Multivariate analysis chromatography

RP-LC, multivariate analysis methods such as principle component analysis (PCA) and nonlinear mapping (NML), or comparative molecular field analysis (CoMFA) approaches and linear free energy-related (LFER) equations have been used to derive structure-retention relationships in chiral chromatography [16-18]. 

Characterization Tools for Pyrolysis Oils. It wasn t too many years ago that the only tools available to the scientist interested in pyrolysis oil composition were gas chromatography and thermogravi-metric analysis. The complexity of the pyrolysis oils demands high performance equipment, and a list of such equipment mentioned during the symposium would include proton and carbon nuclear magnetic resonance spectroscopy, free-jet molecular beam/mass spectrometry (16.25), diffuse reflectEuice Fourier transform infrared spectrometry ( ), photoelectron spectroscopy ( ), as well as procedures such as computerized multivariate analysis methods (32) - truly a display of the some of the most sophisticated analytical tools known to man, and a reflection of the difficulty of the oil composition problem. 

The hydrocarbon ("oil") fraction of a coal pyrolysis tar prepared by open column liquid chromatography (LC) was separated into 16 subfractions by a second LC procedure. Low voltage mass spectrometry (MS), infrared spectroscopy (IR), and proton (PMR) as well as carbon-13 nuclear magnetic resonance spectrometry (CMR) were performed on the first 13 subfractions. Computerized multivariate analysis procedures such as factor analysis followed by canonical correlation techniques were used to extract the overlapping information from the analytical data. Subsequent evaluation of the integrated analytical data revealed chemical information which could not have been obtained readily from the individual spectroscopic techniques. The approach described is generally applicable to multisource analytical data on pyrolysis oils and other complex mixtures. 

Bylund, D., Samskog, J., Markides, K.E. and Jacobsson, S.P., Classification of lactate dehydrogenase of different origin by liquid chromatography-mass spectrometry and multivariate analysis. J. Am. Soc. Mass Spectrom., 14, 236-240 (2003). 

Zambonin, C.G., Balest, L., De Benedetto, G.E. and Palmisano, R, Solid-phase microextraction-gas chromatography mass spectrometry and multivariate analysis for the characterization of roasted coffees. Talanta, 66, 261-265 (2005). 

Versari A, Parpinello GP, Mattioli AU, Galassi S (2008) Characterization of Italian commercial apricot juices by high-performance liquid chromatography analysis and multivariate analysis. Food Chem 108 334-340... 

MG Moshonas, PE Shaw. Dynamic headspace gas chromatography combined with multivariate analysis to classify fresh and processed orange juices. J Essent Oil Res 9 133-139, 1997. 

Multiway and particularly three-way analysis of data has become an important subject in chemometrics. This is the result of the development of hyphenated detection methods (such as in combined chromatography-spectrometry) and yields three-way data structures the ways of which are defined by samples, retention times and wavelengths. In multivariate process analysis, three-way data are obtained from various batches, quality measures and times of observation [55]. In image analysis, the three modes are formed by the horizontal and vertical coordinates of the pixels within a frame and the successive frames that have been recorded. In this rapidly developing field one already finds an extensive body of literature and only a brief outline can be given here. For a more comprehensive reading and a discussion of practical applications we refer to the reviews by Geladi [56], Smilde [57] and Henrion [58]. 

K. Wuthold, I. Germann, G. Roos, O. Kelber, D. Weiser, H. Heinle and K.-A. Kovar, Thin-layer chromatography and multivariate data analysis of willow bark extracts../. Chromatogr. Sci. 42 (2004) 306-309. 

A principal components multivariate statistical approach (SIMCA) was evaluated and applied to interpretation of isomer specific analysis of polychlorinated biphenyls (PCBs) using both a microcomputer and a main frame computer. Capillary column gas chromatography was employed for separation and detection of 69 individual PCB isomers. Computer programs were written in AMSII MUMPS to provide a laboratory data base for data manipulation. This data base greatly assisted the analysts in calculating isomer concentrations and data management. Applications of SIMCA for quality control, classification, and estimation of the composition of multi-Aroclor mixtures are described for characterization and study of complex environmental residues. 

KC Amoldsson, P Kaufmann. Lipid class analysis by normal phase high performance liquid chromatography, development and optimization using multivariate methods. Chromatographia 38 317-324, 1994. 

Plumb, R. S., Stumpf, C. L., Granger, J. H., Castro-Perez, J., Haselden, J. N., and Dear, G. J. (2003). Use of liquid chromatography/time-of-fhght mass spectrometry and multivariate statistical analysis shows promise for the detection of chug metabolites in biological fluids. Rapid Commun. Mass Spectrom. 17 2632-2638. 

Gas chromatography (GC)-MS coupled with multivariate statistical analysis proved valuable in verifying the authenticity of Echinacea species (Lienert et al, 1998). Similar root extracts could be grouped, based on the identified compounds from the GC-run, by principal component and cluster analysis. The correct grouping of the Echinacea species (i.e., purpurea, angustifolia, and pallida) was not influenced by the extraction method or by the aging process of the roots. 

Lienert, D., Anklam, E., and Panne, U. 1998. Gas chromatography—mass spectral analysis of roots of Echinacea species and classification by multivariate data analysis. Phytochem. Anal. 9, 88-98. 

Principal component analysis is most easily explained by showing its application on a familiar type of data. In this chapter we show the application of PCA to chromatographic-spectroscopic data. These data sets are the kind produced by so-called hyphenated methods such as gas chromatography (GC) or high-performance liquid chromatography (HPLC) coupled to a multivariate detector such as a mass spectrometer (MS), Fourier transform infrared spectrometer (FTIR), or UV/visible spectrometer. Examples of some common hyphenated methods include GC-MS, GC-FTIR, HPLC-UV/Vis, and HLPC-MS. In all these types of data sets, a response in one dimension (e.g., chromatographic separation) modulates the response of a detector (e.g., a spectrum) in a second dimension. 

Multivariate methods of data analysis were first applied in chromatography for retention prediction purposes [7. More recently, principal component analysis (PCA), correspondence factor analysis (CFA) and spectral mapping analysis (SMA) have been employed to objectively cla.ssify. stationary phase materials according to the retention... 

Plumb, R.S. Stumpf, C.L. Granger, J.H. Castro-Perez, J. Haselden, J.N. Dear, G.J. Use of Liquid Chromatography/Time-of-Flight Mass Spectrometry and Multivariate Statistical Analysis Shows Promise for the Detection of Drug Metabolites in Biological Fluids, Rapid Commun. Mass Spectrom. 17(23), 2632-2638 (2003). 

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