Abstract

Various types of milk powder purportedly providing diverse health functions have emerged with the growth of the country's elderly population. Some manufacturers illegally add chemical drugs to their products to achieve their reported benefits, which poses a threat to consumer health. The existing standard methods are inapplicable to such complex sample matrices and require testing based on functional claims and classification. These limitations not only consume manpower and resources but also seriously impede daily regulatory efforts to detect unknown risk substances. In this study, a high-throughput method for the screening and quantitative analysis of 300 illegally added chemical drugs in functional milk powder and an identification strategy for unknown structural analogues were established using Zeno SWATH® data-independent acquisition (DIA) ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) technology combined with a QuEChERS sample purification method. The QuEChERS purification process was developed according to the characteristics of milk powder matrix. The supernatant was separated on a Kinetex F5 column (100 mm×3.0 mm, 2.6 μm) by gradient elution using 5 mmol/L ammonium formate aqueous solution (0.1% (v/v) formic acid, ) and methanol-acetonitrile (1∶1, v/v) as mobile phases. The method was validated in terms of selectivity, linearity, limits of detection and quantification (LODs and LOQs, respectively), matrix effect, accuracy, and precision. Based on a screening database for the 300 target substances, electron-activated dissociation (EAD) fragmentation was applied to obtain rich secondary MS fragmentation information, and unknown structural analogues were identified and confirmed through fragment attribution analysis. The results indicated that all compounds had good linear relationships in certain ranges with correlation coefficients >0.99. The LODs and LOQs were 0.04-2.7 and 0.2-8.0 μg/kg, respectively. The average recoveries at three spiked levels were in the range of 73.1%-125.2%, and the relative standard deviations were ≤14.8% (n=6). When the developed method was applied to detect illegally added chemicals in 60 functional milk powder samples, it detected benzoguanidine and sildenafil and successfully identified ethylphenidate, which is the structural analogue of an amphetamine. The proposed method is simple, sensitive, and accurate; thus, it may have practical application value for the daily supervision and law enforcement of milk powders with reported health functions.

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