Short communication: Separation and quantification of caseins and casein macropeptide using ion-exchange chromatography
Bibliographic record
Abstract
The aim of this work was to improve an existing method to separate and quantify the 4 major caseins from milk samples (i.e., containing whey proteins) using ion-exchange chromatography. The separation process was carried out using a mini-preparative cation exchange column (1 or 5mL of column volume), using urea acetate as elution buffer at pH 3.5 with a NaCl gradient. All 4 major caseins were separated, and the purity of each peak was assessed using sodium dodecyl sulfate-PAGE. Purified casein fractions were also added to raw milk to confirm their elution volumes. The quantification was carried out using purified caseins in buffer as well as added directly to fresh skim milk. This method can also be employed to determine the decrease in kappa-casein and the release of the casein-macropeptide during enzymatic hydrolysis using rennet. In this case, the main advantage of using this method is the lack of organic solvents compared with the conventional method for separation of macropeptide (using reversed phase HPLC).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".