What is spectral deconvolution?

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What is spectral deconvolution?

Spectral deconvolution is the mathematical separation of signals that are blended in an XRF spectrum. When peaks from different elements lie close together or broaden, their counts merge. Deconvolution fits a model composed of the individual peak shapes (like Gaussian or pseudo-Voigt) to the measured spectrum and solves for how much each peak contributes. This allows you to quantify each element even when their peaks overlap, improving accuracy in crowded spectra or thick samples. It’s different from adjusting energy calibration (which uses standards to align energy scales), estimating background (which is about the background under peaks), or converting counts to concentration with a calibration curve (which translates counts to amounts after peaks are separated).

Spectral deconvolution is the mathematical separation of signals that are blended in an XRF spectrum. When peaks from different elements lie close together or broaden, their counts merge. Deconvolution fits a model composed of the individual peak shapes (like Gaussian or pseudo-Voigt) to the measured spectrum and solves for how much each peak contributes. This allows you to quantify each element even when their peaks overlap, improving accuracy in crowded spectra or thick samples. It’s different from adjusting energy calibration (which uses standards to align energy scales), estimating background (which is about the background under peaks), or converting counts to concentration with a calibration curve (which translates counts to amounts after peaks are separated).

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