基于GC-MS和MassHunter统计方法的芝麻油掺伪识别

Applied research on gas chromatography-mass spectrometry and Mass Hunter data mining and statistic software for sesame oil adulteration recognition

目的提出了一种用于芝麻油掺伪识别的新方法。方法为实现芝麻油中掺伪的识别,对掺入不同比例的大豆油、玉米油、棕榈油的芝麻油的40个样品进行近红外分析,并且基于主成分分析对掺入油进行识别;应用固相微萃取-气相色谱/质谱联用仪(SPME-GC/MS)和Mass Hunter软件,对于玉米油掺伪芝麻油的风味质谱数据进行了研究。结果 Mass Hunter解卷积软件和Agilent Mass Profiler Professional数据统计软件更加灵敏地从复杂包埋的目标物剖析分离得到了独特的标记物。倍率变化(FC=5)分析和ANOVA(P=0.05)分析的结果以火山曲线表示,确定区分芝麻油和玉米油的独特特征标记物。基于此独特的标记物,通过主成分分析,可对纯芝麻油和掺伪芝麻油进行分类。结论试验证明通过统计分析风味质谱数据寻找特征标记物可解决芝麻油掺伪的识别问题。

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 components 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.