Determination of Sulfite in Food by Liquid Chromatography Tandem Mass Spectrometry: Collaborative Study
Bibliographic record
Abstract
Sulfites are added to a wide range of food and beverage products to prevent browning or oxidation. Although most of the population do not experience side effects from consuming sulfites, a small subset has been shown to experience an "allergic-like" response. For this reason, the U.S. Food and Drug Administration requires that sulfites be labeled on all products that contain more than 10 mg/kg (parts per million) sulfur dioxide. The current regulatory method, optimized Monier-Williams (OMW) Official Method 990.28, has been successful in quantifying sulfites in most matrixes, but is time-consuming and has a method detection limit at the regulatory-labeling threshold. Recently, an LC-tandem MS (MS/MS) method was published that was applicable to a wide range of sulfite-containing matrixes. This method converts free and reversibly bound sulfite to the formaldehyde adduct hydroxymethylsulfonate, which could then be separated from matrix constituents using a hydrophilic interaction LC analytical column and subsequently be detected with tandem MS (MS/MS). In this study, multilaboratory validation was conducted with 11 laboratories in the United States and Canada. Four matrixes were spiked at varying concentrations and three additional commercially sulfited matrixes were included. An abbreviated comparison study between the LC-MS/MS and OMW methods was conducted for select samples. Average recoveries for all matrixes ranged from 86 to 114% with RSDr and RSDR values of 4.5-17.5 and 8.6-22.5%, respectively. Further comparisons will be necessary to draw comparisons between the two methods. This method proved to be a faster and more sensitive way to determine sulfites in food and beverages, showing promise for the continuing improvement of enforcement of sulfite labeling requirements to protect individuals who have sulfite sensitivity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".