Chapter 13 - Automated Resolution of Multiexponential Decay Data

Abstract

This chapter discusses automated resolution of multiexponential decay data. It provides a model composed of sums of exponentials for mixtures of species that are all undergoing first-order decay with lifetimes. This model requires a judgment concerning the correct number of exponentials that best fit the data. Resolution of multiple exponentials becomes difficult when three or more exponentials overlap. The chapter provides automated analysis of data with an unknown number of exponentials based on deviation pattern recognition to find the number of decaying species in luminescence or radioactive decay. Deviation pattern recognition was developed as a basis for decisions concerning the best model for a set of data. This technique employs the shapes of residual or deviation plots obtained after a regression analysis to make automated decisions about goodness of fit. Deviation patterns for fits onto a given model can be generated by nonlinear regression analysis of noise-free theoretical data sets. Information about these can be coded into the computer and used as a basis for construction, the basis of a “decision tree” on which to base decisions in an expert system.