Determination of the number of components in mixtures using a new approach incorporating chemical information

A novel index RESO, based on the ratio of eigenvalues calculated by smoothed PCA and those calculated by ordinary PCA, was developed for determining the number of compounds in mixtures. The proposed method distinguishes itself from other methods by incorporating chemical information in its framework. Its outstanding features, such as the capability of handling situations with existence of minor components or severe collinearity in spectra and confronting heteroscedastic noise to some extent, make RESO more powerful than many other indices reported so far. The results obtained from simulated and actual data sets by the proposed method show that it can deal with more complex situations than many other methods can. Copyright © 1999 John Wiley & Sons, Ltd.

[1]  G. Guiochon,et al.  Statistical approach for estimating the total number of components in complex mixtures from nontotally resolved chromatograms , 1984 .

[2]  Jian-hui Jiang,et al.  Chemical rank estimation for excitation—emission matrices using a morphological approach , 1998 .

[3]  Edmund R. Malinowski,et al.  Determination of the number of factors and the experimental error in a data matrix , 1977 .

[4]  Yizeng Liang,et al.  White, grey and black multicomponent systems: A classification of mixture problems and methods for their quantitative analysis , 1993 .

[5]  B. Silverman,et al.  Smoothed functional principal components analysis by choice of norm , 1996 .

[6]  I. Warner,et al.  Rank estimation of excitation-emission matrices using frequency analysis of eigenvectors. , 1986, Analytical chemistry.

[7]  Richard I. Shrager,et al.  Titration of individual components in a mixture with resolution of difference spectra, pKs, and redox transitions , 1982 .

[8]  Edmund R. Malinowski,et al.  Theory of error in factor analysis , 1977 .

[9]  J. Kankare Computation of equilibrium constants for multicomponent systems from spectrophotometric data , 1970 .

[10]  Edmund R. Malinowski,et al.  Statistical F‐tests for abstract factor analysis and target testing , 1989 .

[11]  S. Wold Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[12]  Hai-Long Wu,et al.  An alternating trilinear decomposition algorithm with application to calibration of HPLC–DAD for simultaneous determination of overlapped chlorinated aromatic hydrocarbons , 1998 .

[13]  H. Woodruff,et al.  Factor analysis of mass spectra from partially resolved chromatographic peaks using simulated data , 1981 .