Analytical methods method comparison data model

CONTENTS 1. Chemometrics and the Analytical Process. 2. Precision and Accuracy. 3. Evaluation of Precision and Accuracy. Comparison of Two Procedures. 4. Evaluation of Sources of Variation in Data. Analysis of Variance. 5. Calibration. 6. Reliability and Drift. 7. Sensitivity and Limit of Detection. 8. Selectivity and Specificity. 9. Information. 10. Costs. 11. The Time Constant. 12. Signals and Data. 13. Regression Methods. 14. Correlation Methods. 15. Signal Processing. 16. Response Surfaces and Models. 17. Exploration of Response Surfaces. 18. Optimization of Analytical Chemical Methods. 19. Optimization of Chromatographic Methods. 20. The Multivariate Approach. 21. Principal Components and Factor Analysis. 22. Clustering Techniques. 23. Supervised Pattern Recognition. 24. Decisions in the Analytical Laboratory. 

The general aim of comparison is determination of 1) empirical and theoretical distributions of karst forms on the values of concerned factors 2) distributions of morphometric parameters of karst forms and their relationships with studied factors of natural conditions. For these purposes uses methods of cartographical and graphical modeling and analytical interpretation of quantitative data. 

Ovejero-Lopez I, Bro R, Bredie WLP. Univariate and multivariate modelling of flavour release in chewing gum using time-intensity a comparison of data analytical methods. Food Qual Prefer 2005 16 327-43. 

A general method has been developed for the estimation of model parameters from experimental observations when the model relating the parameters and input variables to the output responses is a Monte Carlo simulation. The method provides point estimates as well as joint probability regions of the parameters. In comparison to methods based on analytical models, this approach can prove to be more flexible and gives the investigator a more quantitative insight into the effects of parameter values on the model. The parameter estimation technique has been applied to three examples in polymer science, all of which concern sequence distributions in polymer chains. The first is the estimation of binary reactivity ratios for the terminal or Mayo-Lewis copolymerization model from both composition and sequence distribution data. Next a procedure for discriminating between the penultimate and the terminal copolymerization models on the basis of sequence distribution data is described. Finally, the estimation of a parameter required to model the epimerization of isotactic polystyrene is discussed. 

Technique Selection. The design of an experiment is dictated by the nature of the analytical techniques available. The "alphabet soup" of surface methods provide many alternatives to the researcher, but they also add confusion because few workers have a complete array of methods at their laboratory nor do they have a working knowledge of the many possible techniques. Comparison charts, such as Table II (also see ref. 25) can help in selection of appropriate techniques, but operator experience, equipment style and accessories, and availability all make important differences. Frequently it is useful to apply two or more complimentary methods to solve a problem. The different types of data can be used to confirm or rule out any particular model or theory. 

A method was proposed for the parameterization of impedance based models in the time domain, by deriving the corresponding time domain model equation with inverse Laplace transform of the frequency domain model equation assuming a current step excitation. This excitation signal has been chosen, since it can be easily applied to a Li-ion cell in an experiment, allows the analytical calculation of the time domain model equation and is included in the definition of the inner resistance. The voltage step responses of model elements were presented for lumped elements and derived for distributed model elements that have underlying fractional differential equations using fractional calculus. The determination of the inner resistance from an impedance spectrum was proposed as a possible application for this method. Tests on measurement data showed that this method works well for temperatures around room temperature and current excitation amplitudes up to 10 C. This technique can be used for comparisons of measured impedance spectra with conventionally determined inner resistances. 

The derivation of functional relationships between independent variables, i.e., a concentration or amount proportional quantity and dependent variables - the response - belongs to the daily work of an analytical chemist. The functional relation has to be established in the calibration step and the concentration of an unknown sample can be estimated by its inverse application. Really both the dependent and independent variables are superimposed by error. Statistical methods accounting for errors in both responses (y) and concentrations (x) can hardly be applied if only a small sample size is available because the estimates become poor. Furthermore, in comparison to the Bayesian approach the incorporation of prior knowledge or subjective aspects with respect to the uncertainty of the data is carried out more easily by fuzzy methods. Results relying on the Bayesian approach can be doubtful if standard model assumptions do not hold. ... 

The use of IR and Raman spectroscopy as complementary analytical techniques (to other physical and theoretical methods) is unlimited, especially in the case of characterizations. Also, a major use of theoretical calculations is the prediction and validation of experimental (including IR and Raman) spectroscopic data, and some applications have been reported earlier. Thus, in the surface binding of DMMP, DIMP, DFP, and Sarin to silica, a DFT comparison with the experimental IR shifts showed that the theoretically-modelled adsorption sites are similar to those found by experiment, and in the case of Cyclophosphamide, the IR and Raman spectra were assigned from DFT calculations, also a homologous series of aminobisphosphonates were studied and characterized using IR and NMR spectroscopy. ... 

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