Development of a Liquid Chromatography−Tandem Mass Spectrometry Method Using Capillary Liquid Chromatography and Nanoelectrospray Ionization−Quadrupole Time-of-Flight Hybrid Mass Spectrometer for the Detection of Milk Allergens
Bibliographic record
Abstract
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of the tryptic digest of a cleaned-up food matrix extract was used for the detection of milk allergens. The emphasis of this study was on casein, which is the most abundant milk protein and is also considered the most allergenic. A sample cleanup method was developed using an ion exchange column and centriprep device. Cookies spiked with milk powder from 0 to 1250 ppm were extracted, cleaned up, and either digested directly by trypsin or further cleaned up by gel electrophoresis before digestion. The peptide mixture was analyzed on a capillary LC-quadrupole time-of-flight system. Two marker peptides from alphaS1-casein were identified and used for prescreening. The MS/MS data from the mass spectrometry system were processed with Masslynx v4.0 and submitted for database search using either ProteinLynx Global Server or Mascot for protein identification. The LC-MS/MS method, using casein enzyme-linked immunosorbent assay as a reference, was tested on the cookie matrix and was extended to other sample matrices. There were good agreements between the two. This LC-MS/MS method provides a valuable confirmatory method for the presence of casein. It also allows the simultaneous detection of other milk allergens.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".