Determination of β-N-methylamino-L-alanine, N-(2-aminoethyl)glycine, and 2,4-diaminobutyric acid in Food Products Containing Cyanobacteria by Ultra-Performance Liquid Chromatography and Tandem Mass Spectrometry: Single-Laboratory Validation
Bibliographic record
Abstract
A single-laboratory validation study was completed for the determination of β-N-methylamino-L-alanine (BMAA), N-(2-aminoethyl)glycine (AEG), and 2,4-diaminobutyric acid (DAB) in bulk natural health product supplements purchased from a health food store in Canada. BMAA and its isomers were extracted with acid hydrolysis to free analytes from protein association. Acid was removed with the residue evaporated to dryness and reconstituted with derivatization using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AccQ-Fluor). Chromatographic separation and detection were achieved using RP ultra-performance LC coupled to a tandem mass spectrometer operated in multiple reaction monitoring mode. Data from biological samples were evaluated for precision and accuracy across different days to ensure repeatability. Accuracy was assessed by spike recovery of biological samples using varying amino acid concentrations, with an average recovery across all samples of 108.6%. The analytical range was found to be 764-0.746 ng/mL prior to derivatization, thereby providing a linear range compatible with potentially widely varying analyte concentrations in commercial health food products. Both the U. S. Food and Drug Administration (FDA) and U. S. Pharmacopeia definitions were evaluated for determining method limits, with the FDA approach found to be most suitable having an LOD of 0.187 ng/mL and LLOQ of 0.746 ng/mL. BMAA in the collected specimens was detected at concentrations lower than 1 μg/g, while AEG and DAB were found at concentrations as high as 100 μg/g. Finding these analytes, even at low concentrations, has potential public health significance and suggests a need to screen such products prior to distribution. The method described provides a rapid, accurate, and precise method to facilitate that screening process.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".