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2012 , Vol. 70 >Issue 08: 995 - 1000

DOI: https://doi.org/10.6023/A1110193

研究论文

傅里叶变换红外光谱结合模式识别法快速鉴别食用油的真伪

  • 刘玲玲 ,
  • 武彦文 ,
  • 张旭 ,
  • 欧阳杰 ,
  • 李冰宁 ,
  • 侯敏 ,
  • 陈舜琮
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  • a 北京林业大学生物科学与技术学院食品科学与工程系 北京 100083;
    b 北京市理化分析测试中心 北京市食品安全分析测试工程技术研究中心 北京 100089

收稿日期: 2011-10-19

  修回日期: 2012-01-18

  网络出版日期: 2012-02-06

基金资助

北京市自然科学基金(No. 7102021)资助项目.

收起

Application of Fourier Transform Infrared Spectroscopy Combined with Pattern Recognition Method for Rapid Authentication of Edible Oil

  • Liu Lingling ,
  • Wu Yanwen ,
  • Zhang Xu ,
  • Ouyang Jie ,
  • Li Bingning ,
  • Hou Min ,
  • Chen Shuncong
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  • a Department of Food Science and Engineering, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083;
    b Beijing Engineering Research Center of Food Safety Analysis, Beijing Center for Physical and Chemical Analysis, Beijing 100089

Received date: 2011-10-19

  Revised date: 2012-01-18

  Online published: 2012-02-06

Supported by

Project supported by the Natural Science Foundation of Beijing (No. 7102021).

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摘要

采用傅里叶变换红外光谱(FTIR)结合簇类独立软模式识别技术(SIMCA)建立了真伪食用油的快速鉴别方法. 该方法依据FTIR 的指纹特性, 收集并分析了53 个合格食用油和13 个伪造食用油的FTIR 谱图; 通过对谱图取二阶导数和标准化处理, 主成分分析(PCA)提取特征变量; 采用SIMCA 方法分别随机选取43 个合格食用油和9 个伪食用油样品的FTIR 谱图组成训练集, 构建得到真伪食用油的SIMCA 分类模型. 该模型经过剩余10 个合格食用油和4 个伪食用油的验证, 正确识别率达到了100%. 说明FTIR 结合SIMCA 可能成为快速鉴别食用油真伪的一种新方法.

关键词： 食用油; 真伪鉴别; 傅里叶变换红外光谱; 簇类独立软模式

本文引用格式

刘玲玲 , 武彦文 , 张旭 , 欧阳杰 , 李冰宁 , 侯敏 , 陈舜琮 . 傅里叶变换红外光谱结合模式识别法快速鉴别食用油的真伪[J]. 化学学报, 2012 , 70(08) : 995 -1000 . DOI: 10.6023/A1110193

Abstract

The rapidly analytical method for authenticity of edible oil was established by Fourier transform infrared spectroscopy (FTIR) combined with soft independent modeling of class analogy (SIMCA). Based on fingerprint characteristics of FTIR, the spectra of 53 qualified edible oils and 13 false edible oils were analyzed. After preprocessing these spectra data with second derivative and normalization, principal component analysis (PCA) was used to extract the characteristic variables in pattern recognition. Then, 43 qualified oils and 9 false oils were selected as training set to establish SIMCA classification model. And the model was validated by other 10 qualified oils and 4 false oils as validation set with the correct recognition rate of 100%. The results demonstrated that FTIR combined with chemometrics could be alternatively used to rapidly and simply determine the authenticity of edible oil.

Key words： edible oils; authentication; FTIR; SIMCA

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