Experimental Design Multivariate

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

The reliability of multispecies analysis has to be validated according to the usual criteria selectivity, accuracy (trueness) and precision, confidence and prediction intervals and, calculated from these, multivariate critical values and limits of detection. In multivariate calibration collinearities of variables caused by correlated concentrations in calibration samples should be avoided. Therefore, the composition of the calibration mixtures should not be varied randomly but by principles of experimental design (Deming and Morgan [1993] Morgan [1991]). 

A more subjective approach to the multiresponse optimization of conventional experimental designs was outlined by Derringer and Suich (22). This sequential generation technique weights the responses by means of desirability factors to reduce the multivariate problem to a univariate one which could then be solved by iterative optimization techniques. The use of desirability factors permits the formulator to input the range of property values considered acceptable for each response. The optimization procedure then attempts to determine an optimal point within the acceptable limits of all responses. 

As already mentioned, an experimental design approach is preferred to evaluate method robusmess. It is a multivariate approach, evaluating the factor effects on the responses by varying the factors simultaneously, according to the experimental conditions defined by the design. 

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)... 

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

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. 

In ICP-OES, it has been observed that analyte lines with high excitation potentials are much more susceptible to suffer matrix effects than those with low excitation potentials. The effect seems to be related to the ionisation of the matrix element in the plasma, but in fact it is a rather complicated and far from fully characterised effect [8,9]. Therefore, calibration strategies must be carefully designed to avoid problems of varying sensitivity resulting from matrix effects. A possible approach may be to combine experimental designs and multivariate calibration, in much the same way as in the case study presented in the multivariate calibration chapters. 

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. 

Several experimental designs were deployed at different concentration levels of Sb to develop multivariate regression models capable of handling typical interferences which can not be solved easily by traditional methods... 

Several experimental designs were made at different concentrations of Sb and major concomitants (which typically affect its atomic signal) in order to develop multivariate regression models... 

The multivariate interferences of Na, K, Mg and Ca on Mn determination were studied by experimental designs and multivariate regression... 

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

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