Purification and Characterization of Pinto Bean Protein Using Membrane Technology
Bibliographic record
Abstract
Pinto beans, an underutilized legume, are abundant in protein content and contain a variety of beneficial phytonutrients. However, the commonly used protein extraction method, alkaline extraction, is associated with several drawbacks. These drawbacks include low extraction yield and purity as well as the production of large amounts of wastewater that can lead to environmental hazards. In this regard, membrane technology has gained considerable recognition as a superior method for extracting proteins. A combined processing scheme was developed, which included alkaline extraction at pH 10.5, ultrafiltration with a concentration factor of 5.5, diafiltration with a diavolume of 4, and isoelectric precipitation at pH 4.5 followed by freeze drying. The specific functional characteristics (nitrogen solubility index, water and oil holding capacity, and emulsifying and foaming properties) of the protein concentrates were assessed and compared with those of a commercially available soybean protein isolate. Based on pinto bean flour containing 23.9% protein, 85.5% of the protein was recovered in the products of this process: precipitated protein concentrate (PPC) with 86.4% protein, acid-soluble protein concentrate (ASP-C) with 56.3% protein, and meal residue with 6.1% protein. The mass yields were 17.3% in PPC, 3.9% in ASP-C, and 54% in the meal residue. The precipitated protein showed higher emulsifying activity, and the acid-soluble protein showed a high nitrogen solubility index (NSI) and oil-holding capacity. Both proteins had comparable foaming properties to commercial soy protein isolate. The project demonstrated the feasibility of protein production from pinto beans and highlighted the proteins' useful food functionality and good potential for commercialization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".