The illegal use of β-agonists in livestock production poses a severe threat to food safety. Although gas chromatography–mass spectrometry (GC–MS) is a powerful analytical tool, its application in food quality supervision is hindered by the low volatility of β-agonists and their analogues. To address this technical bottleneck, we developed a novel hybrid derivatization method coupling an optimized microwave-assisted protocol with an 80:20 mixed derivatizing reagent of N -methyl- N -(trimethylsilyl)trifluoroacetamide (MSTFA, containing TMSI) and N -methyl-bis(trifluoroacetamide) (MBTFA). The characteristic derivative product profiles were constructed for four types of β-agonists; all the derivative products were well investigated, interpreted and identified via structural elucidation based on electron ionization (EI) fragmentation patterns, with the assistance of Mass Frontier software. Notably, this method can shorten the derivatization time to 5 min, comparing to 30–60 min for conventional methods. The reproducibility and robustness of the derivatization method were evaluated based on the relative standard deviations (RSDs) obtained across different sample matrices, analytical batches, and operators, all of which were within acceptable ranges in compliance with relevant EU regulations. This innovative hybrid derivatization strategy effectively mitigates the low volatility challenge of β-agonists and their analogues in GC–MS analysis, thereby laying a robust foundation for the development of advanced GC–MS-based detection methodologies. It thus enables comprehensive, rapid and sensitive detection of β-agonists and their analogues in feeds and livestock products, further underpinning food safety supervision and safeguarding public health. • Characteristic derivative product profiles for four types of β-agonists were established based on a novel hybrid derivatization strategy. • Characteristic derivatized products of β-agonists were systematically interpreted and structurally elucidated based on their mass spectra. • Optimal derivatization parameters ensure the diversity of derivative products and the derivatization yield.
Li et al. (Wed,) studied this question.