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A Weighted-LSM Method to Improve Classification and Concentration Evaluation from Laser-Induced Fluorescence Spectra

The detection of biological agents using optical systems is an open field of research. Currently, different spectroscopic techniques allow to detect and classify chemical agents while a fast and accurate technique able to identify biological agents is still under investigation. Some optical techniqu...

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Detalles Bibliográficos
Autores principales: Gabbarini, Valentina, Puleio, Alessandro, Rossi, Riccardo, Malizia, Andrea, Gaudio, Pasqualino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607546/
https://www.ncbi.nlm.nih.gov/pubmed/36298072
http://dx.doi.org/10.3390/s22207721
Descripción
Sumario:The detection of biological agents using optical systems is an open field of research. Currently, different spectroscopic techniques allow to detect and classify chemical agents while a fast and accurate technique able to identify biological agents is still under investigation. Some optical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) or Laser-Induced Fluorescence (LIF), are already used as classification methods. However, the presence of background, spectrum similarities and other confounders make these techniques not very specific. This work shows a new method to achieve better performances in terms of classification and concentration evaluations. The method is based on the Weighted Least Square Minimization method. In fact, by using ad hoc weights, the LSM looks at specific features of the spectra, resulting in higher accuracy. In order to make a systematic analysis, numerical tests have been conducted. With these tests, the authors were able to highlight the various advantages and drawbacks of the new methodology proposed. Then, the method was applied to some LIF measurements to investigate the applicability of the method to preliminary experimental cases. The results show that, by using this new weighted LSM, it is possible to achieve better classification and concentration evaluation performances. Finally, the possible application of the new method is discussed.