Chemometric Processing of X‐Ray Fluorescence Data

The processing of spectrometric data is one of the most important steps in X-ray fluorescence (XRF) experiments. Chemometric data processing tools are gaining wide acceptance in modern XRF studies. The application of chemometrics in XRF will be illustrated with the examples from biological sciences. This chapter describes the main chemometric tools applied for XRF data processing. It considers most popular methods: principal component analysis, hierarchical cluster analysis, and partial least squares. There are many other powerful techniques in multivariate analysis that can be employed for effective processing of XRF data. An important tool of exploratory data analysis is a group of methods which is called factor analysis. Another important group of chemometric methods is artificial neural networks — a simplified mathematical way to simulate the learning processes of the human brain. There are many different chemometric tools developed to address various problems of data processing in analytical chemistry.