Neural network-random generation approach to analyze X-ray fluorescence spectra

Abstract

Application of a feed-forward multilayer perceptron neural network to analyze X-ray fluorescence spectra of a compound material is investigated. For this purpose, a general approach is proposed to improve the learning process of the neural network. In this approach, randomly generated composite spectra are constructed from the set of reference spectra corresponding to the pure elements which compose the material. Instead of taking the spectrum as a whole, the input parameters of the neural network arereduced to the neighboring bins of each pure element peak. Bytesting themethodon the spectra of materials made of Ca, Fe, Ni and Cu elements, we obtained an efficiency of about 95% for predicting the relative concentrations of the four elements in the material. Multivariate Data Analysis refers to any statistical technique used to analyze data that arise from more than one variable.Multivariate classification methods based on machine learning techniques have become a fundamental ingredient to most analyses. The multivariate classifiers themselves have signifi- cantly evolved in recent years. Among the multivariate classification methods, Artificial Neural Networks (ANN) are widely used in various fields. Applications range from image analysis to financial movements predictions and analysis. In (1,8) a review of applications of ANN and its potentialities in atomic and nuclear physics is shown. In high energy physic, ANNis mainly used for classification tasks such assignal over background discrimi- nation. With the search for ever smaller signals in ever larger data sets, it has become essential to extract a maximum of the available information from the data (2). ANN are also applied in other cases where strong non-lineal effects are present like in the spectral analyses generated in analytical techniques like PIXE (3) and XRF (X-ray fluorescence) (4). Several types of artificial neural network are commonly used in the practice. Among these types, we can mention the multi layer perceptrons (MLP), the radial basis function networks (RBF) and theself- organizing maps (SOM). They differ by their processing elements and bythe specific manner in which these elements are connected. The potentialities of ANN seem to be more convenient in the repetitive analysis of many spectra presenting similar patterns with specificdifferences. A typical case is presented in the elementalanalysis of samples of unknown composition using energy dispersiveXRF spectroscopy.Their respective spectra