Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a ‘black box’ approach and how to avoid them

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/74629
Información del item - Informació de l'item - Item information
Title: Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a ‘black box’ approach and how to avoid them
Authors: Safi, Ali | Campanella, Beatrice | Grifoni, Emanuela | Legnaioli, Stefano | Lorenzetti, Giulia | Pagnotta, Stefano | Poggialini, Francesco | Ripoll-Seguer, Laura | Hidalgo, Montserrat | Palleschi, Vincenzo
Research Group/s: Espectroscopía Atómica-Masas y Química Analítica en Condiciones Extremas
Center, Department or Service: Universidad de Alicante. Departamento de Química Analítica, Nutrición y Bromatología | Universidad de Alicante. Instituto Universitario de Materiales
Keywords: LIBS | Cast iron | Calibration curves, Partial Least Squares Analysis | Artificial Neural Networks
Knowledge Area: Química Analítica
Issue Date: Jun-2018
Publisher: Elsevier
Citation: Spectrochimica Acta Part B: Atomic Spectroscopy. 2018, 144: 46-54. doi:10.1016/j.sab.2018.03.007
Abstract: The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as ‘black boxes’ whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a ‘black box’ approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.
URI: http://hdl.handle.net/10045/74629
ISSN: 0584-8547 (Print) | 1873-3565 (Online)
DOI: 10.1016/j.sab.2018.03.007
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier B.V.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.sab.2018.03.007
Appears in Collections:INV - SP-BG - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2018_Safi_etal_SpectrActaB_final.pdfVersión final (acceso restringido)1,69 MBAdobe PDFOpen    Request a copy
Thumbnail2018_Safi_etal_SpectrActaB_accepted.pdfEmbargo 24 meses (acceso abierto: 18 marzo 2020)1,2 MBAdobe PDFOpen    Request a copy


Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.