Fourier-transform infrared spectroscopy factor analysis

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. 

We have, in conclusion, presented a systematic approach for investigating polymer chain conformations in the amorphous and semicrystalline materials. Fourier transform infrared spectroscopy has been utilized and combined with the techniques of factor analysis, absorbance ratioing, and least squares curve-fitting. 

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. 

On the other hand, successful identification of bacterial spores has been demonstrated by using Fourier transform infrared photoacoustic and transmission spectroscopy " in conjunction with principal component analysis (PCA) statistical methods. In general, PCA methods are used to reduce and decompose the spectral data into orthogonal components, or factors, which represent the most coimnon variations in all the data. As such, each spectrum in a reference library has an associated score for each factor. These scores can then be used to show clustering of spectra that have common variations, thus forming a basis for group member classification and identification. 

Major technological and scientific innovation in the past 10 to 15 years has significantly broadened the applicability of Raman spectroscopy, particularly in chemical analysis. Fourier transform (FT)-Raman, charge-coupled device (CCD) detectors, compact spectrographs, effective laser rejection filters, near-infrared lasers, and small computers have contributed to a revolution in Raman instrumentation and made routine analytical applications possible. An increase in instrumental sensitivity by factors as large as 10, plus decreases in both interferences and noise resulted from this revolution. The number of vendors of Raman spectrometers increased from 3 to 12 over a 10-year period, and integrated commercial spectrometers led to turnkey operation and robust reliability. 

Fourier transform mid-infrared (FTIR), near-infrared (FTNIR), and Raman (FT-Raman) spectroscopy were used for discrimination among 10 different edible oils and fats, and for comparing the performance of these spectroscopic methods in edible oil/fat studies. The FTIR apparatus was equipped with a deuterated triglycine sulfate (DTGS) detector, while the same spectrometer was also used for FT-NIR and FT-Raman measurements with additional accessories and detectors. The spectral features of edible oils and fats were studied and the unsaturation bond (C=C) in IR and Raman spectra was identified and used for the discriminant analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for the disaimination and classification of different edible oils and fats based on spectral data. FTIR spectroscopy measurements in conjunction with CVA yielded about 98% classification accuracy of oils and fats followed by FT-Raman (94%) and FTNIR (93%) methods however, the number of factors was much higher for the FT-Raman and FT-NIR methods. 

The use of infrared spectrometry for quantitive analysis became possible only in the 1980s, when affordable and user-friendly benchtop Fourier-transform spectrometers became available. The sensitivity of the FT-IR spectroscopy was, however, insufficient to meet the requirements of the immunoassay. To address this problem, an instrument equipped with a liquid nitrogen-cooled detector made from a semi-conducting material, for example MCT (mercury-cadmium-telluride) or InSb (indium antimonide), was used to increase sensitivity by a factor of 20 compared with the thermal detector DTGS found in standard FT-IR machines. Use of a light-pipe cell with a long optical path (20 mm) for a... 

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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