酱油挥发性成分固相微萃取条件的优化

刘非,杜丽平,肖冬光

食品与发酵工业 ›› 2017, Vol. 43 ›› Issue (7) : 70.

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PDF(1264 KB)
食品与发酵工业 ›› 2017, Vol. 43 ›› Issue (7) : 70.

酱油挥发性成分固相微萃取条件的优化

  • 刘非,杜丽平,肖冬光
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Optimization of extraction conditions for volatile compounds in soy sauce by solid-phase microextraction

  • LIU Fei,DU Li-ping,XIAO Dong-guang
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摘要

顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)是提取挥发性成分比较常用的方法.为了确定提取酱油挥发性成分的SPME萃取头,分别以挥发性成分的累积峰面积标准化值和峰面积相对标准偏差的平均值与标准偏差为指标,考察了不同型号萃取头的萃取灵敏度和重复性,确定采用65μm PDMS/DVB提取酱油挥发性成分;并以各类挥发性成分峰面积和总峰面积为指标,优化了其萃取条件,确定萃取条件为:样品用量8 mL,萃取温度60℃,萃取时间60 min.利用优化方法对酱油挥发性成分进行提取,经气相色谱-质谱联用(gas chromatography-mass spectrometry,GC-MS)分析,共鉴定出86种挥发性成分,其中最主要的为酯类物质,其次为醇类、醛类和酸类.挥发性成分峰面积的平均相对标准偏差为8.14%,方法重复性较好.

Abstract

Headspace solid-phase microextraction (HS-SPME) was commonly used for the extraction of volatile compounds.To determine the SPME fiber to extract volatile compounds in soy sauce,the sensitivity of different fibers were examined on the basis of cumulative area normalization value,and their repeatability were examined by the average value and standard deviation of relative standard deviation (RSD) of peak area.65μm PDMS/DVB was then selected to extract volatile compounds in soy sauce.By comparing the peak area of all kinds of volatiles and the total peak area,the extraction conditions were optimized,and the extraction conditions were established as follows:the volume of soy sauce sample was 8 mL,extraction temperature was 60 ℃C and extraction time was 60 min.Using the optimized method for the extraction of volatile compounds in soy sauce,a total of 86 compounds were identified by GCMS,wherein the main compounds were esters and followed by alcohols,aldehydes and acids.The average RSD of the peak area of compounds was 8.14%,which showed that the repeatability of the method was well.

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刘非,杜丽平,肖冬光. 酱油挥发性成分固相微萃取条件的优化[J]. 食品与发酵工业, 2017, 43(7): 70
LIU Fei,DU Li-ping,XIAO Dong-guang. Optimization of extraction conditions for volatile compounds in soy sauce by solid-phase microextraction[J]. Food and Fermentation Industries, 2017, 43(7): 70

基金

国家重点研发计划项目(2016YFD0400505)
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