广东省农业科学院机构知识库

Headspace solid-phase microextraction-gas chromatography-mass spectrometry analysis of the volatile components of longan (Dimocarpus longan Lour.)

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文献类型：外文期刊

作者： Zhang, Yi ¹ ; Gao, Bei ¹ ; Zhang, Mingwei ¹ ; Shi, John ³ ; Xu, Yujuan ² ;

作者机构： 1.Huazhong Agr Univ, Coll Food Sci & Technol, Wuhan 430070, Peoples R China

2.Guangdong Acad Agr Sci, Agrobiotech Res Inst, Guangzhou 510610, Guangdong, Peoples R China

3.Agr & Agri Food Canada, Guleph Food Res Ctr, Guelph, ON N1G 5C9, Canada

关键词： Longan;HS-SPME;GC-MS;Volatile components;Optimization;Central composite design

期刊名称：EUROPEAN FOOD RESEARCH AND TECHNOLOGY （影响因子：2.998；五年影响因子：3.005 ）

ISSN： 1438-2377

年卷期： 2009 年 229 卷 3 期

页码：

收录情况： SCI

摘要： Headspace solid-phase microextraction has been applied to the analysis of volatile components in longan (Dimocarpus longan Lour.). Silica fiber coated with divinylbenzene-carboxen-polydimethylsiloxane (DVB-CAR-PDMS) was found to be more efficient for extracting volatile compounds than other fibers such as those coated with polydimethylsiloxane, polyacrylate, and carboxen-polydimethylsiloxane. The SPME headspace volatiles were collected using a DVB-CAR-PDMS fiber. The extraction conditions were optimized using a response surface experimental design to analyze the effect of three factors: extraction temperature, equilibrium time and extraction time. The best response was obtained when the extraction temperature was around 50 A degrees C, equilibrium time near 15 min and extraction time close to 25 min. The volatile extracts were analyzed using gas chromatograph/mass spectrometry system. Sixty-two different volatile compounds from the headspace of longan were identified. The linearity was good in the considered concentration ranges (R (2) a parts per thousand yen 0.965). Average recoveries ranged from 78.6 to 108.3% and showed good accuracy. The precision of the present method was found to be good (RSD < 13.59%). Therefore, this method can be applied to determine the quality of the longan or other volatile components as an alternative to full-scale headspace analysis.

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