Principal component analysis spectroscopy

Principal component analysis has been used in combination with spectroscopy in other types of multicomponent analyses. For example, compatible and incompatible blends of polyphenzlene oxides and polystyrene were distinguished using Fourier-transform-infrared spectra (59). Raman spectra of sulfuric acid/water mixtures were used in conjunction with principal component analysis to identify different ions, compositions, and hydrates (60). The identity and number of species present in binary and tertiary mixtures of polycycHc aromatic hydrocarbons were deterrnined using fluorescence spectra (61). 

In contrast, SIMCA uses principal components analysis to model object classes in the reduced number of dimensions. It calculates multidimensional boxes of varying size and shape to represent the class categories. Unknown samples are classified according to their Euclidean space proximity to the nearest multidimensional box. Kansiz et al. used both KNN and SIMCA for classification of cyanobacteria based on Fourier transform infrared spectroscopy (FTIR).44... 

The extent of homogeneous mixing of pharmaceutical components such as active drug and excipients has been studied by near-IR spectroscopy. In an application note from NIRSystems, Inc. [47], principal component analysis and spectral matching techniques were used to develop a near-IR technique/algorithm for determination of an optimal mixture based upon spectral comparison with a standard mixture. One advantage of this technique is the use of second-derivative spectroscopy techniques to remove any slight baseline differences due to particle size variations. 

Scheinost AC, Kretzschmar R, Pflster S, Roberts DR. Combining selective sequential extractions, X-ray absorption spectroscopy, and principal component analysis for quantitative zinc speciation in soil. Environ. Sci. Technol. 2002 36 5021-5028. 

M.-L. O Connell, T. Howley, A.G. Ryder, M.N. Leger and M.G. Madden, Classification of a target analyte in solid mixtures using principal component analysis, support vector machines, and Raman spectroscopy, Proc. SPIE-Int. Soc. Opt. Eng., 5826, 340-350 (2005). 

Schievano, E., Pasini, G., Cozzi, G., and Mammi, S. (2008). Identification of the production chain of Asiago d Allevo cheese by nuclear magnetic resonance spectroscopy and principal component analysis.. Agric. Food Chem. 56, 7208-7214. 

They employed principal components analysis (PCA) and linear discriminant analysis (LDA) to distinguish the two types of polyps. The spectra (Fig. 2.9) have bands at similar wave numbers and their features are similar, making it difficult for the untrained eye to distinguish between them. The application illustrates the importance of multivariate analysis in clinical applications of Raman spectroscopy. It is often the case that there are only small differences between normal and diseased tissues. 

Raman spectroscopy can also directly benefit TE analysis by non-invasively monitoring the growth and development of ECM by different cells on a multitude of scaffold materials exposed to various stimuli (e.g. growth factors, mechanical forces and/or oxygen pressures). Indeed the non-invasive nature of Raman spectroscopy enables the determination of the rate of ECM formation and the biochemical constituents of the ECM formed. Univariate (peak area, peak ratios, etc.) and multivariate analytical techniques (e.g. principal component analysis (PCA)) can be used to determine if there are any significant differences between the ECM formed on various scaffolds and/or cultured with different environmental parameters, and what these biochemical differences are. Least square (LS) modelling, for example, could allow the quantification of the relative components of the ECM formed (Fig. 18.3) [4, 38],... 

Infrared spectroscopy was widely used in the second half of the 20th century, and this technique has allowed some advances to be made in awareness of functionalities in, and of complexes formed by, humic molecules. However, the greatest advances in determinations of functionalities, in aspects of compositions and structures, and now in aspects of humic interactions have been made since the introduction of solid-state 13C NMR spectroscopy (Wilson, 1987 Malcolm, 1989). Chapter 15 in this book (by Simpson and Simpson) has reviewed in detail the applications of NMR in the solid and liquid states to studies of compositions and interactions of NOM. We now have a good indication of the types of functionalities that compose HS, and combinations of modem NMR technologies and principal component analysis (PCA) techniques allow us to deduce the origins of some of the functionalities (Novotny et al., 2007). 

Cohenford, M. A., Godwin, T. A., Cahn, F., Bhandare, P., Caputo, T. A. andRigas, B. (1997) Infrared spectroscopy of normal and abnormal cervical smears evaluation by principal component analysis. Gynecol. Oncol. 66, 59-65. 

PACS PCA PDB PEEM PESTM PET PrP Picture Archiving and Communication Systems Principal Component Analysis Protein Data Bank Photoemission Electron Microscopy STM Photoemission Spectroscopy Positron Emission Tomography Prion Protein... 

Other spectrophotometric techniques have been reported for the analysis of spironolactone. Near infrared diffuse reflectance first-derivative spectroscopy was used for determination of spironolactone in pharmaceutical dosage forms [30]. Readings were taken at 15 nm intervals, and then 81 absorbance readings were imput into a computer for principal component analysis. 

One of the emerging biological and biomedical application areas for vibrational spectroscopy and chemometrics is the characterization and discrimination of different types of microorganisms [74]. A recent review of various FTIR (Fourier transform infrared spectrometry) techniques describes such chemometrics methods as hierarchical cluster analysis (HCA), principal component analysis (PCA), and artificial neural networks (ANN) for use in taxonomical classification, discrimination according to susceptibility to antibiotic agents, etc. [74],... 

Multivariate curve resolution is the main topic of Malinowski s book [23]. The author is a physical chemist and so the book is oriented towards that particular audience, and especially relates to the spectroscopy of mixtures. It is well known because the first edition (in 1980) was one of the first major texts in chemometrics to contain formal descriptions of many common algorithms such as principal components analysis. 

Isaure, M.-P. et al., Quantitative Zn speciation in a contaminated dredged sediment by m-PIXE, m-SXRF, EXAFS spectroscopy and principal component analysis, Geochim. Cosmochim. Acta, 66, 1549, 2002. 

NIR spectroscopy became much more useful when the principle of multiple-wavelength spectroscopy was combined with the deconvolution methods of factor and principal component analysis. In typical applications, partial least squares regression is used to model the relation between composition and the NIR spectra of an appropriately chosen series of calibration samples, and an optimal model is ultimately chosen by a procedure of cross-testing. The performance of the optimal model is then evaluated using the normal analytical performance parameters of accuracy, precision, and linearity. Since its inception, NIR spectroscopy has been viewed primarily as a technique of quantitative analysis and has found major use in the determination of water in many pharmaceutical materials. 

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