摘要
目的建立不同黄酒的有机酸高效液相色谱指纹图谱,并结合化学计量学分析对黄酒进行鉴别研究。方法采用Diamonsil Plus C_(18)(150 mm×4.6 mm,5μm)色谱柱,以0.1%磷酸-甲醇(98:2,V:V)为流动相进行等度洗脱,流速为0.55 mL/min,柱温设定为30℃。在检测波长200及210 nm条件下检测不同黄酒中的有机酸;采用ChemPattern先进化学计量学系统解决方案软件对有机酸图谱进行多元统计分析及相似度阈值分析,建立模式识别,完成对不同黄酒的鉴别。结果4种不同类型黄酒有机酸指纹图谱共有模式包含36个峰。根据标准物质图谱信息指认8个共有峰,共有模式下共有峰的相对保留时间相对标准偏差(relative standard deviations,RSDs)小于1.5%,相对峰面积RSDs为26.3%~198.1%。多种分析方法的分类结果准确一致,证明了本方法用于不同黄酒鉴别的可行性。结论本方法简便易行、准确可靠,适用于共有模式内黄酒的鉴别评价。
Objective To establish organic acid fingerprints of different types of Huangjiu by high performance liquid chromatography,and identify it combined with chemometric analysis.Methods Diamonsil Plus C_(18)(150 mm×4.6 mm,5μm)was used as the chromatographic column,and 0.1% phosphoric acid water(A)-methanol(B)(98:2,V:V)was used as the mobile phase for isocratic elution at a flow rate of 0.55 mL/min,with the column temperature set at 30℃.The organic acids in different types of Huangjiu were detected at the wavelength of 200 and 210 nm.The organic acid fingerprints were analyzed by multivariate statistical analysis and similarity threshold analysis with ChemPattern advanced chemometrics system solution software,and the pattern recognition was established to complete the identification of different types of Huangjiu.Results There were 4 different types of Huangjiu and the total pattern of organic acid fingerprint contained 36 peaks.Eight common peaks were identified according to the map information of reference materials,under the common pattern,the relative standard deviations(RSDs)of relative retention times of common peaks were less than 1.5%,and the RSDs of relative peak area were between 26.3%-198.1%.The classification results of various analytical methods were accurate and consistent,which proved the feasibility of this method for the identification of different types of Huangjiu.Conclusion The proposed method is simple,accurate and reliable,which is suitable for the identification and evaluation of Huangjiu in common mode.
作者
张长勇
张雪婧
纪丽君
张玲
金琦
叶湖
ZHANG Chang-Yong;ZHANG Xue-Jing;JI Li-Jun;ZHANG Ling;JIN Qi;YE Hu(Suzhou Institute of Product Quality Supervision and Inspection,Suzhou 215104,China;Suzhou Institute for Food Control,Suzhou 215104,China)
出处
《食品安全质量检测学报》
CAS
北大核心
2022年第1期48-55,共8页
Journal of Food Safety and Quality
基金
江苏省市场监督管理局科技计划项目(KJ196038)。
