Classification and Prediction by LF NMR
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  • 作者：Xiaolong Shao (12)
    Yunfei Li (23) yfli@sjtu.edu.cn
  • 关键词：Food processing – ; Quality control – ; Classification – ; Prediction – ; PLS – ; Low field NMR
  • 刊名：Food and Bioprocess Technology
  • 出版年：2012
  • 出版时间：July 2012
  • 年：2012
  • 卷：5
  • 期：5
  • 页码：1817-1823
  • 全文大小：291.9 KB
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  • 作者单位：1. Institute of Refrigeration and Cryogenic Engineering, School of Power and Energy Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China2. Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, 200240 Shanghai, China3. Bor.S.Luh Food Safety Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, 200240 Shanghai, China
  • 刊物类别：Chemistry and Materials Science
  • 刊物主题：Chemistry
    Food Science
    Chemistry
    Agriculture
    Biotechnology
  • 出版者：Springer New York
  • ISSN：1935-5149
文摘
The classification analysis and firmness prediction of sweet corn subjected to different treatments were investigated to develop the potential application of low field nuclear magnetic resonance for food quality control. Principal component analysis (PCA) was firstly used for exploiting the invisible changes of the internal characteristics of blanched sweet corn. Then, two classification methods involving regression analysis in combination with linear discriminant analysis (LDA), which is based on data compression technology (PCA and partial least squares, PLS), were compared. The PCA score plots clearly showed that the samples varied according to the temperature of the treatment. For the goal of classification, the principal components extracted from the PLS analysis were more useful than those obtained from PCA. Cross validation was helpful to determine the appropriate number of principal components. In fact, it proved that PLS combined with LDA yielded the highest success classification rate (94.3%), and PLS also performed well in firmness prediction of processed sweet corn.

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