本文建立了一种利用元素分析-同位素比质谱法（Elemental analyzer-Isotope ratio mass spectrometry，EA-IRMS）测定食醋总碳同位素比值的方法，并对食醋进行聚类分析。优化了样品稀释倍数及进样量。2 g芳香醋用4 mL超纯水进行稀释，2 g糯米酿造食醋用2 mL超纯水进行稀释，苹果醋、配制白醋不稀释，进样量均为1 μL，样品分析时间为670 s。结果显示：芳香醋、糯米酿造食醋、苹果醋及配制白醋的δ13C值范围分别为-26.94‰~-23.46‰、-25.06‰~-22.40‰、-27.99‰~-22.76‰、-19.06‰~-13.74‰，聚类分析可将不同产地、不同原料、不同生产方式的食醋进行初步归类，并且该方法可鉴别出芳香醋和掺入≥4%糯米酿造食醋、≥6%苹果醋、≥2%配制白醋的芳香醋混合样品。食醋及质控样品（咖啡因）的测试精度（SD）在0.02‰~0.25‰（n=6）之间，均小于0.3‰，满足方法检测分析要求。结果表明，所建EA-IRMS方法结合聚类分析可用于食醋分类及掺假鉴别。

A method was established for determining the carbon isotope ratio of vinegars by elemental analysis-isotope ratio mass spectrometry (EA-IRMS), and cluster analysis was conducted on the vinegar samples. The sample dilution factor and injection volume were optimized; 2 g of balsamic vinegar was diluted in 4 mL of ultrapure water, 2 g of vinegar made from sticky rice was diluted with 2 mL of ultrapure water, and apple vinegar and blended white vinegar were not diluted. The sample injection volume was 1 μL and the sample analysis time was 670 s. The results indicated that the δ13C values of the balsamic vinegar, sticky rice vinegar, apple vinegar, and blended white vinegar were in the ranges of –(26.94-23.46%), -(25.06-22.40%), -(27.99-22.76%) and -(19.06-13.74%), respectively. The vinegars produced from different regions, with different raw materials, and different modes were preliminarily classified by cluster analysis, and this method could distinguish between pure balsamic vinegar samples and balsamic vinegar samples blended with ≥4% sticky rice fermented vinegar, ≥6% apple vinegar, and ≥2% blended white vinegar. The precision (standard deviation, SD) of vinegars and caffeine (quality control sample) were between 0.02% and 0.25% (n = 6), and were all less than 0.3%. This method can meet the requirements for determination. The results show that the established EA-IRMS method combined with cluster analysis can be applied for classification of vinegars and detection of adulterations in vinegar.

上海出入境检验检疫局科技专项基金（HK007-2015）；上海出入境检验检疫局科技专项基金（HK004-2016）；上海市科委科技专项基金（15395810100）；安徽省科技专项基金（1604b0602028）