Factor analysis chromatography

Keller, H. R. and Massart, D. L., Artefacts in Evolving Factor Analysis-Based Methods for Purity Control in Liquid Chromatography with Diode-Array Detection, Ana/yt/ca Chimica Acta 263, 1992, 21-28. 

B. Waiczak, L. Morin-Allory, M. Chrdtien, M. Lafosse and M. Dreux, Factor analysis and experiment design in high-performance liquid chromatography. III. Influence of mobile phase modifications on the selectivity of chalcones on a diol stationary phase. Chemom. Intell. Lab. Syst., I (1986) 79-90. 

Iterative target transformation factor analysis (ITTFA) is an extension of TTFA and has been introduced by Hopke et al. [12] in environmetrics and by Gemperline [13,14] and Vandeginste et al. [15] in chromatography. The idea behind ITTFA is... 

B.G.M. Vandeginste, F. Leyten, M. Gerritsen, J.W. Noor, G. Kateman and J. Frank, Evaluation of curve resolution and iterative target transformation factor analysis in quantitative analysis by liquid chromatography. J. Chemom., 1 (1987) 57-71. 

P.J. Gemperline, A priori estimates of the elution profiles of the pure components in overlapped liquid chromatography peaks using target factor analysis. J. Chem. Inf. Comput. Sci., 24 (1984) 206-212. 

H.R. Keller and D.L. Massart, Peak purity control in liquid-chromatography with photodiode array detection by fixed size moving window evolving factor analysis. Anal. Chim. Acta, 246 (1991) 379-390. 

M.J.P. Gerritsen, H. Tanis, B.G.M. Vandeginste and G. Kateman, Generalized rank annihilation factor analysis, iterative target transformation factor analysis and residual bilinearization for the quantitative analysis of data from liquid-chromatography with photodiode array detection. Anal. Chem., 64 (1992) 2042-2056. 

MEEKC Microemulsion electrokinetic chromatography TFA Target factor analysis... 

An important group of methods relies on the inherent order of the data, typically time in kinetics or chromatography. These methods are often based on Evolving Factor Analysis and its derivatives. Another well known family of model-free methods is based on the Alternating Least-Squares algorithm that solely relies on restrictions such as positive spectra and concentrations. 

Walczak, B., Chretien, J.R., Dreux, M., Morin-Allory, L., and Lafosse, M. (1987), Factor Analysis and Experiment Design in High-performance Liquid Chromatography. IV. Influence of Mobile Phase Modifications of the Selectivity of Chalcones on an ODS Stationary Phase, Chemom. Intel. Lab. Sys., 1, 177-189. 

Vandeginste, B.G.M., Derks, W., and Kateman, G., Multicomponent self-modeling curve resolution in high performance liquid chromatography by iterative target transformation factor analysis, Anal. Chim. Acta, 173, 253-264, 1985. 

The conversion from abstract to chemical factors is sometimes called a rotation or transformation and will be discussed in more detail in Chapter 6, and is illustrated in Figure 4.9. Note that factor analysis is by no means restricted to chromatography. An example is the pH titration profile of a number of species containing different numbers of protons together with their spectra. Each equilibrium species has a pH titration profile and a characteristic spectrum. 

Relationship between PCA and factor analysis in coupled chromatography... 

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... 

Selection of the analytical instrumentation for the analysis of the pyrolysate is a very important step for obtaining the appropriate results on a certain practical problem. However, not only technical factors are involved in this selection the availability of a certain instrumentation is most commonly the limiting factor. Gas chromatography (GC) and gas chromatography-mass spectrometry (GC/MS) are, however, the most common techniques utilized for the on-line or off-line analysis of pyrolysates. The clear advantages of these techniques such as sensitivity and capability to identify unknown compounds explain their use. However, the limitations of GC to process non-volatile samples and the fact that larger molecules in a pyrolysate commonly retain more structural information on a polymer would make HPLC or other techniques more appropriate for pyrolysate analysis. However, not many results on HPLC analysis of pyrolysates are reported (see section 5.6). This is probably explained by the limitations in the capability of compound identification of HPLC, even when it is coupled with a mass spectrometric system. Other techniques such as FTIR or NMR can also be utilized for the analysis of pyrolysates, but their lower sensitivity relative to mass spectrometry explains their limited usage. 

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. 

Ounnar, S. Righezza, M. Chretien, J.R. Factor analysis in normal phase liquid chromatography of A-henzylideneani-lides. J. Liq. Chromatogr. Relat. Technol. 1998, 20, 2017-2037. 

Reverse-phase high-performance liquid chromatography Target factor analysis Unstirred water layer... 

M. Maeder, Evolving Factor Analysis a New Multivariate Technique in Chromatography, Anal. Chem. 59 (1987), 527-530. 

M. Maeder and A. Zilian, Evolving Factor Analysis, A New Multivariate Technique in Chromatography, Chemometrics Intelligent Laboratory Systems 3 (1988), 205-213. 

A.K. Elbergali and R.G. Brereton, Influence of Noise, Peak Position and Spectral Similarities on Resolvability of Diode-Array High-Performance Liquid Chromatography by Evolutionary Factor Analysis, Chemometrics Intelligent Laboratory Systems 23 (1994), 97-106. 

A.K. Elbergali, R.G. Brereton and A. Rahmani, Influence of the Method of Calculation of Noise Thresholds on Wavelength Selection in Window Factor Analysis of Diode Array High-Performance Liquid Chromatography, Analyst (London) 121 (1996), 585-590. 

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. 

Let us consider the different steps of a target transformation factor analysis, for example, data from the combination of liquid chromatography with ultraviolet (LTV) spectroscopy. 

The result is used to determine regions of maximum purity. These approximate to the elution profiles or spectra (in coupled chromatography) of each component in a mixture that can then be employed in factor analysis as some information on each pure component is known. Sometimes there are embedded peaks, for which there is no pure (or selective or composition 1) region. The eigenvalue plots can still provide valuable information as to where each component elutes but sometimes it is hard to obtain unique mathematical solutions to the determination of information on each compound. 

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