Imaged capillary isoelectric focusing and online mass spectrometry for milk whey protein characterization in dairy products
Bibliographic record
Abstract
Characterizing major bovine milk proteins, including whey and casein, is of significant interest in the dairy industry. The diverse array of protein proteoforms can be different in terms of genetic variation, breed ways, lactation stage, and animal nutritional status. Current routine methods for bovine milk protein profiling are typically based on immunological techniques, infrared spectroscopy, slab gel isoelectric focusing, capillary electrophoresis, and high-performance liquid chromatography. However, there are obvious disadvantages of existing approaches including low throughput, tedious operation, unsatisfactory repeatability, and lack of robust quantitation capability. In this study, we present a novel approach that, for the first time, combines imaged capillary isoelectric focusing with mass spectrometry to separate and characterize whey proteins in milk products. The established method provided a rapid, repeatable, accurate, and simultaneous analysis of α-lactalbumin, β-lactoglobulin A, and β-lactoglobulin B within 10 min for diverse bovine milk samples. The methodology was systematically validated regarding repeatability of pI and peak area, sensitivity, linearity and recovery. The integration of high-resolution mass spectrometry with nano-electrospray ionization and icIEF has been pivotal in accurately identifying intact whey proteins in milk products. This approach has significantly enhanced the precise characterization of protein proteoforms in milk.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".