Effects of Vigorous Blending on Yield and Quality of Protein Isolates Extracted From Cottonseed and Soy Flours
Bibliographic record
Abstract
Cottonseed protein has shown great potential as a biodegradable and renewable resource for industrial processes such as the manufacture of wood adhesives. To improve the recovery of the protein from cottonseed flour, we tested the effects of vigorous blending on the extraction efficiency and recovery yield of one- and two-step procedures for isolation of cottonseed protein. For comparison, the effects on one-step soy protein isolation were also examined. Our data indicated that vigorous blending improved the protein recovery from cottonseed and soy flour as much as 40-60%, compared to mild agitation in the extraction phase. The improvement was likely due to the enhanced solid (flour)-liquid (extracting solvent) interaction, and the increased extraction temperature of the vigorous blending process. Similarities in the protein content, molecular mass distribution pattern, and secondary structure of each type of protein isolates processed under different blending treatments indicated that quality of the isolates was not altered by vigorous blending. However, dissimilarities in molecular mass distribution patterns and secondary structures were identified between the different types of isolates (i. e. total, water soluble, and alkali soluble cottonseed proteins, and total soy protein). These differences will enable us to explore in future work the correlations between cottonseed protein structures and industrial use characteristics (such as adhesive properties).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".