Residual-based localization and quantification of peaks in x-ray diffractograms

TL;DR

This paper introduces a residual-based method using the taut string technique to accurately identify and quantify peaks in x-ray diffractograms, including baseline estimation and peak shape modeling.

Contribution

It presents a novel residual-based approach combining taut string and smoothing splines for peak detection and baseline correction in x-ray diffraction data.

Findings

Effective peak detection and quantification demonstrated

Accurate baseline estimation achieved

Method outperforms traditional techniques in noise handling

Abstract

We consider data consisting of photon counts of diffracted x-ray radiation as a function of the angle of diffraction. The problem is to determine the positions, powers and shapes of the relevant peaks. An additional difficulty is that the power of the peaks is to be measured from a baseline which itself must be identified. Most methods of de-noising data of this kind do not explicitly take into account the modality of the final estimate. The residual-based procedure we propose uses the so-called taut string method, which minimizes the number of peaks subject to a tube constraint on the integrated data. The baseline is identified by combining the result of the taut string with an estimate of the first derivative of the baseline obtained using a weighted smoothing spline. Finally, each individual peak is expressed as the finite sum of kernels chosen from a parametric family.