Characterisation of Protein Isolates Prepared from Processed Mungbean (Vigna radiata) Flours
Bibliographic record
Abstract
Pulses are normally processed prior to consumption, are high in protein, providing opportunities for improving nutritional qualities of food. Processing methods can modify nutritional properties, affecting protein content, composition and functionality. In this study, we investigated the effect of processing methods on protein concentration and yield from protein isolates prepared by alkaline solubilisation and isoelectric precipitation, from whole seed flour, raw dahl flour, roasted dahl flour and germinated flour of mungbean. Protein isolates contained protein contents ranging from 87.3 to 90.4 g 100 g-1. The effect of processing methods on protein solubility characteristics was evident, as protein yields ranged from 75.7 (whole seed flour), 80.8 (raw dahl flour), 27.1 (roasted dahl flour) and 65.9 g 100 g-1 (germinated seed flour). Essential amino acid content was higher in protein isolates prepared from germinated flour, at 30.03 g 100 g-1 (39.1% of amino acids), compared to raw dahl flour, at 27.08 g 100 g-1 (38.3% of amino acids). A comparative proteomic analysis of protein isolates, prepared from raw dahl and germinated flour, resulted in the inferred identification of 214 proteins from protein sequence databases, in which, 72 proteins were classified as being common, 42 proteins specific to raw dahl flour and 28 proteins specific to germinated flour. Processing methods such as roasting and germination can significantly alter flour protein solubility, consequently, impacting on overall efficiency of protein extraction. The comparative proteomic analysis used in this study proved to be useful for investigating changes in protein composition and relative abundance, highlighting the potential in applying this technology for further characterisation of modified protein fractions for food applications.
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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 | 0.001 | 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 | 0.001 |
| 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".