional profile, and this treatment was selected for further development of meat-analogous nuggets through assorted combinations of pea protein isolate (PPI) and lentil protein isolate (LPI) along with control treatment containing chicken nuggets. After development, nuggets were further tested for moisture content, total plate count and textural hardness for a storage period of 4 weeks along with sensory profile analyses of the developed nuggets. Meat-analogous nuggets prepared from 60 % LPI and 40 % PPI had an optimal response to texture profile and were also favored in sensory profile. This product might help the protein requirement as a vegan-based diet and enhance peas and lentils productivity into a marketable and nourishing product if it were to be developed more widely and commercialized.
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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".