以烟台、新疆、宁夏与北京四大产区32款赤霞珠葡萄酒作为实验样本,采用搅拌棒吸附萃取(SBSE)与气相色谱-质谱联(GC-MS)、液液微萃取技术(LLME)与气相色谱-质谱联用(GC-MS)定量分析了葡萄酒中59种挥发性香气成分。定量数据运用多维统计方法进行分析,通过方差分析(ANOVA)与主成分分析(PCA)选出13种代表性化合物作为葡萄酒产地判别的自变量(4-萜品醇、1-壬醇、2-苯乙醇、乙酸2-苯乙酯、3-甲硫基-1-丙醇、苯甲酸、4-甲基苯酚、α-萜品醇、癸酸、辛酸乙酯、3-辛醇、2-壬醇、棕榈酸乙酯),用这13种物质进行判别分析(DA)并建立了赤霞珠葡萄酒的原产地鉴定模型。该模型对32款不同产区的葡萄酒进行了正确的分类,交叉验证的结果显示,对未知样品归类的准确率到达100%,充分肯定了此模型对赤霞珠葡萄酒原产地鉴别的能力。

Thirty-two Cabernet Sauvignon wines from four different regions(Yantai,Xinjiang,Ningxia and Beijing) were analyzed using stir bar sorptive extraction(SBSE) in combination with gas chromatography-mass spectrometry(GC-MS) and liquid-liquid microextraction(LLME) coupled with gas chromatography-mass spectrometry(GC-MS),and 59 volatile compounds were quantified.Quantitative data were processed by multivariate data analysis,13 representative compounds(4-terpineol,1-nonanol,2-phenylethyl alcohol,2-phenylethyl acetate,3-methylthio-1-propanol,benzoic acid,4-methylphenol,α-terpineol,decanoic acid,ethyl octanoate,3-octanol,2-nonanol,ethyl hexadecanoate) were identified as potential predictors for the different geographical origins through analysis of variance(ANOVA) and principal component analysis(PCA).Then the geographical origin identification model of Cabernet Sauvignon wines was established using discriminant analysis(DA).In this model all the wines were successfully classified according to the geographical origins.Moreover,in order to evaluate the recognition ability of the model,cross validation was done and the results showed that the prediction accuracy for all the unknown wine samples reached 100%.