Determination of vitamin K concentration in Canadian cheese using liquid chromatography-mass spectrometry
Bibliographic record
Abstract
Vitamin K refers to a family of fat soluble vitamins which includes phylloquinone (vitamin K1) and menaquinones (vitamin K2). All forms of vitamin K act as cofactors for γ-glutamyl carboxylase, an enzyme which adds Gla residues to certain proteins and are required for the protein to bind calcium. Differences in metabolism of phylloquinone and menaquinones result in phylloquinone being primarily used to help activate blood clotting factors while menaquinones help activate other proteins such as osteocalcin and Matrix-Gla protein. These two proteins have been shown to help fight osteoporosis and heart disease. Increased vitamin K intake has been shown to fight these diseases, but little information is available on the amount of vitamin K in food. To gain more information on the amount of vitamin K in Canadian foods, liquid chromatography-mass spectrometry was used to measure phylloquinone, menaquinone-4 (MK-4), and menaquinone-7 (MK-7) in Canadian cheese obtained from grass-fed cows and conventional supermarket cheese. Samples were homogenized in water, melted in a boiling water bath with HCl and then extracted with hexanes before filtration and injection into the LC-MS. A calibration curve from 0.1 to 1 µg/g was obtained for phylloquinone and MK-4, but was unsuccessful for MK-7. Gouda cheese made from grass-fed cows during the early spring and summer contained very high amounts of phylloquinone and MK-4, as did Maasdammer cheese from grass-fed cows. Vitamin K was not detected in conventional supermarket cheddar cheese and only MK-4 was detected in Gouda from the spring. The extraction procedure likely had a low recovery as the peak area of the internal standards was 10-20x larger in the standards than in the samples. A LC-MS method was successfully used to detect low concentrations of phylloquinone and MK-4, but further optimization of the extraction procedure is required for analysis of a larger number of samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".