320 - canonization chemometrics

Multivariate chemometric techniques have subsequently broadened the arsenal of tools that can be applied in QSAR. These include, among others. Multivariate ANOVA [9], Simplex optimization (Section 26.2.2), cluster analysis (Chapter 30) and various factor analytic methods such as principal components analysis (Chapter 31), discriminant analysis (Section 33.2.2) and canonical correlation analysis (Section 35.3). An advantage of multivariate methods is that they can be applied in... 

While principal components models are used mostly in an unsupervised or exploratory mode, models based on canonical variates are often applied in a supervisory way for the prediction of biological activities from chemical, physicochemical or other biological parameters. In this section we discuss briefly the methods of linear discriminant analysis (LDA) and canonical correlation analysis (CCA). Although there has been an early awareness of these methods in QSAR [7,50], they have not been widely accepted. More recently they have been superseded by the successful introduction of partial least squares analysis (PLS) in QSAR. Nevertheless, the early pattern recognition techniques have prepared the minds for the introduction of modem chemometric approaches. 

Chemometrics is a branch of science and technology dealing with the extraction of useful information from multidimensional measurement data using statistics and mathematics. It is applied in numerous scientific disciplines, including the analysis of food [313-315]. The most common techniques applied to multidimensional analysis include principal components analysis (PCA), factor analysis (FA), linear discriminant analysis (LDA), canonical discriminant function analysis (DA), cluster analysis (CA) and artificial neurone networks (ANN). 

The chemometric methods discussed above have found widespread applications in chromatography, and many theoretical and practical chromatographers have become familiar with these techniques and have applied them successfully. However, other less well-known methods have also found applicability in the analysis of chromatographic retention data. Thus, canonical variate analysis has been applied in pyrolysis GC-MS, artificial neural network for the prediction of GLC retention indices, and factor analysis for the study of the retention behavior of A-benzylideneaniline derivatives. 

A Negiz and A Cinar. PLS, balanced and canonical variate realization techniques for identifying varma models in state space. Chemometrics Intell. Lah. Sys., 38 209-221, 1997. 

To establish a correlation between the concentrations of different kinds of nucleosides in a complex metabolic system and normal or abnormal states of human bodies, computer-aided pattern recognition methods are required (15, 16). Different kinds of pattern recognition methods based on multivariate data analysis such as principal component analysis (PCA) (8), partial least squares (16), stepwise discriminant analysis, and canonical discriminant analysis (10, 11) have been reported. Linear discriminant analysis (17, 18) and cluster analysis were also investigated (19,20). Artificial neural network (ANN) is a branch of chemometrics that resolves regression or classification problems. The applications of ANN in separation science and chemistry have been reported widely (21-23). For pattern recognition analysis in clinical study, ANN was also proven to be a promising method (8). 

Factor Analysis. Several choices had to be made in preparing the data for factor analysis as well as in choosing criteria for selecting the number of factors needed to describe the data space (e.g. eigenvalue > 1.0, ratio adjacent eigenvalues > 2.0, etc.) and the number of factor scores to be used as input into the canonical correlation analysis. These choices may have affected subsequent interpretation of the multivariate spaces and evaluation of the chemometric analysis methods. Table II shows the types of spectral data input into factor analyses of the first 13 subfractions. 

D. Bertrand, P. Courcoux, J. -C. Autran, R. Meritan. Stepwise canonical discriminant analysis of continuous digitalized signals application to chromatograms of wheat proteins. J Chemometrics 4 413-428, 1990. 

N0rgaard L, Bro R, Westad F, Engelsen SB. A modification of canonical variates analysis to handle highly coUinear multivariate data. J Chemometr 2006 20 425. 

ECVA [41] is a recent chemometric classification tool representing a new approach for grouping samples based on the standard Canonical Variates Analysis, but with an underlying PLS engine. It is able to cope with several different classes yielding powerful separations. As with PLS-DA, it is vital with a good validation as to avoid overfitting. 

Nprgaard L, Soletormos G, Harrit N, Albiechtsen M, Olsen O, Nielsen D, et al. Fluorescence spectroscopy and chemometrics for classification of breast cancer samples—a feasibility study using extended canonical variates analysis. J Chemom 2(X)7 21 451-8. 

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