Objective To establish a new method for the sesame oil adulteration. Methods For the purpose of the authentication of sorts which were adulterated into sesame oil, 40 sesame oil samples adulterated with different ratio of soybean oil, maize oil and palm oil were detected by near infrared instrument and principal component analysis were used to cluster analysis. The mass data possessed a multitude of flavor compo-nents obtained from a series of samples containing different proportion three-level corn oil adulterated sesame oil were investigated and statistical evaluated using SPME-GC/MS and MassHunter software. Results The results showed that the characteristics of the marker was more sensitive analyzed from complex and overlapped matrix utilized deconvolution software and Agilent Mass Profiler Professional data statistical software. The marker was determined to distinguish the pure sesame oils with the sesame oils adulterated with maize oils using factor change analysis (FC=5) and variance analysis (P=0.05). Based on the characteristics of marker, the real sesame oils and adulterated sesame oils were classified through principal component analysis. Conclusion Experiments proved that statistical analysis of mass spectrometry data for flavor characteristics of markers could be used in the identification of sesame oil adulteration.
标题：基于GC-MS和MassHunter统计方法的芝麻油掺伪识别
英文标题：Applied research on gas chromatography-mass spectrometry and MassHunter data mining and statistic software for sesame oil adulteration recognition
