摘要
目的:为准确测定油茶籽油中的β-谷甾醇、菜籽甾醇、菜油甾醇含量。方法:建立超高效液相色谱—四极杆/飞行时间质谱测定方法。样品以6-酮胆甾烷醇为内标物,50 mL乙醇加热回流提取30 min,离心分层后,样液经Xbridge BEH C_(18)(100 mm×4.6 mm,2.5μm)色谱柱洗脱分离,大气压化学电离源(APCI)电离和正离子模式分析测试。结果:3种甾醇在0.1~10.0 mg/L的质量浓度范围内具有良好的线性关系,相关系数R^(2)均大于0.9992,本底值0.5,1.0,2.0,10.0倍添加水平的回收率为84.5%~99.5%,相对标准偏差(RSDs)为3.92%~7.44%,方法检出限均为1.0 mg/kg,方法定量限为3.0 mg/kg。结论:该方法操作简单、溶剂消耗少且环保、回收率及重现性好,可用于准确测定油茶籽油中的3种甾醇。
Objective:In this study,ultra performance liquid chromatography-high resolution quadrupole time of flight mass spectrometer(UPLC-Q-TOF/MS)was established to determineβ-sitosterol,brassicasterol,and campesterol in camellia oleifera seed oil.Methods:Using 6-ketocholestanol as the internal standard and performing sample extraction by heating it for 30 min and refluxing it with 50 mL ethanol.After centrifugation and layering,three sterols were separated by Xbridge BEH C_(18)(100 mm×4.6 mm,2.5μm)Chromatographic column elution.Finally,ionized by atmospheric pressure chemical ionization source and analyzed by positive ion mode.Results:The internal standard curves of the three sterols showed an observable linear relationship in the range of 0.1~10.0 mg/L,while the correlation coefficient R^(2) was both greater than 0.9992.The average recoveries at 0.5,1.0,2.0,and 10.0 times of background value were in the range of 84.5%~99.5%.The relative standard deviation(RSDs)was ranged from 3.92%to 7.44%.Moreover,the limits of detection were 1.0 mg/kg and the limits of quantitation were 3.0 mg/kg,respectively.Conclusion:The method was simple in operation,low in solvent consumption,and toxicity.It had good recoveries and reproducibility that could be applied for the accurate determination of three sterols in camellia oleifera seed oil.
作者
潘锦华
陈峰
白建平
段雪菲
李绮淇
PAN Jin-hua;CHEN Feng;BAI Jian-ping;DUAN Xue-fei;LI Qi-qi(Guangzhou Inspection and Testing Certification Group Co.,Guangzhou,Guangdong 511447,China;National Quality Testing Center for Processed Food〔Guangdong〕,Guangzhou,Guangdong 511447,China)
出处
《食品与机械》
CSCD
北大核心
2023年第7期56-61,共6页
Food and Machinery
基金
广州市市场监督管理局科技项目(编号:2022kj51)。
