Experimental chemometrics

Real data, as we have seen, is far too complicated to work with to try to obtain fundamental understanding, just as the physical world is often too complicated to study directly in toto. Therefore work such as was presented in the Linearity in Calibration chapter is needed, creating a simplified system where the characteristic of interest can be isolated and studied - just as physical experiments often work with a simplified portion of the physical world for the same reason. This might be categorized as Experimental Chemometrics , controlling the nature of the data in a way that allows us to relate the properties of the data to the behavior of the model. Does this mimic the real world No, but it does provide a window into the inner workings of the calibration calculations, and we need as many such windows as we can get. 

Note These equations are from Doming, S. N. Morgan, S. L. Experimental Design A Chemometric Approach. Elsevier Amsterdam, 1987, and pseudo-three-dimensional plots of the response surfaces can be found in their figures 11.4, 11.5, and 11.14. The response surface for problem (a) also is shown in Color Plate 13. 

Deming, S. N. Morgan, S. L. Experimental Design A Chemometric Approach. Elsevier Amsterdam, 1987. 

Morgan, E. Chemometrics Experimental Design. John Wiley and Sons Chichester, England, 1991. 

In the context of chemometrics, optimization refers to the use of estimated parameters to control and optimize the outcome of experiments. Given a model that relates input variables to the output of a system, it is possible to find the set of inputs that optimizes the output. The system to be optimized may pertain to any type of analytical process, such as increasing resolution in hplc separations, increasing sensitivity in atomic emission spectrometry by controlling fuel and oxidant flow rates (14), or even in industrial processes, to optimize yield of a reaction as a function of input variables, temperature, pressure, and reactant concentration. The outputs ate the dependent variables, usually quantities such as instmment response, yield of a reaction, and resolution, and the input, or independent, variables are typically quantities like instmment settings, reaction conditions, or experimental media. 

S. N. Denting and S. L. Morgan, Experimental Design A Chemometric Approach Elsevier Science Publishing Co., Inc., Amsterdam, The Netherlands, 1987. D. D. Wolff and M. L. Parsons, Pattern Recognition Approach to Data Interpretation Plenum Press, New York, 1983. 

Volume 3 Experimental Design A Chemometric Approach, by S.N. Deming and S.L. Morgan... 

Deming SN, Morgan SL (1993) Experimental design a chemometric approach, 2nd edn. Elsevier, Amsterdam... 

Morgan E (1991) Chemometrics experimental design. Wiley, Chichester, UK... 

The next several chapters will deal with the philosophy of experimental designs. Experimental design is at the very heart of the scientific method without proper design, it is well-nigh impossible to glean high-quality information from experimental data collected. No amount of sophisticated processing or chemometrics can create information not presented within the data. 

In point of fact, the sciences of both statistics and chemometrics each have their own approach to how experiments should be designed, each with a view toward making experimental procedures better in some sense. There is a gradation between the two approaches, nevertheless there is also somewhat of a distinction between what might be thought of as classical statistical experimental design and the more currently fashionable experimental designs considered from a chemometric point of view. These differences in approach reflect differences in the nature of the information to be obtained from each. 

Sections on matrix algebra, analytic geometry, experimental design, instrument and system calibration, noise, derivatives and their use in data analysis, linearity and nonlinearity are described. Collaborative laboratory studies, using ANOVA, testing for systematic error, ranking tests for collaborative studies, and efficient comparison of two analytical methods are included. Discussion on topics such as the limitations in analytical accuracy and brief introductions to the statistics of spectral searches and the chemometrics of imaging spectroscopy are included. 

Also, we do not cover several typical chemometrics types of analyses, such as cluster analysis, experimental design, pattern recognition, classification, neural networks, wavelet transforms, qualimetrics etc. This explains our decision not to include the word chemometrics in the title. 

Experimental Design A Chemometric Approach, by S.N. Deming and S.L. Morgan Advanced Scientific Computing in BASIC with Applications in Chemistry, Biology and Pharmacology, by P. Valko and S. Vajda PCs for Chemists, edited by J. Zupan... 

In terms of computer-based and chemometric approach, additional improvements were also needed in mathematical models for chromatography and in method development, in order to help identifying the correct type of model and the adequate experimental parameters then, application to high volume of generated data is possible. 

When from initial experiments, conditions that indicate the enantioselectivity of the system towards a given enantiomer pair or towards a limited series of substances are known, one might optimize their separation. To obtain optimal conditions, the different chemometric techniques used for method optimization in classic chromatographic or electrophoretic separations can also be applied for the chiral ones. Different experimental design approaches, using both screening and response surface designs can be In Reference 331, for... 

Morgan, E. (1991). Chemometrics Experimental Design, Analytical Chemistry by Open Learning, Wiley, Chichester. 

Berridge, J.C. (1989), Chemometrics and Method Development in High-performance Liquid Chromatography. Part 2 Sequential Experimental Designs, Chemom. Intel. Lab. Sys., 5, 195-207. 

Wolters, R., and Kateman, G. (1990), The Constmction of Simultaneous Optimal Experimental Designs for Several Polynomials in the Calibration of Analytical Methods, J. Chemometrics, 4, 171-185. 

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