Classification methods chemometric modelling

In a related paper Herrador and Gonzalez [144] described the application of PCA and CA and of two supervised techniques, LDA and back-propagated ANN on Al, Ba, Ca, Cu, K, Mg, Mn, and Zn data obtained from commercial Spanish tea samples. A minitorch ICP-AES instrument was used for the determinations. The characterization of three classes of tea was achieved. In a paper that expands previous research described in reference [47], trace metal concentrations measured by ICP-AES and ICP-MS were employed by Moreda-Pineiro et al. [145] for a more elaborated chemometric treatment on 85 samples of tea of Asian, African, commercial, and unknown origin. Seventeen elements (Al, Ba, Ca, Cd, Co, Cr, Cu, Cs, Mg, Mn, Ni, Pb, Rb, Sr, Ti, V, and Zn) were determined. In addition to the techniques employed in the already mentioned papers (PCA, CA, LDA), soft independent modeling (SIM) of class analogy was also applied. The latter method resulted in the totally correct (100 percent) classification of Chinese teas. 

Chemometrics is a most useful tool in QSAR and QSPR studies, in that it forms a firm base for data analysis and modelling and provides a battery of different methods. Moreover, a relevant aspect of the chemometric philosophy is the attention it pays to the predictive power of the models (estimated by using -> validation techniques), -> model complexity, and the continuous search for suitable parameters to assess the model qualities, such as -> classification parameters and -> regression parameters. Chemometrics includes several fields of mathematics and statistics as listed below. 

Gramatica, P., Navas, N. and Todeschini, R. (1999b). Classification of Organic Solvents and Modelling of Their Physico-Chemical Properties by Chemometric Methods Using Different Sets of Molecular Descriptors. TRAC, 18,461-471. 

To demonstrate the robnstness of Principal Component Analysis (PCA) classification and identification methods when applied to the IR spectra of different spore types, transmission FTIR spectral libraries were generated for three different spore strains (BG, BA, and BS) and classification models were developed based upon Mahalanobis Distance by PCA with Residnals (MD/PCA/R) statistical methods using PLSplns IQ (Thermo Electron) chemometric software. Figme 5 shows a representative IR spectrum of a BG spore sample recorded in transmission, which in many respects is representative of all spore types dne to their nearly identical compositions at the molecnlar level. 

Todeschini R, Consonni V, Mauri A, Pavan M (2004) MobyDigs software for regression and classification models by genetic algorithms in Nature-inspired methods in chemometrics genetic algorithms and artificial neural networks (R. Leardi Ed.), Chapter 5, Elsevier pp 141-167... 

A widely applied discipline of chemometrics is pattern recognition, which involves the classification and identification of samples. Its purpose is to develop a semiquantitative model that can be applied to the identification of unknown sample patterns. To assure the best reliability, pattern recognition requires the applications of a minimum of two analytical methods. The ammonium-azonium tautomerism in /V, /V-d i a I ky I a minoazo dyes used as indicators requires three techniques UV-Vis, IR, and Raman spectroscopy.223 A study of the matrix composition concerning the ratio of the compounds is necessary as well. The application of pattern recognition analysis in geochemical techniques not only assures better reliability but is also quite useful in addressing real-world environmental problems.224... 

Mass spectrometry and chemometric methods cover very diverse fields Different origin of enzymes can be disclosed with LC-MS and multivariate analysis [45], Pyrolysis mass spectrometry and chemometrics have been applied for quality control of paints [46] and food analysis [47], Olive oils can be classified by analyzing volatile organic hydrocarbons (of benzene type) with headspace-mass spectrometry and CA as well as PC A [48], Differentiation and classification of wines can similarly be solved with headspace-mass spectrometry using unsupervised and supervised principal component analyses (SIMCA = soft independent modeling of class analogy) [49], Early prediction of wheat quality is possible using mass spectrometry and multivariate data analysis [50],... 

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