Chemometrics signal variations

The acoustic spectra were recorded simultaneously as other process experiments, in themselves not related to acoustic chemometrics, were carried out. This resulted in many days with stable conditions in the reactor, and no particular variations in the acoustic signals. Therefore there were only a limited number of days (hours) which displayed significant variation in process parameters, which are necessary for successful multivariate analysis and calibration. 

Kohler, A., Zimonja, M., Segtnan, V., and Martens, H. (2009). Standard normal variate, multiplicative signal correction and extended multiplicative signal correction preprocessing in biospectroscopy. In "Comprehensive Chemometrics", (S. D. Brown, R. Tauler, and B. Walczak, Eds), Vol. 2, pp. 139-162. Elsevier, Amsterdam. 

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 contribution of equipment drift is very smalt as discussed in the chemometrics section. To illustrate this, consider the variability of a real-time NIR signal from a ribbon as it is compacted on the Fitzpatrick IRS20 as shown in Figure 22. The variation in slope is on the order of 0.5 units introducing only a small error in the measurement. Of course once quantified this contribution can be corrected for in the data. 

Wold et al. [59] introduced a chemometric method named orthogonal signal correction (OSC) in 1998. The purpose of OSC is to remove strong structured variation (OSC components) from spectra... 

---