Laser-induced breakdown spectroscopy (LIBS) spectra are characterized by a redundancy of information that makes the use of multivariate methods for building calibration surfaces particularly suited. In the previous chapter, the case of linear calibration surfaces in all the coordinates considered was discussed. However, many effects in LIBS may produce a nonlinear dependence of the signal from the concentration of the elements in the sample. In the past decades, several multivariate nonlinear chemometric methods have been proposed and successfully tested for LIBS analytical applications. In this chapter, we will present a general overview and some examples on the application of these techniques.

Nonlinear Methods

Poggialini Francesco;Campanella Beatrice;Legnaioli Stefano;Raneri Simona;Palleschi Vincenzo
2022

Abstract

Laser-induced breakdown spectroscopy (LIBS) spectra are characterized by a redundancy of information that makes the use of multivariate methods for building calibration surfaces particularly suited. In the previous chapter, the case of linear calibration surfaces in all the coordinates considered was discussed. However, many effects in LIBS may produce a nonlinear dependence of the signal from the concentration of the elements in the sample. In the past decades, several multivariate nonlinear chemometric methods have been proposed and successfully tested for LIBS analytical applications. In this chapter, we will present a general overview and some examples on the application of these techniques.
2022
Istituto di Chimica dei Composti OrganoMetallici - ICCOM -
9781119759614
LIBS
Quantitative analysis
Multivariate nonlinear algorithms
Artificial neural networks
Convolutional neural networks
Kalman filter
Calibration-free LIBS
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/416279
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