FOOD SCIENCE ›› 2021, Vol. 42 ›› Issue (18): 178-184.doi: 10.7506/spkx1002-6630-20200831-416

• Component Analysis • Previous Articles     Next Articles

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

WANG He, ZHAO Yuhong, YANG Kai   

  1. (1. School of Forestry, Northeast Forestry University, Harbin 150040, China;2. Key Laboratory of Forest Food Resources Utilization of Heilongjiang Province, Harbin 150040, China;3. Heilongjiang Academy of Forestry, Harbin 150081, China)
  • Published:2021-09-29

Abstract: 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.

Key words: pine nut oil; headspace-solid phase microextraction-gas chromatography-mass spectrometry; electronic nose; volatile components; principal component analysis; linear discriminant analysis

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