Multivariate calibration models sensitivity

In this situation, it would be ideal to produce a calibration on only one of the analyzers, and simply transfer it to all of the other analyzers. There are certainly cases where this can be done effectively, especially if response variability between different analyzers is low and the calibration model is not very complex. However, the numerous examples illustrated above show that multivariate (chemometric) calibrations could be particularly sensitive to very small changes in the analyzer responses. Furthermore, it is known that, despite the great progress in manufacturing reproducibility that process analyzer vendors have made in the past decade, small response variabilities between analyzers of the same make and... 

The most stringent need for wavenumber axis calibration is in determinations based on band position. For this reason, qualitative analyses are likely to be affected by drifts or inaccuracy in the wavenumber axis [14]. Likewise, quantitative determinations based on band position, such as strain in diamond films [6], will be affected similarly. Other quantitative analyses may also be affected by band-position error. It is common to use the raw spectral intensities (intensity at every wavenumber) in a multivariate analysis. Although this approach can be very powerful, any unexpected shift in wavenumber calibration can cause severe error in the model. In essence, the spectral pattern to which the model has been trained has been shifted. The mathematics of the model are expecting a particular relationship of intensity between adjacent variables (wavenumbers) and cannot usually account for shifts [31], To some extent, multivariate models can be desensitized to inaccuracy and imprecision by assuring that the calibration samples also exhibit some of the same shifting features, but model sensitivity may suffer as a result. Although not in common use, other deconvolution methods have been introduced which may be applicable to removing shift effects of inaccurate wavenumber calibrations [37]. 

Multivariable calibration permits the simultaneous determination of multicomponent mixtures and it is mainly based on spectroscopy data. Full-spectrum multivariate calibration methods offer the advantage of speed in the determination of the analytes, avoiding separation steps in the analytical procedures. Partial least squares (PLS) has become the usual first-order multivariate tool because of the quality of the calibration models obtained, the ease of its implementation, and the availability of software [27]. However, all first-order methods, of which PLS is no exception, are sensitive to the presence of unmodeled interferents, that is, compounds occurring in new samples that have not been included during the training step of the multivariate model. This situation is encountered... 

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. 

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