Chem. J. Chinese Universities ›› 1999, Vol. 20 ›› Issue (S1): 26.
• Atomic Spectrometry • Previous Articles Next Articles
GAN Lu, LUO Li-Qiang, WU Xiao-Jun
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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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GAN Lu, LUO Li-Qiang, WU Xiao-Jun. Classification and Prediction of Geological Samples by Principal Component Analysis[J]. Chem. J. Chinese Universities, 1999, 20(S1): 26.
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