The most common way to analyze PALS spectra involves fitting a parameter-dependent model to the experimental data. Traditionally, this fit involves local non-linear optimization routines that depend on a reasonable initial guess for the searched parameters. This, together with the fact that very different sets of parameters may yield indistinguishably good fits for a given experimental spectrum, gives rise to ambiguities in the data analysis in most but the simplest cases. In order to alleviate these difficulties, a computer program named PAScual was developed that incorporates 2 advanced algorithms to provide a robust fitting tool: on the one hand, it incorporates a global non-linear optimization routine based on a Simulated Annealing algorithm and, on the other hand, it yields information on the reliability of the results by means of a Markov Chain Monte-Carlo Bayesian Inference method. In this work the methods used in PAScual are described and tested against both simulated and experimental spectra, comparing the results with those from the well-established program LTv9. The examples focus on the type of complex data those results from the study of self-assembled amphiphile materials containing co-existing aqueous and hydrocarbon regions.
|Pages (from-to)||456 - 466|
|Number of pages||11|
|Journal||Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment|
|Publication status||Published - 2009|