Chemometrics multivariate statistical analysis

Introduction to multivariate statistical analysis in chemometrics / Kurt Varmuza and Peter Filzmoser. p. cm. 

The arrival of computers in every chemical laboratory has made possible the use of multivariate statistical analysis and mathematics in the analysis of measured chemical data. Sometimes, the methods were inadequate or only partially suitable for a particular chemical problem, so handling methods were modified or new ones developed to fit the chemical problem. On the basis of these elements, common to every field of chemistry, in 1974 a new chemical science was identified chemometrics, the science of chemical information. In the same year, Bruce Kowalski and Svante Wold founded the Chemometrics Society, which since then has been spreading information on multivariates in chemistry all over the world. 

The following two examples [EINAX et al., 1990 KRIEG and EINAX, 1994] demonstrate not only the power, but also the limits of multivariate statistical methods applied to the description of polluted soils loaded with heavy metals from different origins. Case studies with chemometric description of soil pollution by organic compounds are also discussed in the literature. DING et al. [1992], for example, evaluated local sources of chlorobenzene congeners in soil samples by using different methods of multivariate statistical analysis. 

Westerhuis JA, Kourti T, MacGregor JF, Comparing alternative approaches for multivariate statistical analysis of batch process data, Journal of Chemometrics, 1999, 13, 397-413. 

Varmuza, K. Filzmoser, P. (2009). Introduction to multivariate statistical analysis in chemometrics, CRC Press, Taylor Francis Group, ISBN 14005975, Boca Raton, FL, USA... 

Varmuza, K. Filzmoser, P. (2009). Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press, ISBN 9781420059472, Florida, USA Lai, H. Nakasi, L Lacoste, E. Singh, N. Sasaki (2009). T. Artemisinin-Transferrin Conjugate Retards Growth of Breast Tumors in the Rat. Anticancer Research, Vol. 29, No. 10, (October 2009), pp. 3807-3810, ISSN 1791-7530... 

Varmuza, K. and Filzmoser, R (2009) Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press, Boca Raton, EL, Berlin. 

Anderson TW (2003) An Introduction to Multivariate Statistical Analysis, 3rd edn. New York Wiley-Interscience. Beebe KR, Pell RJ, and Seasholtz MB (1998) Chemometrics A Practical Guide. New York Wiley. 

Varmuza K, Filzmoser P. Introduction to multivariate statistics analysis in chemometrics. Boca Raton, FL CRC Press Taylor Francis Group 2009. 

Due to the large size of the datasets obtained from such a study (>100 high-resolution chromatorgrams each with 20-40 identified products) it is convenient to employ relatively simple chemometric methodologies such as multivariate statistical analysis to effectively analyze these data [74]. In this study, principle components analysis (PCA) was employed to reduce the dimensionality of the complete pyrolysis dataset and extract significant correlations between sample structure and product speciation. 

Evidence of the appHcation of computers and expert systems to instmmental data interpretation is found in the new discipline of chemometrics (qv) where the relationship between data and information sought is explored as a problem of mathematics and statistics (7—10). One of the most useful insights provided by chemometrics is the realization that a cluster of measurements of quantities only remotely related to the actual information sought can be used in combination to determine the information desired by inference. Thus, for example, a combination of viscosity, boiling point, and specific gravity data can be used to a characterize the chemical composition of a mixture of solvents (11). The complexity of such a procedure is accommodated by performing a multivariate data analysis. 

A homogeneity index or significance coefficienf has been proposed to describe area or spatial homogeneity characteristics of solids based on data evaluation using chemometrical tools, such as analysis of variance, regression models, statistics of stochastic processes (time series analysis) and multivariate data analysis (Singer and... 

A total of 185 emission lines for both major and trace elements were attributed from each LIBS broadband spectrum. Then background-corrected, summed, and normalized intensities were calculated for 18 selected emission lines and 153 emission line ratios were generated. Finally, the summed intensities and ratios were used as input variables to multivariate statistical chemometric models. A total of 3100 spectra were used to generate Partial Least Squares Discriminant Analysis (PLS-DA) models and test sets. 

Nowadays, generating huge amounts of data is relatively simple. That means Data Reduction and Interpretation using multivariate statistical tools (chemometrics), such as pattern recognition, factor analysis, and principal components analysis, can be critically important to extracting useful information from the data. These subjects have been introduced in Chapters 5 and 6. 

Despite the broad definition of chemometrics, the most important part of it is the application of multivariate data analysis to chemistry-relevant data. Chemistry deals with compounds, their properties, and their transformations into other compounds. Major tasks of chemists are the analysis of complex mixtures, the synthesis of compounds with desired properties, and the construction and operation of chemical technological plants. However, chemical/physical systems of practical interest are often very complicated and cannot be described sufficiently by theory. Actually, a typical chemometrics approach is not based on first principles—that means scientific laws and mles of nature—but is data driven. Multivariate statistical data analysis is a powerful tool for analyzing and structuring data sets that have been obtained from such systems, and for making empirical mathematical models that are for instance capable to predict the values of important properties not directly measurable (Figure 1.1). 

Because data analysis is of central interest, particularly in the application of chemometric methods in the field of environmental research, a rough list of important multivariate statistical methods is given below (Tab. 1-1). 

The main goal of this chapter is to present the theoretical background of some basic chemometric methods as a tool for the assessment of surface water quality described by numerous chemical and physicochemical parameters. As a case study, long-term monitoring results from the watershed of the Struma River, Bulgaria, are used to illustrate the options offered by multivariate statistical methods such as CA, principal components analysis, principal components regression (models of source apportionment), and Kohonen s SOMs. 

Hellberg, E. Johansson, W. Lindberg, M. Sjoestroem, Multivariate data analysis in chemistry , in B.R. Kowalski, (ed.), Chemometrics, mathematics and statistics in chemistry , D. Reidel Publishing Company, Dordrecht, Holland (1984), p. 17,... 

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