Effects of Continuous Dielectric Barrier Discharge Cold Plasma on Fava Bean Protein Isolate Functionality and 3D Printability
Bibliographic record
Abstract
Plant proteins are emerging food ingredients in the food processing industry. Fava bean proteins are highly nutritious due to their complete amino acid profile, making them an ideal candidate for human consumption. However, they exhibit poor functionality, including poor solubility and gelling properties. To improve the functionality of fava bean protein isolates (FBPI), cold plasma treatments can be used for protein modification and improvement of functionality. Cold plasma is a non-thermal technology that produces reactive species capable of modifying the structure and functionalities of plant proteins. This study explored the effects of continuous dielectric barrier discharge (DBD) plasma on FBPI. Continuous treatments were investigated as they are more scalable compared to batch treatments, which can be time-consuming and expensive. For treatment, FBPI were combined with water to create a consistent suspension, which was then recirculated between a sample container and the treatment platform, where it was exposed to DBD plasma. FBPI exposed to DBD plasma demonstrated changes in secondary structures, primarily transforming α-helices into β-sheets, with 12.5% and 66% increases in β-sheets for dry and wet FBPI (DW-FBPI) and deep eutectic solvent extracted FBPI (DES-FBPI) samples, respectively. In addition, treated DES-FBPI gels heated at 80°C exhibited a 70% increase in gel hardness, as determined by texture profile analyses. Improvements in 3D printability were also observed, including enhanced gel extrudability and increased structural rigidity. Overall, DBD plasma-treated samples produced 3D-printed structures that were more precise, rigid, and with reduced deformation. Overall, DBD plasma treatments were shown to be an effective method for modifying the structural and functional properties of FBPI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".