餐厨废油脂的脂肪酸指纹图谱构建及识别初探

Establishment and Discriminate of Fatty Acid Fingerprint from Waste Cooking Oil

摘要:

应用气相色谱-质谱联用仪测定各类食用植物油（花生油、菜籽油、棕榈油、食用调和油）、动物油脂（牛油、羊油、鸭油）和餐厨废油脂37种脂肪酸的含量，以这37种脂肪酸含量为指标构建不同类型油脂的指纹图谱数据库；同时，利用SAS统计分析系统软件对各类油脂的脂肪酸组成进行聚类分析和判别分析，聚类分析用Flexible-Beta Method进行分类，判别分析采用距离判别法，从而建立各类油脂的判别函数，通过此函数对给定的样品进行餐厨废油脂的鉴别。聚类分析的结果显示，油脂品种分类明确，运用此判别函数对23个盲样进行考核，所有普通食用油脂都能判断正确，此判别系统对于少量的餐厨废油脂掺伪和饱和脂肪酸含量高的油脂容易造成误判，准确率为91.3%，表明脂肪酸指纹谱图鉴别法是一种有效且可靠性高的鉴别餐厨废油脂的方法。

Abstract:

GC-MS was applied to analyze the contents of 37 fatty acids in vegetable oils (peanut oil, colza oil, palm oil and edible blend oil), animal oils (butter, mutton fat, duck grease) and catering waste oils respectively to establish a characteristic fingerprint data base for different types of vegetable oils．Meanwhile, discrimination (distance discriminant method) and cluster (Flexible-Beta Method) analysis were conducted to determine fatty acid compositions of the abovementioned oils l. The results showed that normal oils were well classified through the function except for a small amount of adulteration and oils with high saturated fatty acid content. Twenty-three blind samples were investigated and the accuracy was as high as 91.3%. Therefore, the established model could distinguish different kinds of oil correctly based on changes in content or proportion of fatty acids.