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作 者:张宁[1,2] 陈海涛[3] 孙宝国[1] 毛学英[2] 张玉玉[4] Zhang Ning;Chen Haitao;Sun Baoguo;Mao Xueying;Zhang Yuyu(Beijing Advanced Innovation Center for Food Nutrition and Human Health,Beijing Technology & Business University(BTBU),Beijing 100048;College of Food Science & Nutritional Engineering,China Agricultural University,Beijing 100083;Beijing Key Laboratory of Flavor Chemistry,Beijing Technology and Business University(BTBU),Beijing 100048;Beijing Laboratory for Food Quality and Safety,Beijing Technology & Business University(BTBU),Beijing 100048)
机构地区:[1]北京食品营养与人类健康高精尖创新中心(北京工商大学),北京100048 [2]中国农业大学食品科学与营养工程学院,北京100083 [3]北京市食品风味化学重点实验室(北京工商大学),北京100048 [4]食品质量与安全北京实验室(北京工商大学),北京100048
出 处:《中国食品学报》2018年第6期307-316,共10页Journal of Chinese Institute Of Food Science and Technology
基 金:"十二五"国家科技支撑计划项目(2014BAD04B06)
摘 要:采用固相微萃取(SPME)结合气相色谱-质谱联用技术(GC-MS),对比分析传统烹饪与市售软包装的番茄牛腩菜肴的风味成分。对两种样品进行气-质分析,选取两种极性的色谱柱(DB-WAX,DB-5)对样品进行分离。综合两种样品在不同色谱柱上的分离结果:从两种样品中共检出挥发性成分94种,其中从传统烹饪菜肴共检出风味成分65种,软罐头中共检出52种。从两样品同时检到的风味成分共23种。从检出化合物数量上看,传统烹饪与软罐头番茄牛腩中的醇类、醛类、烃类和含硫/氮类化合物差异明显;从所检出的化合物相对含量上看,二者的酚/醚类、醛类、酮类与含硫/氮类化合物差异明显。This study presents the application of solid-phase microextraction(SPME) method on the comparative analysis of volatile compounds in Braised Beef Brisket with Tomato between Chinese traditional cooked method and soft canned product by using gas chromatography-mass spectrometry(GC/MS) with two different polar columns(DB-WAX,DB-5). The results showed that a total of 94 compounds were identified in the two samples of different processing methods while 65 compounds in traditional cooked dishes and 52 compounds in soft canned products. Besides, 23 compounds were all identified in the two samples. According to the quantity of the identified compounds, an obvious difference existed in alcohols, aldehydes, hydrocarbons and sulfur-containing/nitrogen-containing compounds between traditional cooked dishes and soft canned products; from the aspect of relative contents, phenols/ethers, aldehydes, ketones and sulfur-containing/nitrogen-containing compounds displayed a clear distinction between the two samples.
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