Infrared spectroscopy factor analysis

Fourier transform infrared (FTIR) spectroscopy of coal low-temperature ashes was applied to the determination of coal mineralogy and the prediction of ash properties during coal combustion. Analytical methods commonly applied to the mineralogy of coal are critically surveyed. Conventional least-squares analysis of spectra was used to determine coal mineralogy on the basis of forty-two reference mineral spectra. The method described showed several limitations. However, partial least-squares and principal component regression calibrations with the FTIR data permitted prediction of all eight ASTM ash fusion temperatures to within 50 to 78 F and four major elemental oxide concentrations to within 0.74 to 1.79 wt % of the ASTM ash (standard errors of prediction). Factor analysis based methods offer considerable potential in mineral-ogical and ash property applications. 

In conclusion, the analysis of spectra properly recorded to 185 nm, or lower where possible, can give useful estimates of secondary structure content, but the content of turns and of P-structure should be interpreted with caution. Fourier transform infrared spectroscopy (FTIR) provides better estimates of the latter. When using the results of far-UV CD determination to characterize reproducibility of folding for different samples, it is important first to compare the spectra visually and to look for possible trends or factors that may explain small differences, rather than to rely solely on comparison of derived secondary structure contents. 

Rodriguez-Otero, J.L., Hermida, M., Centeno, J. 1997. Analysis of dairy product by near infrared spectroscopy. J. Agric. Food Chem. 45, 2815-2819. van de Voort, F.R., Kermasha, S., Mills, B.L., Ng-Kwai-Hang, K.F. 1987. Factors affecting differences in milk test fat results obtained by the Babcock, Rose-Gottlieb and infrared milk analysis. J. Dairy Sci. 71, 290-298. 

Following the discovery in 1972 by Turner and co-workers 18) that Cr(CO)5 could form complexes with CH4, Poliakoff and Turner used matrix isolation and infrared spectroscopy 19) to study the photolysis of Fe(CO)5. Photolysis of Fe(CO)5 in a neon or argon matrix produced Fe(CO)4, which was found to have a relatively unusual structure with C2v symmetry. The energy-factored (or Cotton-Kraihanzel) force field (EFFF) is a powerful method used in the analysis of the... 

Allosio N, Boivin P, Bertrand D, Courcoux P, Characterisation of barley transformation into malt by three-way factor analysis of near infrared spectra, Journal of Near Infrared Spectroscopy, 1997, 5, 157-166. 

There are indications that each protein has a characteristic infrared spectrum and so infrared analysis may prove to be a valuable analytical tool in rapidly distinguishing similar from dissimilar proteins, and also similar proteins from one another. Infrared spectroscopy may also be a useful technique for studying the reactions of polypeptides and proteins with other chemical compounds. Further progress in the apphcation of infrared analysis to the protein field depends on several factors, which fall into two main categories. 

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. 

Factor analysis was performed on the IR spectra of subfractions 1 to 13 using 28 nonzero wavenumber variables. Five of the orginal 33 variables were unique to spectrum 15 and were not used in the factor analysis of samples 1-13. Figure 4b shows the factor score plots of the IR data on subfractions 1-13 in the FI vs. F2 factor space. Samples 1-7 are very close together, implying that infrared spectroscopy does not detect much difference between these dominantly aliphatic mixutres in this space. Analysis of the underlying correlation between variables by means of the variance diagram method showed that component (a) (350 ) represents methyl and methylene absorptions such as 2870, 2850, 2920, 1460 and 720 cm". Component axes (b) (120 ) with peak 1516 cm l and (c) (160 ) with 3050,... 

The improvement in computer technology associated with spectroscopy has led to the expansion of quantitative infrared spectroscopy. The application of statistical methods to the analysis of experimental data is known as chemometrics [5-9]. A detailed description of this subject is beyond the scope of this present text, although several multivariate data analytical methods which are used for the analysis of FTIR spectroscopic data will be outlined here, without detailing the mathematics associated with these methods. The most conunonly used analytical methods in infrared spectroscopy are classical least-squares (CLS), inverse least-squares (ILS), partial least-squares (PLS), and principal component regression (PCR). CLS (also known as K-matrix methods) and PLS (also known as P-matrix methods) are least-squares methods involving matrix operations. These methods can be limited when very complex mixtures are investigated and factor analysis methods, such as PLS and PCR, can be more useful. The factor analysis methods use functions to model the variance in a data set. 

In mid-infrared spectroscopy, Fourier transform instruments are used almost exclusively. However, in Raman spectroscopy both conventional dispersive and Fourier transform techniques have their applications, the choice being governed by several factors [133], [134]. Consequently, a modern Raman laboratory is equipped with both Fourier transform and CCD-based dispersive instruments. For a routine fingerprint analysis, the FT system is generally used, because it requires less operator skill and is quicker to set up the FT system is also be tried first if samples are highly fluorescent or light sensitive. However, if the utmost sensitivity is required, or if Raman lines with a shift smaller than 100 cm" are to be recorded, conventional spectrometers are usually preferred. 

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