Effects of ultrasound-assisted basic electrolyzed water (BEW) extraction on structural and functional properties of Antarctic krill (Euphausia superba) proteins
Bibliographic record
Abstract
A novel protein extraction method of ultrasound-assisted basic electrolyzed water (BEW) was proposed, and its effects on the structural and functional properties of Antarctic krill proteins were investigated. Results showed that BEW reduced 30.9% (w/w) NaOH consumption for the extraction of krill proteins, and its negative redox potential (-800 ~ -900 mV) protected the active groups (carbonyl, free sulfhydryl, etc.) of the proteins from oxidation compared to deionized water (DW). Moreover, the ultrasound-assisted BEW increased the extraction yield (9.4%), improved the solubility (8.5%), reduced the particle size (57 nm), favored the transition of α-helix and β-turn to β-sheet, promoted the surface hydrophobicity and disulfide bonds formation of krill proteins when compared to BEW without ultrasound. These changes contributed to the enhanced foam capacity, foam stability and emulsifying capacity of the krill proteins. Notably, all the physicochemical, structural and functional properties of the krill proteins were comparable to those extracted by the traditional ultrasound-assisted DW. This study suggests that the ultrasound-assisted BEW can be a potential candidate to extract proteins, especially offering an alternative way to produce marine proteins with high nutritional quality.
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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) | 0.000 | 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 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".