Detection of adulteration of sesame and peanut oils via volatiles by GC × GC–TOF/MS coupled with principal components analysis and cluster analysis

Abstract

AbstractThe method of headspace coupled with comprehensive two‐dimensional GC–time‐of‐flight MS (HS‐GC × GC–TOF/MS) was applied to differentiate the volatile flavor compounds of three types of pure vegetable oils (sesame oils, peanut oils, and soybean oils) and two types of adulterated oils (sesame oils and peanut oils adulterated with soybean oils). Thirty common volatiles, 14 particular flavors and two particular flavors were identified from the three types of pure oils, from the sesame oils, and from the soybean oils, respectively. Thirty‐one potential markers (variables), which are crucial to the forming of different vegetable oil flavors, were selected from volatiles in different pure and adulterated oils, and they were analyzed using the principal component analysis (PCA) and cluster analysis (CA) approaches. The samples of three types of pure vegetable oil were completely classified using the PCA and CA. In addition, minimum adulteration levels of 5 and 10% can be differentiated in the adulteration of peanut oils and sesame oils with soybean oils, respectively.Practical applications: The objective was to develop one kind of potential differentiated method to distinguish high cost vegetable oils from lower grade and cheaper oils of poorer quality such as soybean oils. The test result in this article is satisfactory in discriminating adulterated oils from pure vegetable oils, and the test method is proved to be effective in analyzing different compounds. Furthermore, the method can also be used to detect other adulterants such as hazelnut oil and rapeseed oil. The method is an important technical support for public health against profit‐driven illegal activities.