A Rapid High‐Throughput Method for Determining Albumin and Globulin Contents in Pea and Soybean Seeds
Bibliographic record
Abstract
ABSTRACT Protein fractionation and characterization are essential to understanding the functional properties of seed proteins, which are valuable ingredients for modern plant breeding and the food industry. To address the need for a rapid, cost‐effective, and high‐throughput (HPT) method for assessing the albumin/globulin ratio in pea and soybean, we adopted a selective extraction procedure using a high‐salt buffer at neutral pH. We have further modified this method for highly selective extraction that operates on a microscale, utilizing approximately 20 mg of seed meal per assay, which has been highlighted with improved efficiency and accuracy. This approach mitigates issues of cross‐contamination between albumin and globulin fractions that are encountered in older methods. The albumin/globulin ratios for extracted protein isolates were obtained by applying the Bradford method. The findings of the HPT technique have remained within usual ranges, in contrast to large‐scale extraction (LSE). The albumin/globulin ratios of green peas were 0.12 ± 0.01 and 0.13 ± 0.01, attained by LSE and HTP, respectively. The LSE and HTP values for soybeans were 0.14 ± 0.01. The protein isolates from yellow pea yielded the results of 0.17 ± 0.02 using LSE and 0.19 ± 0.02 with HTP. The assay was validated by comparing the albumin/globulin fractions with electrophoretograms, which are obtained by curve integration of Coomassie blue‐stained SDS‐PAGE gels of pea and soybean seed protein isolates. This method was also successfully applied to analyze commercially available green and yellow pea varieties, each exhibiting distinct albumin/globulin ratios, emphasizing the effectiveness and distinctive functionality of this method.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".