Precision chemometrics

Because of peak overlappings in the first- and second-derivative spectra, conventional spectrophotometry cannot be applied satisfactorily for quantitative analysis, and the interpretation cannot be resolved by the zero-crossing technique. A chemometric approach improves precision and predictability, e.g., by the application of classical least sqnares (CLS), principal component regression (PCR), partial least squares (PLS), and iterative target transformation factor analysis (ITTFA), appropriate interpretations were found from the direct and first- and second-derivative absorption spectra. When five colorant combinations of sixteen mixtures of colorants from commercial food products were evaluated, the results were compared by the application of different chemometric approaches. The ITTFA analysis offered better precision than CLS, PCR, and PLS, and calibrations based on first-derivative data provided some advantages for all four methods. ... 

A critical attitude towards the results obtained in analysis is necessary in order to appreciate their meaning and limitations. Precision is dependent on the practical method and beyond a certain degree cannot be improved. Inevitably there must be a compromise between the reliability of the results obtained and the use of the analyst s time. To reach this compromise requires an assessment of the nature and origins of errors in measurements relevant statistical tests may be applied in the appraisal of the results. With the development of microcomputers and their ready availability, access to complex statistical methods has been provided. These complex methods of data handling and analysis have become known collectively as chemometrics. 

The calibration problem in chromatography and spectroscopy has been resolved over the years with varying success by a wide variety of methods. Calibration graphs have been drawn by hand, by instruments, and by commonly used statistical methods. Each method can be quite accurate when properly used. However, only a few papers, for example ( 1,2,15,16,26 ), show the sophisticated use of a chemometric method that contains high precision regression with total assessment of error. 

Chemometric methods can greatly increase the number of analyzable peaks in MDLC in particular, the generalized rank annihilation method (GRAM) can quantify overlapping peaks by deconvoluting the combined signal to those of each dimension. Standards with precise retention time are required, and there must be some resolution in both dimensions [60,61]. 

Given the quite simple and clear model of sampling strategies and the economically very important impact of sampling there has been published comparatively little about sampling strategies. The emphasis has been more on analytical techniques. Detection limit, precision and capacity have been the main topics in analytical chemistry for more then 30 years. Chemometrics, providing means to extract more informa-... 

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. 

Internal standard (IS) calibration requires ratioing of an analytical signal to an IS which has very similar characteristics to that of the analyte of interest (an element which is similar to the analyte either in mass, ionisation potential or chemical behaviour). Quantitative analysis applying internal standardisation is the most popular calibration strategy in ICP-MS, as improvements in precision are obtained when the technique is appropriately used. Of course, the validity of this calibration method requires that one ensures a good selection of the correct internal standard. For this purpose it is possible to resort to chemometric methods [16]. 

In any chemometrics application, it is critical to obtain calibration samples that are both sufficiently relevant and sufficiently representative of the samples that the model will be applied to. Furthermore, for quantitative applications, the reference concentrations of the calibration samples must also be accurate and precise enough to result in a model that performs within desired specifications. In PAC, it is often difficult to obtain calibration samples that are both highly relevant and very accurate. For extracted process samples,... 

Frequently, however, the lack of specificity in an analytical technique can be compensated for with sophisticated data processing, as described in the chemometrics chapter of this text (Chapter 8). Quinn and associates provide a demonstration of this approach, using fiber-optic UV-vis spectroscopy in combination with chemometrics to provide realtime determination of reactant and product concentrations.23 Automatic window factor analysis was used to evaluate the spectra. This technique was able to detect evidence of a reactive intermediate that was not discernable by off-line HPLC, and control charting of residuals was shown to be diagnostic of process upsets. Similarly, fiber-optic NIR was demonstrated by some of the same authors to predict reaction endpoint with suitable precision using a single PLS factor.24... 

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. 

All manufacturers offer optical measurement tools, the performance of which are in general satisfactory, though methods of standardisation of the measurement, tools for the treatment of spectral data and the quality of the chemometric tools available are usually more variable. The quality of the chemometrics used to obtain spectral data of a wine or a must depend on the reliability and the precision of the analytical data. 

Selectivity of the UV method can be increased by the use of spectra derivatives [50]. Derivative spectrophotometry is a chemometric method in which classic UV spectra (zero-order spectra) are differentiated with respect to wavelength before being analyzed. It is much more selective and precise than classic UV spectroscopy [50]. Examples of the use of the spectra derivatives method in drug purity analysis are shown in Table 8.4. 

The examples enumerated above prove conclusively that chemometric techniques can be effectively employed for the elucidation of a large number of problems in chromatography, connected with the accurate and precise evaluation of large data matrices. 

Each of these questions can be answered provided that the data are collected with the appropriate level of precision. There are many specialized texts (Adams, 1995, Jolliffe, 1986, Malinowski and Howery, 1980, Pelikan et al, 1994) and review articles (Lavine, 2000) on chemometrics, and in the following section it is intended to address only those methods most commonly used in spectral analysis of chemorheological data using remote spectroscopy. 

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