Heuristic and Statistical Algorithms for Automated Emission Spectral Background Intensity Estimation

Abstract

Two algorithms for the complete automation of background estimation in ICP emission spectroscopy are presented and evaluated. One of these algorithms is based on heuristic spectral interpretation, while the other is based on statistical spectral interpretation. These algorithms both address the weaknesses of the conventionally employed approaches of blank subtraction in calibration and background estimation through interpolation from analyst-selected wavelengths adjacent to the analyte peak. In a rigorous evaluation with synthetic spectra, these algorithms are characterized for performance in terms of accuracy, precision, and robustness. As a demonstration of the algorithms' performance with experimentally measured spectra, a determination of uranium in the presence of a calcium background interference is performed. These algorithms require no analyst interaction for their operation, and they estimate the background for every spectrum measured.

Keywords

Emission spectroscopy Spectral interpretation Background correction Baseline fitting Computer algorithms Synthetic spectra experiments Inductively coupled plasma

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