Principal component analysis , pattern recognition technique

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 More recently they have been superseded by the successful introduction of partial least squares analysis (PLS) in QSAR.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. 

These applications demonstrate that pattern recognition techniques based on principal components may be effectively used to character zate complex environmental residues. In comparisons of PCBs in bird eggs collected from different regions, we demonstrated through the use of SIHCA that the profiles in samples from a relatively clean area differed in concentration and composition from profiles in samples from a more highly contaminated region. Quality control can be evaluated by the proximity of replicate analysis of samples in principal components plots. 

SIMCA method relies on a pattern-recognition technique called principal component analysis (PCA). 

The data processing of the multivariate output data generated by the gas sensor array signals represents another essential part of the electronic nose concept. The statistical techniques used are based on commercial or specially designed software using pattern recognition routines like principal component analysis (PCA), cluster analysis (CA), partial least squares (PLSs) and linear discriminant analysis (LDA). 

Principal component analysis and Kohonen self-organizing maps allow multivariate data to be displayed as a graph for direct viewing, thereby extending the ability of human pattern recognition to uncover obscure relationships in complex data sets. This enables the scientist or engineer to play an even more interactive role in the data analysis. Clearly, these two techniques can be very useful when an investigator believes that distinct class differences exist in a collection of samples but is not sure about the nature of the classes. 

There are several books on pattern recognition and multivariate analysis. An introduction to several of the main techniques is provided in an edited book [19]. For more statistical in-depth descriptions of principal components analysis, books by Joliffe [20] and Mardia and co-authors [21] should be read. An early but still valuable book by Massart and Kaufmann covers more than just its title theme cluster analysis [22] and provides clear introductory material. 

Principal components analysis (PGA), a multivariate statistical technique, was used for data reduction and pattern recognition [8]. PGA represents the variation present in many variables using a small number of principal components (PCs). PCA functions by finding a new set of axes, which more efficiently describe the variance in the data. Samples are no longer described by their intensities in... 

In the literature, a large number of substituent descriptors have been reported. In order to use this information for substituent selection, appropriate statistical methods may be used. Pattern recognition or data reduction techniques, such as principal component analysis (PCA) or cluster analysis (CA) are good choices. As explained in Section V in more detail, PCA consists of condensing the information in a data table into a few new descriptors made of linear combinations of the original ones. These new descriptors are called principal components or latent variables. This technique has been applied to define new descriptors for amino acids, as well as for aromatic or aliphatic substituents, which are called principal properties (PPs). The principal properties can be used in factorial design methods or as variables in QSAR analysis. 

Chemometrical techniques, particularly Principal Component Analysis (PCA) are very well suited for the evaluation of data on multicomponent mixtures such as PCBs, TCDDs, TCDFs, or PAHs. The reduction of the dimensionality of the data leads to two-dimensional or three-dimensional projections which facilitate pattern recognition and interpretation of the results. PCA is used mainly for pattern recognition such as the distinction between control and... 

While detection of cations via porphyrin-ba.sed materials has been explored less than anion sensing, the ability of a porphyrin to coordinate different metals and the unique spectral signatures that result form the basis for metal ion detection. Use of free-base porphyrins in polymer matrices has allowed for the detection of heavy metal ions by Ache et Immobilization of 5,10,15,20-tetrakis(4-N-methylpyridyOporphyrin on Nafion membranes pennitted detection of cadmium and mercury in solution with detection limits of 5 X 10 M and 2 x 10 M, respectively over a 20-minute measuring period. The method is subject to interferences from other metal ions, but the researchers were able to detect several ions simultaneously using pattern-recognition techniques such as principal component analysis. Sol-gel films doped with 5,l0,l5,20-tetra(p-sulfonatophenyDporphyrin have also been used by Ache and coworkers for the fluorimetric determination of mercury in solution, with a detection limit of approximately 7 X 10... 

Acoustic emission power spectra are similar in many respects to optical spectra and are amenable to chemometric processing (multivariate analysis). Principal component analysis, partial least squares (PLS), neural networks, and qualitative techniques such as SIMCA (soft independent modeling of class analogy a pattern recognition technique) have been employed... 

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