Using contrasts as data pretreatment method in pattern recognition of multivariate data

Abstract A contrast method originally proposed by Spiegelman [C.H. Spiegelman, Calibration: a look at the mix of theory, methods and experimental data, presented at Compana '95, Wuerzburg, Germany.] is modified to pretreat multivariate data for classification. Three NIR data sets and one pollution data set are used as examples. Our results show that the contrast method greatly improves the ratios of between- to within-class variance. It is more powerful than offset correction, SNV, first- and second-derivative methods in the cases studied. This conclusion does not depend on the type of classifier used. Regularised discriminant analysis (RDA) and partial least squares (PLS2) with univariate feature selection based on Fisher's ratio were applied here. There is a risk that chance correlations occur after the contrast pretreatment. The chance correlation decreases after first eliminating un-informative variables using the modified Uninformative Variable Elimination (UVE)-PLS method.

[1]  Desire L. Massart,et al.  Artificial neural networks in classification of NIR spectral data: Design of the training set , 1996 .

[2]  Desire L. Massart,et al.  Feature selection using the Kalman filter for classification of multivariate data , 1996 .

[3]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[4]  R. Sanderson,et al.  The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra , 1994 .

[5]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[6]  Paul H. Garthwaite,et al.  Transformations to reduce the effect of particle size in near-infrared spectra , 1988 .

[7]  Beata Walczak,et al.  Spectral transformation and wavelength selection in near-infrared spectra classification , 1995 .

[8]  Johann Gasteiger,et al.  Neural Networks for Chemists: An Introduction , 1993 .

[9]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[10]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[11]  D. Massart,et al.  Feature selection for the discrimination between pollution types with partial least squares modelling , 1996 .

[12]  R. J. Barnes,et al.  Correction to the Description of Standard Normal Variate (SNV) and De-Trend (DT) Transformations in Practical Spectroscopy with Applications in Food and Beverage Analysis—2nd Edition , 1993 .

[13]  Desire L. Massart,et al.  Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data , 1996 .

[14]  Desire L. Massart,et al.  The robust normal variate transform for pattern recognition with near-infrared data , 1999 .