Fractionation of pea flour components using continuous and discontinuous density gradient centrifugation
Bibliographic record
Abstract
Density gradient centrifugation (DGC) has been widely used for biomolecule separation based on their buoyant density. This study investigated the effectiveness of DGC in fractionating pea flour into starch- and protein-enriched fractions using three density gradient media (DGM) – polyvinylpyrrolidone (PVP), Optiprep, and corn syrup – applied as a continuous Optiprep gradient and five discontinuous gradients by layering different DGM. DGC was performed using 5 g of pea flour in 10 ml of DGM at 14,000 x g for 10 min. DGC achieved a clear fractionation of pea flour into top and bottom fractions, with 80–90% product recovery. The bottom fractions exhibited higher bulk and tapped densities than the top fractions, indicating successful density-based separation. Compositional analysis confirmed that starch was concentrated in the bottom fractions, while the top fractions were protein rich. The PVP-Optiprep-corn syrup gradient achieved three layered fractions with 71.83% starch (dry basis) in the middle fraction and 36.84% protein (dry basis) in the top fraction. Scanning electron microscopy also confirmed distinct starch granules in the bottom fractions and protein-starch aggregates in the top fractions. This study demonstrates the potential of DGC as a sustainable method for pea flour fractionation, offering an alternative to conventional starch and protein isolation techniques.
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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 | 0.001 | 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 | 0.001 |
| 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".