Enhancing the potential of brewer's spent grain utilization in human nutrition through pulsed electric field-assisted protein extraction
Bibliographic record
Abstract
Brewer's spent grains (BSG), the main by-product of beer production, represent a promising source of proteins suitable for human nutrition, aligning with global efforts to reduce food industry waste and support a sustainable economy. However, given the complexity and heterogeneity of the matrix, protein extraction is challenging. The present study proposes to evaluate the impact of BSG pretreatment with pulsed electric fields (PEF) on protein extraction and functional properties of protein extracts at different pH values (3, 5, and 7). The protein extraction yield with the best PEF pretreatment condition of 120 pulses (71.4 %) was not significantly improved compared to unpretreated extraction (74.0 %). However, extracts pretreated with PEF demonstrated structural differences compared to conventional ones. Those differences led to significant improvements in functional properties. Indeed, protein solubility was improved by up to 81 %, and the foaming capacity and stability were increased by 104 % and 21 %, respectively, compared to the control extract. PEF pretreatments also showed an improvement in water-holding capacity (up to 11.5 g/g) compared to unpretreated extract (3.9 g/g), which confirms their potential for uses in food formulations.
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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".