Harnessing the resolution power of two-dimensional liquid chromatography (2D-LC) for the screening of bisphenol contaminants in canned food items
Bibliographic record
Abstract
Overlapping peaks can be difficult to avoid in 1D-LC, which make the identification and quantification of compounds ambiguous, especially if the only available detector is a UV/DAD. To overcome this, a two-dimensional liquid chromatography (2D-LC) method to monitor Bisphenols (BPs), and particularly the chosen analogues BPA, BPB, BPF, BPS, BPZ, and BPAF, in a complex matrix (canned food) was developed and validated. BPs are endocrine disruptors present in the lining of the can, which may leach into the content of canned food. It is essential to continuously identify and monitor bisphenols that are unintentionally ingested using newly available and highly efficient methods such as 2D-LC. The coefficient of variation (CV) of peak areas and retention times for all analytes in both dimensions ranged from 0.050 to 2.960 %. The recovery of the whole procedure was determined to be 67.4 ± 0.1 % in the first dimension, and 64.3 ± 0.1 % in the second dimension. The limit of detection (LOD) determined in the second dimension for the analytes ranged from 0.075 to 0.301 ppm. Finally, four canned food samples were prepared by solid-liquid extraction, and surveyed for BPA, BPB, BPF, BPS, BPZ, and BPAF. BPF (0.093 ppm) and BPAF (2.656 ppm) were accurately detected in two separate samples. This report highlights the first successful application of 2D-LC to bisphenol compounds, an important class of emerging contaminants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".