Advances and Applications of Capillary Electrophoresis Mass Spectrometry in Food Analysis: Strategies for Online and Offline Preconcentration
Bibliographic record
Abstract
Advancements in food technology have increased the need for thorough analysis to ensure food safety, quality, and compliance with regulatory requirements. Capillary electrophoresis-mass spectrometry (CE-MS) has emerged as a powerful tool in food analysis due to its high separation efficiency, low sample consumption, and ability to handle complex matrices. However, challenges such as the use of volatile running buffers and maintaining the stability of the electrical circuit connecting the CE and MS systems have been addressed through advancements in interface designs, such as sheathless systems and optimized sheath-liquid compositions. Online and offline preconcentration techniques have significantly enhanced CE-MS sensitivity (up to 1000-fold) through stacking methods such as large volume sample stacking (LVSS) and dynamic pH junction stacking. Meanwhile, offline sample preparation techniques, such as solid-phase extraction (SPE) and liquid-based methods, are essential for removing matrix interferences and preconcentrating targeted analytes. This review explores both online and offline preconcentration methods and emphasizes the importance of CE-MS in helping researchers develop effective strategies for selecting the best preconcentration methods for food analysis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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".