Multivariate spectral analysis

Perform multivariate spectral analysis to obtain selective multianalyte response... 

We have shown a new concept for selective chemical sensing based on composite core/shell polymer/silica colloidal crystal films. The vapor response selectivity is provided via the multivariate spectral analysis of the fundamental diffraction peak from the colloidal crystal film. Of course, as with any other analytical device, care should be taken not to irreversibly poison this sensor. For example, a prolonged exposure to high concentrations of nonpolar vapors will likely to irreversibly destroy the composite colloidal crystal film. Nevertheless, sensor materials based on the colloidal crystal films promise to have an improved long-term stability over the sensor materials based on organic colorimetric reagents incorporated into polymer films due to the elimination of photobleaching effects. In the experiments... 

KEYWORDS Laser-induced breakdown spectroscopy, LIBS, multivariate spectral analysis, obsidian sourcing, geochemical fingerprinting... 

For a quantitative analysis or classification of biological or medical problems by means of Raman spectroscopy the application of multivariate spectral analysis methods is required. These multivariate methods allow one to extract diagnostic, chemical, and morphological relevant information out of the complex Raman spectrum and must be applied due to the high amount of similar spectral features. 

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

Spectral features and their corresponding molecular descriptors are then applied to mathematical techniques of multivariate data analysis, such as principal component analysis (PCA) for exploratory data analysis or multivariate classification for the development of spectral classifiers [84-87]. Principal component analysis results in a scatter plot that exhibits spectra-structure relationships by clustering similarities in spectral and/or structural features [88, 89]. 

Haaland, D.M., et. al. "Multivariate Least-Squares Methods Applied to the Quantitative Spectral Analysis of Multicomponent Samples", Appl. Spec. 1985 (39) 73-84. 

Differentiation of vapor responses of the colloidal crystal film was accomplished with spectral measurements of the shape changes of the diffraction peak. Selectivity of response was obtained by applying multivariate data analysis to correlate these spectral changes to the effects of species of different chemical nature and to establish the identity and concentration of species. 

We collected sets of single-shot broadband LIBS spectra in the Army Research LIBS Laboratory for 27 obsidian samples from major sites across the CVF as well as for samples from 4 other California obsidian locations - Bodie Hills, Mt. Hicks, Fish Springs, and Shoshone. The resultant obsidian LIBS spectral database was analyzed by multivariate statistical analysis. 

In this example, we apply D-PLS (PLS discriminant analysis, see Section 5.2.2) for the recognition of a chemical substructure from low-resolution mass spectral data. This type of classification problems stood at the beginning of the use of multivariate data analysis methods in chemistry (see Section 1.3). 

A great variety of different methods for multivariate classification (pattern recognition) is available (Table 5.6). The conceptually most simply one is fc-NN classification (Section 5.3.3), which is solely based on the fundamental hypothesis of multivariate data analysis, that the distance between objects is related to the similarity of the objects. fc-NN does not assume any model of the object groups, is nonlinear, applicable to multicategory classification, and mathematically very simple furthermore, the method is very similar to spectral similarity search. On the other hand, an example for a rather sophisticated classification method is the SVM (Section 5.6). 

Varmuza, K. Analytical Sciences 17(suppl.), 2001, i467-i470. Recognition of relationships between mass spectral data and chemical structures by multivariate data analysis. 

E.V. Thomas and D.M. Haaland, Comparison of multivariate calibration methods for quantitative spectral analysis. Anal. Chem., 62, 1091-1099 (1990). 

Lewi, P. J. (2005), Spectral mapping, a personal and historical account of an adventure in multivariate data analysis, Chemomet. Intell. Lab. Syst., 77, 215-223. 

The feasibility of diffuse reflectance NIR, Fourier transform mid-IR and FT-Raman spectroscopy in combination with multivariate data analysis for in/ on-line compositional analysis of binary polymer blends found in household and industrial recyclates has been reported [121, 122]. In addition, a thorough chemometric analysis of the Raman spectral data was performed. 

Peebles, D.E., J.A. Ohlhausen, P.G. Kotula, S. Hutton, and C. Blomfield. 2004. Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging Effect of image acquisition time. J. Vacuum Sci. Tech. A 22 1579-1586. 

Siclovan, O., Fluorescence spectroscopy and multivariate spectral descriptor analysis for high-throughput multiparameter optimization of polymerization conditions of combinatorial 96-microreactor arrays, J. Comb. Chem. 2003, 5, 8-17. 

Lienert, D., Anklam, E., and Panne, U. 1998. Gas chromatography—mass spectral analysis of roots of Echinacea species and classification by multivariate data analysis. Phytochem. Anal. 9, 88-98. 

Of course, the reason for the improvement in the calibration model when the second term is included is that A21 serves to compensate for the absorbance due to the tyrosine since X21 is in the spectral region of a tyrosine absorption band with little interference from tryptophan. Figure 6. In general, the selection of variables for multivariate regression analysis may not be so obvious. 

As an easily managed example of multivariate data analysis we shall consider the spectral data presented in Table 11. These data represent the recorded absorbance of 14 standard solutions containing known amounts of tryptophan, measured at seven wavelengths, in the UV region under noisy conditions and in the presence of other absorbing species. Two test spectra, X and XZ, are also included. 

Quantitative metabolomics, on the other hand, can be described as a targeted approach focused on the analysis of specific metabolite species. In this method, multivariate statistical analysis follows metabolite identification and quantitation. Because of the reliable peak identification and measurement of metabolite integrals, quantitative metabolomics promises greater insights into the dynamics and fluxes of metabolites, as well as robust statistical models for distinguishing classes with better classification accuracy. A major requirement for quantitative metabolomics is good-quality spectral analysis to provide reliable peak assignments and metabolite identification. 

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