Effect of particle size, flour:water ratio and type of pulse on the physicochemical and functional properties of wet protein extraction
Bibliographic record
Abstract
Abstract Background and Objectives Extraction of proteins by alkaline extraction followed by isoelectric precipitation at three different flour: water (f:w) ratios (1:10, 1:7, and 1:5) were investigated for four pulse flours: yellow pea (YP), green lentil (GL), kabuli chickpea (CH), and navy bean (NB). Pulse flours were prepared by pre‐breaking the seeds and milling into flour using a Ferkar (knife) mill with either a 0.5 mm (P1) or 1.27 mm (P2) screen. Surface properties and functionality of the protein concentrates were evaluated. Findings Extraction yields showed no major differences between the investigated f:w ratios. Protein yields decreased significantly as the amount of water decreased. The 1:10 f:w ratio was selected to perform extractions with coarser flours (1.27 mm; P2). Results showed that flour with a smaller particle size increased yields by 0.4% to 3.2% for extraction, and 7.9%–10.3% for protein. Functional properties showed no major differences between proteins extracted at different f:w ratios, although differences were found between the different pulse types. In some cases, P2 and P1 concentrates differed in functional properties, but this was not consistent for all pulses. Conclusions Finer flour (P1) and higher f:w ratio (1:10) resulted in higher extraction and protein yields. Significance and Novelty This study highlights the importance of milling and control of the particle size of the flours and water use on the preparation of protein concentrates.
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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 | 0.001 |
| 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 | 0.001 | 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".