High Voltage Electrical Treatments To Improve the Protein Susceptibility to Enzymatic Hydrolysis
Bibliographic record
Abstract
The rapidly growing global population raises important issues associated with the environmental burden imposed by agri-food and biotechnological industries to satisfy the increasing demand of high-quality food and nutraceuticals. Hence, the introduction of emergent ecoefficient technologies in the bioproduction lines is inevitable. The present study deals with environmentally sustainable high voltage electrical treatments (HVETs)—pulsed electric field (PEF) and electrical arc—to improve the susceptibility of β-lactoglobulin to enzymatic hydrolysis. This protein was chosen due to its high excess in dairy industry coproducts, which must be valorized. The results demonstrate that, at the optimal HVET duration of 10 min (voltage = 40 kV and pulse frequency = 0.5 Hz), the degree of hydrolysis can be improved by 80% and 66% for the PEF and electrical arc, respectively. This fact is related to the ability of HVET to induce the active sites formation in protein molecule for the nucleophilic enzymatic action, which leads to the release of bioactive and functionally active peptides. Moreover, it is possible to control the selectivity of hydrolysis by varying the HVET modes. Thus, the implication of HVET to valorize the dairy whey proteins by their enzymatic hydrolysis can significantly improve the process ecoefficiency.
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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".