HS-SPME-GC-MS结合电子鼻对10个品系红松籽油挥发性物质分析比较  

Comparative Analysis of Volatile Profiles in Kernel Oils of Ten Korean Pine(Pinus koraiensis)Varieties by Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry and Electronic Nose

作　　者：王贺[1] 赵玉红[1,2] 杨凯 WANG He;ZHAO Yuhong;YANG Kai(School of Forestry,Northeast Forestry University,Harbin 150040,China;Key Laboratory of Forest Food Resources Utilization of Heilongjiang Province,Harbin 150040,China;Heilongjiang Academy of Forestry,Harbin 150081,China)

机构地区：[1]东北林业大学林学院,黑龙江哈尔滨150040 [2]黑龙江省森林食品资源利用重点实验室,黑龙江哈尔滨150040 [3]黑龙江省林业科学研究院,黑龙江哈尔滨150081

出　　处：《食品科学》2021年第18期178-184,共7页Food Science

基　　金：“十三五”国家重点研发计划重点专项(2016YFC050030501);黑龙江省科技厅重点攻关项目(GC12B203)。

摘　　要：采用顶空固相微萃取与气相色谱-质谱(headspace solid phase microextraction-gas chromatography-mass spectrometry,HS-SPME-GC-MS)联用技术结合电子鼻(electronic nose,E-nose)对10个品系(种)红松籽油挥发性物质进行区别和比较。10个样品中GC-MS共鉴定163种挥发性物质,包含烃类、醇类、醛类、酯类、酮类、酸类等类型的挥发性物质,且以烃类、醛类、醇类和酯类为主,主要贡献风味的物质为醛类、醇类和酯类。通过聚类分析和主成分分析(principal component analysis,PCA)对10个红松籽油品系进行区分,可以将样品分为3组,各组之间的挥发性成分含量存在显著差异。采用PCA和线性判别分析处理E-nose数据,PC的累计贡献率分别达到97.17%、88.82%,说明传感器识别度高、样品间区分度好。2种技术相关性分析结果表明,信号传感器与不同挥发性物质存在相关性。本研究评价10个品系松籽油的挥发性物质,探讨HS-SPME-GC-MS与E-nose相结合用于区别和比较10个品系松仁油挥发性成分的可行性。The aim of this study was to analyze and discriminate the volatile profiles of the kernel oils of 10 Korean pine varieties by headspace-solid phase microextraction-gas chromatography-mass spectrometry(HS-SPME-GC-MS)and electronic nose(E-nose).Totally 163 volatile compounds were identified from the 10 oils,including hydrocarbons,alcohols,aldehydes,esters,ketones and acids,with hydrocarbons,aldehydes,alcohols and esters being the predominant ones.The main flavor contributors were aldehydes,alcohols and esters.By cluster analysis(CA)and principal component analysis(PCA),the pine nut oils could be divided into three groups.There were significant differences in volatile contents among these groups.When the E-nose data were processed by PCA and CA,the cumulative contribution rates of the first two principal components were 97.17%and 88.82%,respectively,indicating that the sensors had a high degree of recognition and good discrimination between the samples.Moreover,the signal sensors were correlated with different volatile components.This study confirmed the feasibility of applying HS-SPME-GC-MS combined with E-nose to analyze and discriminate the volatile profiles of the 10 pine nut oils.

关 键 词：红松籽油 顶空固相微萃取-气相色谱-质谱联用 电子鼻 挥发性风味成分 主成分分析 线性判别分析 

分 类 号：TS222[轻工技术与工程—粮食、油脂及植物蛋白工程]