近赤外スペクトルの主成分分析による用途特性の異なる小麦粉の分類

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  • Principal Component Analysis(PCA) Applied to Near Infrared Spectra for Classifying Wheat Flours.
  • キンセキガイ スペクトル シュセイブン ブンセキ ニヨル ヨウト トクセイ ノ

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The method was developed to perform principal component analysis (PCA) using widely used NECTM PC on near infrared spectra taken by IBMTM PC. As the application of this method, wheat flours with different processing qualities were classified. Four kinds of commercially available wheat flours such as bread making four, Chinese noodle making flour, Japanese noodle making flour, and confectionery making flour were analyzed. The NIR spectra of 18 samples were recorded from 1100 to 2500nm and the dd2 log(1/R) at several informative wavelengths were selected as variables for PCA. Principal components were calculated using the 11 dd2 log(1/R) values at the wavelengths where standard deviation of dd2 log(1/R) were large and downward peaks of dd2 log(1/R) were observed. On the plane with axes of the first and the third principal components, four kinds of wheat flours were clearly classified. On examination of the eigenvector of the first and the third principal components, and the chemical constituents and physical properties of samples, it can be thought that the first principal component relates to sample particle sizes, and the third one relates starch contents, . As a result, it can be concluded that near infrared spectroscopy has a possibility to classify wheat flours with different processing qualities using PCA.

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