Factor analysis, FTIR

Fig. 11. Amide F thermal denaturation spectra for ribonuclease A as followed by FTIR (left) and VCD (right), which show the IR peak shifting from the dominant /3-sheet frequency (skewed with a maximum at 1635 cm-1) to the random coil frequency ( 1645-1650 cm-1) and the VCD shape changing from the W-pattern characteristic of an a + p structure to a broadened negative couplet typical of a more disordered coil form. The process clearly indicates loss of one form and gain of another while encompassing recognition of an intermediate form. (This is seen here most easily as the decay and growth back of the 1630 cm-1 VCD feature, but is more obvious after factor analysis of the data set, Fig. 15).

Fig. 14. Factor analysis loadings (first and second spectral components) for thermal unfolding of RNase A as monitored with amide F FTIR and far-UV ECD. In each case a pretransition is evident in the curves before the main transition at 55°C. This full band shape analysis can sense smaller variations and can be partitioned to give added insight. Since the main ECD change could be shown to be loss of intensity, the major structural change was unfolding of a helix. The frequency dispersion of the FTIR change showed that some /3-sheet loss accompanied this pretransitional helix unfolding, but that most sheet loss was in the main transition.

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. 

Factor analysis extracts information from the sample data set (e.g., IR spectra) and does not rely on reference minerals. However, because abstract factors have no physical meaning, reference minerals may be needed in target transformations or other procedures to extract mineralogical information. One valuable piece of information obtainable without the use of extraneous data is the number of components required to represent the data within experimental error. Reported applications of factor analysis to mineralogy by FTIR are few (12). However, one commercial laboratory is offering routine FTIR mineral analyses to the petroleum industry, based on related methods (22). 

The next section of this paper describes the use of classical least-squares analysis of FTIR data to determine coal mineralogy. This is followed by promising preliminary results obtained using factor analysis techniques. 

In the next section, the potential for factor analysis and related chemometric techniques for providing improvements in the determination of minerals in coal by FTIR are explored. 

Experience in this laboratory has shown that even with careful attention to detail, determination of coal mineralogy by classical least-squares analysis of FTIR data may have several limitations. Factor analysis and related techniques have the potential to remove or lessen some of these limitations. Calibration models based on partial least-squares or principal component regression may allow prediction of useful properties or empirical behavior directly from FTIR spectra of low-temperature ashes. Wider application of these techniques to coal mineralogical studies is recommended. 

In the ATR FTIR study of the synthesis of cyclopentyl silsesquioxane 7F3, in situ ATR FTIR spectra of the reaction mixture were collected every 2 min during the reaction. The spectra obtained were plotted as a function of reaction time (Fig. 9.11). Pure component spectra and relative concentration profiles were subsequently recovered using a multivariate curve resolution (MCR) [59] technique based on a modified target factor analysis algorithm [60]. 

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. 

PLS factor analysis supplied with FTIR systems has been applied to determine vinyl acetate concentrations in PE copolymer pellets of varying size using PA-FTIR without sample preparation [453]. Herres [192] has compared the suitability of various non-destructive methods (FTIR, ATR-FTIR, PA-... 

M. Reeder, W. Enlow and E. Borkowski, Factors Influencing the FTIR Analysis of Phosphites in Polyolefins, Technical Paper FI 81, GE Specialty Chemicals, General Electric Company, Parkersburg, WV (1989). 

Selection of on-site analytical techniques involves evaluation of many factors including the specific objectives of this work. Numerous instrumental techniques, GC, GC-MS, GC-MS-TEA, HPLC, HPLC-MS-MS, IR, FTIR, Raman, GC-FTIR, NMR, IMS, HPLC-UV-IMS, TOF, IC, CE, etc., have been employed for their laboratory-based determination. Most, however, do not meet on-site analysis criteria, (i.e., are not transportable or truly field portable, are incapable of analyzing the entire suite of analytes, cannot detect multiple analytes compounded with environmental constituents, or have low selectivity and sensitivity). Therefore, there exists no single technique that can detect all the compounds and there are only a few techniques exist that can be fielded. The most favored, portable, hand-held instrumental technique is ion mobility spectrometry (IMS), but limitations in that only a small subset of compounds, the inherent difficulty with numerous false positives (e.g., diesel fumes, etc.), and the length of time it takes to clear the IMS back to background are just two of its many drawbacks. 

The opportunity to reduce the cost per analysis point can be very important for certain applications. One option with FTIR is to use sample stream multiplexing with a manifold system. For gas-based systems this is very straightforward, and does not impose a high cost overhead. Liquid systems are more complex, and are dependent on the nature of the stream involved, and material reactivity, miscibility and viscosity are important factors to consider. As noted previously, with the introduction of new, miniaturized technologies, the hope is that newer, less expensive devices can be produced, and these can provide the needed multiplicity to reduce the cost per analysis point to an acceptable level. 

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. 

Table V. Rotated Matrix of Factor Loadings for Components Analysis of FTIR Absorption Bands in the Aliphatic Stretching Region...

Aliphatic stretching region in vitrinite, rotated matrix of factor loadings for components analysis of FTIR absorption bands, 1l4t... 

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