Improvement in 3D Printability and Functionalities of Fava Bean Protein Isolates by Cold Plasma Technology
Bibliographic record
Abstract
Plant-based proteins have gained significant popularity in different forms, such as supplements or ingredients in food products. Different plant proteins, such as fava bean protein isolates (FPBI) are being explored for their use in the food industry. Fava beans are an excellent choice as they feature a high protein content and thrive in Canadian climates. FBPI have great potential in the food industry; however, they tend to have poor functionality, resulting in challenges for their application in the food industry. The research featured in this thesis aims to improve the FBPI functionalities by utilizing two different cold plasma treatments, including direct and indirect methods. Two different sources of sustainably extracted FBPI were used in this research. FBPI samples were first mixed with water to create a protein suspension. For indirect cold plasma treatment, the FPBI were mixed with plasma-activated water to induce physicochemical modifications on the protein. For direct cold plasma treatment, FBPI were mixed with water before applying continuous cold plasma treatment directly on the protein suspension. The cold plasma treatments resulted in major changes in FBPI protein secondary structures, as well as improvements in their 3D printability, gel texture, and rheological properties. The cold plasma treatments also modified the color of the protein gels to varying degrees. The findings in this research demonstrated that cold plasma treatments are effective in improving FBPI functionality, which further supports their potential applications in the food industry. With improved functionality, FBPI can be utilized in food formulations, improving food nutrition and ultimately sustainability.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".