Multivariate data analysis and experimental

Multivariate data analysis and experimental design, 25 (1988) 291 Muscarinic Receptors, 43 (2005) 105... 

K.H. Esbensen, Multivariate Data Analysis - in Practice. An Introduction to Multivariate Data Analysis and Experimental Design, 5th edn, CAMO AS, Oslo, 2001. 

Multivariate Data Analysis and Experimental Design in Biomedical Research... 

Winiwarter, S., Bonham, N. M., Ax, F., Hallberg, A., Lennemas, H., Karlen, A. Correlation of human jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach. J. Med. Chem. 1998, 41, 4939-4949. 

Winiwarter S, Bonham NM, Ax F, Hallberg A, Lennernas H and Karlen A (1998) Correlation of Human Jejunal Permeability (in Vivo) of Drugs With Experimentally and Theoretically Derived Parameters A Multivariate Data Analysis Approach. J Med Chem 41 pp 4939 1949. 

Parshad, H., Frydenvang, K., Eiljefors, T., and Earsen, C.S. Correlation of aqueous solubility of salts of benzylamine with experimentally and theoretically derived parameters. A multivariate data analysis approach, Int. J. Pharmaceut., 237(1-2) 193-207, 2002. 

Chemometrican Data management and data fusion Process data analysis Multivariate data analysis Analyzer calibration model development Method equivalence Process models development (e.g., MSPC) Experimental design (e.g., DOE)... 

Data analysis and sensibly applied statistical tools are of crucial importance for metabolomics. Good experimental design is of course a fundamental first requirement. There have been a number of books and research papers written recently discussing statistics use and models for data analysis of metabolomics.100-104 Statistical and experimental robustness have been the focus of metabolomics and demonstrated in a study of NMR protocols and multivariate statistical batch processing, which were examined for consistency over six different centers. The data were shown to be sufficiently robust to generate comparable results across each center.105... 

A different approach to mathematical analysis of the solid-state C NMR spectra of celluloses was introduced by the group at the Swedish Forest Products Laboratory (STFI). They took advantage of statistical multivariate data analysis techniques that had been adapted for use with spectroscopic methods. Principal component analyses (PCA) were used to derive a suitable set of subspectra from the CP/MAS spectra of a set of well-characterized cellulosic samples. The relative amounts of the I and I/3 forms and the crystallinity index for these well-characterized samples were defined in terms of the integrals of specific features in the spectra. These were then used to derive the subspectra of the principal components, which in turn were used as the basis for a partial least squares analysis of the experimental spectra. Once the subspectra of the principal components are validated by relating their features to the known measures of variability, they become the basis for analysis of the spectra of other cellulosic samples that were not included in the initial analysis. Here again the interested reader can refer to the original publications or the overview presented earlier. ... 

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