电子鼻结合顶空SPME-GC-MS分析毛竹冬笋的挥发性成分

摘要

为研究毛竹冬笋的主要挥发性成分,以自然生长毛竹冬笋(CK)和施肥处理毛竹冬笋(EG)为研究对象,利用电子鼻结合顶空固相微萃取-气相色谱-质谱联用法(Headspace solid-phase microextraction-Gas chromatogra-phy-mass spectrometry,SPME-GC-MS)对其挥发性成分进行检测与分析.通过对毛竹冬笋样品进行GC-MS检测、主成分分析(Principal component analysis,PCA)以及线性判别分析(Linear discriminant analysis,LDA)可知,毛竹冬笋的挥发性成分主要是柏木醇、肉豆蔻醛、1,3-二羟基丙酮、5-羟甲基糠醛和2-羟基-丁酸酮. PCA和LDA主成分贡献率分别为99. 336%、93. 719%,均高于85. 000%,说明电子鼻传感器的识别效应较高,并且样品之间的挥发性成分区分效果较好. SPME-GC-MS分析结果表明,2种样品均鉴定出34种成分,均是醇类、醛类、酮类的相对含量较高,并且CK高于EG,与由电子鼻检测得出的电阻比结果以及PCA和LDA分析结果基本吻合.说明,电子鼻能够区分CK和EG的挥发性成分.%In order to research the main volatile compounds of winter moso bamboo shoots, electronic nose (E-nose) and solid phase microextraction-gas chromatography-mass spectrometry(SPME-GC-MS) were used to detect and analyze the volatile components of two different types of winter moso bamboo shoots from natural growth (CK) and fertilization (EG) treatment. The main volatile components of winter moso bamboo shoots were cefrarolol, myristylaldehyde, 1,3-dihydroxyac-etone, 5-hydroxymethylfurfural, and 2-hydroxy-butyrophenone by using GC-MS, principal component analysis (PCA) and linear discriminant analysis (LDA). The principal component contributions of PCA and LDA were 99.336% and 93.719%, respectively, suggesting that the electronic nose sensor had a higher recognition effect, and a good separation of volatile components between samples. The results of SPME-GC-MS analysis showed that 34 components were identified in both samples, the levels of alcohols, aldehydes and ketones were relatively high, and these in CK were higher than those in EG treatment, which were basically consistent with the results of the resistance ratio obtained by electron-ic nose and the analysis results of PCA and LDA. It can be seen that the electronic nose can distinguish the volatile components in CK and EG.