Chemometrics multivariate experimental design

In order to overcome, or at least minimise, such drawbacks we can resort to the use of chemometric techniques (which will be presented in the following chapters of this book), such as multivariate experimental design and optimisation and multivariate regression methods, that offer great possibilities for simplifying the sometimes complex calibrations, enhancing the precision and accuracy of isotope ratio measurements and/or reducing problems due to spectral overlaps. 

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) ... 

CE also suffers from several weaknesses as an analytical technique (e.g., adsorption of charged species to the capillary wall, presence of Joule heating). Hence, it is important to be able to determine optimal conditions in CE method development (23). Various chemometric-based techniques including multivariate experimental design and response surface methodology have been devised to help optimize the performance of a system (23-26). 

There are broadly two uses of chemometrics that interest the process chemist. The first of these is simply data display. It is a truism that the human eye is the best analytical tool, and by displaying multivariate data in a way that can be easily assimilated by eye a number of diagnostic assessments can be made of the state of health of a process, or of reasons for its failure [ 153], a process known as MSPC [154—156]. The key concept in MSPC is the acknowledgement that variability in process quality can arise not just by variation in single process parameters such as temperature, but by subtle combinations of process parameters. This source of product variability would be missed by simple control charts for the individual process parameters. This is also the concept behind the use of experimental design during process development in order to identify such variability in the minimum number of experiments. 

Most of the approaches are illustrated with examples. We also illustrate how experimental designs can serve to develop calibration sample sets — a widely applied method in chemometrics, especially multivariate calibration. 

Otto s book on chemometrics [4] is a welcome recent text, that covers quite a range of topics but at a fairly introductory level. The book looks at computing in general in analytical chemistry including databases, and instrumental data acquisition. It does not deal with the multivariate or experimental design aspects in a great deal of detail but is a very clearly written introduction for the analytical chemist, by an outstanding educator. 

We will refer to physical measurements of die form in Table 5.1 as the A block and those in Table 5.2 as the c block. One area of confusion is that users of different techniques in chemometrics tend to employ incompatible notation. In the area of experimental design it is usual to call the measured response v e.g. the absorbance in a spectrum, and the concentration or any related parameter V. In traditional multivariate calibration this notation is swapped around. For die purpose of a coherent text it would be confusing to use two opposite notations however, some compatibility widi die established literature is desirable. Figure 5.1 illustrates the notation used in this text. 

Within this context, the aim of this work was to apply chemometric experimental designs for the optimization of casein separation by CE using a neutral capillary and to build a multivariate model for the reliable prediction of cheese... 

See also Chemometrics and Statistics Experimental Design Optimization Strategies Multivariate Classification Techniques Multivariate Calibration Techniques Expert Systems Multicriteria Decision Making Signal Processing Spectral Deconvolution and Filtering. 

Since many factors will affect experimental results, quite complex experimental designs may be necessary. The choice of the best practical levels of these factors, i.e. the optimization of the experimental conditions, will also require detailed study. These methods, along with other multivariate methods covered in the next chapter, are amongst those given the general term chemometrics. 

Sahni NS, Isaksson T, Tormod Naes. The use of experimental design methodology and multivariate analysis to determine critical control points in a process. Chemometrics Intelligent Lab Syst 2001 56 105-121. 

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