[关键词]
[摘要]
建立了网红减肥类食品中非法添加匹可硫酸钠检测的超高效液相色谱-串联质谱(UHPLC-MS/MS)方法。样品经甲醇水溶液-盐析辅助体系提取,Oasis PRiME HLB通过式固相萃取柱净化,C18色谱柱分离,流动相为乙腈-乙酸铵,采用梯度洗脱,电喷雾离子源正离子(ESI+)电离模式,多反应监测(MRM)进行扫描,外标法定量。该研究优化了提取体系,实现不同基质样品很好的分散,抗干扰效果好。采用该实验建立的方法,匹可硫酸钠在0.50~250.00 ng/mL范围内线性关系良好,相关系数(r2)大于0.998,方法检出限(S/N≥3)为10.00 μg/kg,定量限(S/N≥10)为25.00 μg/kg。在低(25.00 μg/kg)、中(50.00 μg/kg)、高(250.00 μg/kg)3个浓度水平下平均回收率在81.51%~104.53%之间,相对标准偏差相对标准偏差(RSD)在1.14%~5.64%之间。该方法简便、高效、准确性好、抗干扰能力强,适用于减肥类食品中非法添加匹可硫酸钠的准确定性、定量分析。
[Key word]
[Abstract]
An ultra high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS) method was developed for the determination of illegally added sodium picosulfate in the internet-famous weight-loss foods. The samples were extracted by a salting-out assisted methanol-water solution system, and purified by an Oasis PRiME HLB solid phase extraction column with a pass-through cleanup strategy. The separation was carried out on a C18 column using acetonitrile ammonium acetate as the mobile phase in a gradient elution mode. Eletrospray ionization source with positive ion (ESI+) ionization mode, multiple reaction monitoring (MRM) scanning and external standard quantification method were used. In this study, the extraction system was optimized to achieve good dispersion of different matrix samples and good anti interference effect. The established method showed a linear relationship in the picosulfate concentration range of 0.50~250.00 ng/mL, with the correlation coefficients higher than 0.998, and the limit of detection (LOD) and the limit of quantitation (LOQ) being 10.00 μg/kg and 25.00 μg/kg, respectively. At low (25.00 μg/kg), medium (50.00 μg/kg) and high (250.00 μg/kg) concentrations, the average recoveries ranged from 81.51% to 104.53%, with the relative standard deviation (RSD) ranging from 1.14% to 5.64%. This method is somple, efficient and accurate with strong anti-interference, thus is suitable for the qualitative and quantitative analyses of sodium picosulfate illegally added to weight-loss foods.
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[基金项目]
国家市场监督管理总局科研计划项目(2022MK072)