Multivariate data analysis and experimental design

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... 

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... 

This chapter constitutes an attempt to demonstrate the utility of multivariate statistics in several stages of the scientific process. As a provocation, it is suggested that the multivariate approach (in experimental design, in data description and in data analysis) will always be more informative and make generalizations more valid than the univariate approach. Finally, the multivariate strategy can be really enjoyable, not the least for its capacity to reveal hidden treasures in data that in a univariate analysis look like a set of random numbers. 

It was suggested that computer-based data analysis techniques (often involving multivariate statistical methods) can aid in this classification or simplification, as has been so profitable in other thermochemical conversion endeavours, for example, as applied to coal and petroleum. Again, it was emphasized that there is a need for a critical synthesis of the wealth of experimental data into regimes of behaviour, and simpler predictive equations or simulations, that are useful to the technologists in industry who are designing industrial scale reactors. 

Fortunately, various chemometric-based techniques, including multivariate experimental design and data analysis techniques, have been devised to aid in optimizing the performance of systems and extend their separation capabilities. In broadest terms, chemometrics is a subdiscipline of analytical chemistry that uses mathematical, statistical, and formal logic to (10) ... 

The basic principle of experimental design is to vary all factors concomitantly according to a randomised and balanced design, and to evaluate the results by multivariate analysis techniques, such as multiple linear regression or partial least squares. It is essential to check by diagnostic methods that the applied statistical model appropriately describes the experimental data. Unacceptably poor fit indicates experimental errors or that another model should be applied. If a more complicated model is needed, it is often necessary to add further experimental runs to correctly resolve such a model. 

On the other hand, atomic emission spectra are inherently well suited for multivariate analysis due to the fact that the intensity data can be easily recorded at multiple wavelengths. The only prerequisite is that the cahbration set encompasses all likely constituents encountered in the real sample matrix. Calibration data are therefore acquired by a suitable experimental design. Not surprisingly, many of the present analytical schemes are based on multivariate calibration techniques such as multiple linear regression (MLR), principal components regression (PCR), and partial least squares regression (PLS), which have emerged as attractive alternatives. 

We close this chapter with a brief introduction to the implications of work in dynamical systems theory for experimental design and analysis. This section is meant to portray a systems perspective that may be a fruitful worldview fi om which to approach research. The multivariate, replicated, repeated-measures, single-subject design can be used to provide data for examination within this dynamical systems perspective. 

Artificial Intelligence in Chemistry Chemical Engineering Expert Systems Chemometrics Multivariate View on Chemical Problems Electrostatic Potentials Chemical Applications Environmental Chemistry QSAR Experimental Data Evaluation and Quality Control Fuzzy Methods in Chemistry Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry NMR Data Correlation with Chemical Structure Protein Modeling Protein Structure Prediction in ID, 2D, and 3D Quality Control, Data Analysis Quantitative Structure-Activity Relationships in Drug Design Quantitative Structure-Property Relationships (QSPR) Shape Analysis Spectroscopic Databases Structure Determination by Computer-based Spectrum Interpretation. 

New multivariate tools for experimental design and data analysis. 

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