An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis

作者：D'Andrea Eleonora; Pagnotta Stefano; Grifoni Emanuela; Lorenzetti Giulia; Legnaioli Stefano; Palleschi Vincenzo^*; Lazzerini Beatrice

来源：Spectrochimica Acta Part B: Atomic Spectroscopy , 2014, 99: 52-58.

DOI：10.1016/j.sab.2014.06.012

• Laser-induced breakdown spectroscopy
• Artificial neural network
• Quantitative analysis
• Bronze

The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a 'black box' system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples.

• 出版日期2014-9-1