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Advancing liquid scintillation spectra deconvolution for radiological characterization: comparative techniques for
M Pérez-Baeza1,2, S Carlos3, D Ginestar4
1Laboratorio de Radiactividad Ambiental, Universitat Politècnica de València, Cami de Vera s/n, València, 46022, València, Spain. mperbae@upvnet.upv.es.
Analytical and Bioanalytical Chemistry
|February 20, 2026
Summary
Faster radioactivity analysis is crucial for effective control. This study developed mathematical deconvolution methods using spectral filtering and multivariate calibration for rapid radioisotope identification and quantification, achieving accurate results.
Area of Science:
- Nuclear Chemistry and Physics
- Analytical Chemistry
- Spectroscopy
Background:
- Effective radioactivity control necessitates faster analysis processing times.
- Existing protocols require updates to meet the demand for rapid radioisotope analysis.
- Mathematical deconvolution offers a pathway to separate complex isotope spectra.
Purpose of the Study:
- To develop and evaluate mathematical deconvolution techniques for faster radioisotope identification and quantification.
- To assess the accuracy of spectral filtering and multivariate statistical calibration methods in radioactivity analysis.
- To determine the optimal combination of techniques for predicting the activity of beta emitters.
Main Methods:
- Spectral filtering techniques including Fourier series, Laguerre polynomials, Legendre polynomials, and Savitzky-Golay filter were employed.
- Multivariate statistical calibration methods such as least squares and partial least squares regressions were used for deconvolution.
- The methods were applied to predict the activity of beta emitters (63Ni, 55Fe, 14C, 3H, 89Sr, 90Sr) using liquid scintillation counting.
Main Results:
- All analyzed techniques provided accurate results, with relative biases generally below 20% for activity calculations.
- Spectral reconstruction errors were found to be a maximum of approximately 4.5%.
- The combination of Legendre or Laguerre polynomials with partial least squares regression yielded the best performance.
Conclusions:
- Mathematical deconvolution methods, particularly using Legendre or Laguerre polynomials and partial least squares regression, significantly improve radioisotope analysis speed and accuracy.
- These advanced techniques enable faster identification and quantification of radioisotopes, crucial for radioactivity control.
- The developed protocols offer a reliable approach for predicting the activity of various beta emitters.
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