Conventional spectral analysis methods

In addition to the conventional spectral analysis methods and chemometrics, two-dimensional (2D) correlation spectroscopy has recently been introduced to NIR spectroscopy (4,12-16). In this method spectral peaks are spread over a second dimension to simplify the visualization of complex spectra consisting of many overlapped bands and to explore correlation between the bands. There are two kinds of 2D correlation spectroscopy used in NIR spectroscopy. One is statistical 2D correlation proposed originally by Barton et al (16). This method employs cross-correlation based on the least-squares linear regression analysis to assess spectral changes in two regions, such as the NIR and mid-IR regions, that arise from variations in sample composition (16). In another 2D correlation spectroscopy proposed by Noda (12, 13), 2D spectra are constructed from a set of spectral data collected from a system under an external physical perturbation, which induces selective alterations in spectral features. 

Conventional spectral analysis methods used in NIR spectroscopy are summarized as follows. 

K. Murayama, B. Czamik-Matusewicz, Y. Wu, R. Tsenkova, Y. Ozaki. Comparison between conventional spectral analysis methods, chemometrics, and two-dimensional correlation spectroscopy in the analysis of near-infrared spectra of protein. Appl Spectrosc 54 978, 2000. 

The results for bacterial whole-cell analysis described here establish the utility of MALDI-FTMS for mass spectral analysis of whole-cell bacteria and (potentially) more complex single-celled organisms. The use of MALDI-measured accurate mass values combined with mass defect plots is rapid, accurate, and simpler in sample preparation then conventional liquid chromatographic methods for bacterial lipid analysis. Intact cell MALDI-FTMS bacterial lipid characterization complements the use of proteomics profiling by mass spectrometry because it relies on accurate mass measurements of chemical species that are not subject to posttranslational modification or proteolytic degradation. 

It has been recognized that many of the time-consuming interactive tasks carried out by an expert during the process of spectral analysis could be done more efficiently by automated computational systems [6]. Over the past few years, this potential has been realized to some degree. Today automated methods for NMR structure determination are playing a more and more prominent role and can be expected to largely supersede the conventional manual approaches to solving three-dimensional protein structures in solution. 

In an attempt to address these questions, a modern method of statistical physics was recently applied by Varotsos et al. (2007) to C02 observations made at Mauna Loa, Hawaii. The necessity to employ a modern method of C02 data analysis stems from the fact that most atmospheric quantities obey non-linear laws, which usually generate non-stationarities. These non-stationarities often conceal existing correlations between the examined time series and therefore, instead of applying the conventional Fourier spectral analysis to atmospheric time series, new analytical techniques capable of eliminating non-stationarities in the data should be utilized (Hu et al., 2001 Chen et al., 2002 Grytsai et al., 2005). 

Although the complex local structure of carbon materials cannot be easily or completely elucidated by conventional analysis methods, the high-resolution soft X-ray absorption spectroscopy and spectral analysis using the DV-Xa method are promising tools to analyze the local structure. In addition, comparative analysis of carbon materials with reference compounds that have been known their local and molecular structures is a valid approach for elucidating the local structure. 

Infrared analyses are conducted on dispersive (scanning) and Fourier transform spectrometers. Non-dispersive industrial infrared analysers are also available. These are used to conduct specialised analyses on predetermined compounds (e.g. gases) and also for process control allowing continuous analysis on production lines. The use of Fourier transform has significantly enhanced the possibilities of conventional infrared by allowing spectral treatment and analysis of microsamples (infrared microanalysis). Although the near infrared does not contain any specific absorption that yields structural information on the compound studied, it is an important method for quantitative applications. One of the key factors in its present use is the sensitivity of the detectors. Use of the far infrared is still confined to the research laboratory. 

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