高等学校化学学报

高等学校化学学报 ›› 1999, Vol. 20 ›› Issue (S1): 26.

• Atomic Spectrometry • 上一篇    下一篇

Classification and Prediction of Geological Samples by Principal Component Analysis

GAN Lu, LUO Li-Qiang, WU Xiao-Jun   

  1. Institute of Rock and Mineral Analysis, Chinese Academy of Geological Sciences, Beijing, 100037, P. R. China
  • 出版日期:1999-12-31 发布日期:1999-12-31

Classification and Prediction of Geological Samples by Principal Component Analysis

GAN Lu, LUO Li-Qiang, WU Xiao-Jun   

  1. Institute of Rock and Mineral Analysis, Chinese Academy of Geological Sciences, Beijing, 100037, P. R. China
  • Online:1999-12-31 Published:1999-12-31

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摘要：

The principal component analysis is an ancient multivariate statistical method[1]. It is extensively used in spectrometry with the popularization of computer and development of the method of chemometrics. It is regarded as an effective method of multivariate statistical analysis. The principal component analysis is universally included in common program package of multivariate statistical analysis. The method, as well as other multivariate calibration methods, combined with artificial neural networks forms the foundation of the chemometrics.

Abstract:

The principal component analysis is an ancient multivariate statistical method[1]. It is extensively used in spectrometry with the popularization of computer and development of the method of chemometrics. It is regarded as an effective method of multivariate statistical analysis. The principal component analysis is universally included in common program package of multivariate statistical analysis. The method, as well as other multivariate calibration methods, combined with artificial neural networks forms the foundation of the chemometrics.

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