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低场核磁技术检测芝麻油掺假
引用本文:邵小龙,张蓝月,冯所兰. 低场核磁技术检测芝麻油掺假[J]. 食品科学, 2014, 35(20): 110-113. DOI: 10.7506/spkx1002-6630-201420022
作者姓名:邵小龙  张蓝月  冯所兰
作者单位:1.南京财经大学食品科学与工程学院,江苏 南京 210023;2.粮食储运国家工程实验室,江苏 南京 210023
基金项目:国家自然科学基金青年科学基金项目,公益性行业(粮食)科研专项,江苏省属高校自然科学面上项目
摘    要:为评价低场核磁检测油脂掺假的能力,先用低场核磁结合主成分分方法区分大豆油和3 种芝麻油(分别为精炼、冷榨和热榨工艺)样品,然后用偏最小二乘法分析不同掺兑比例的模拟掺假样品数据。结果表明,大豆油和芝麻油样品的特征信号区域在0~900 ms弛豫时间段,低场核磁能够较好地区分芝麻油和大豆油样品;低场芝麻油中掺入大豆油的最低检测比例为体积分数5%~10%,精炼芝麻油中掺入冷榨或热榨芝麻油的最低检测比例为体积分数10%~20%。因此低场核磁技术可以作为油脂掺假的快速初筛检测方法之一。

关 键 词:低场核磁共振  油脂掺假  快速检测  偏最小二乘法  检出限  

Application of LF-NMR for Detection of Sesame Oil Adulteration
SHAO Xiao-long,ZHANG Lan-yue,FENG Suo-lan. Application of LF-NMR for Detection of Sesame Oil Adulteration[J]. Food Science, 2014, 35(20): 110-113. DOI: 10.7506/spkx1002-6630-201420022
Authors:SHAO Xiao-long  ZHANG Lan-yue  FENG Suo-lan
Affiliation:1. College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China;2. National Engineering Laboratory of Grain Storage and Transportation, Nanjing 210023, China
Abstract:To evaluate the ability of low field nuclear magnetic resonance (LF-NMR) in detecting oil adulteration, LF-NMR
and principal component analysis (PCA) were applied jointly to discriminate pure soybean oil from pure sesame oils (refined,
cold pressed and hot pressed), and partial least squares (PLS) regression was applied to analyze three sesame oils adulterated
with soybean oil and mixtures of two different sesame oils. The results showed that transverse relaxation time (0 to 900 ms)
was an important signal region for discrimination of oil samples. LF-NMR could completely distinguish four different pure
oil samples. The limit of detection (LOD) for sesame oil adulteration with soybean oil was 5% to 10%, and the LOD for
refined sesame oil adulteration with cold or hot pressed sesame oil was 10% to 20%. Therefore, LF-NMR can be used as a
rapid screening method to detect adulteration of oil.
Keywords:low field nuclear magnetic resonance  oil adulteration  rapid detection  partial least squares  detection limit
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