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Statistical comparison of predictive models for quantitative analysis and classification in the framework of LIBS spectroscopy: A tutorial

hal.structure.identifierLaboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
dc.contributor.authorDUPONCHEL, Ludovic
hal.structure.identifierGeoRessources
hal.structure.identifierFaculté des Sciences et Technologies [Université de Lorraine] [FST ]
dc.contributor.authorFABRE, Cécile
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorBOUSQUET, Bruno
hal.structure.identifierInstitut Lumière Matière [Villeurbanne] [ILM]
dc.contributor.authorMOTTO-ROS, Vincent
dc.date.issued2023
dc.identifier.issn0584-8547
dc.description.abstractEnLaser-Induced Breakdown Spectroscopy (LIBS) is a widely accepted technique used for both classification and quantification purposes considering complex and heterogeous samples. Based on a set of training spectra acquired from diverse and representative samples within a specific application domain, it becomes possible to apply various data processing techniques and modeling methods to construct the predictive model in question. Naturally the complexity of both the laser-matter and the laser-plasma interactions and the heterogeneity of natural samples often requires the development of various predictive models, which are then compared based on figures of merit such as the RMSEP (Root Mean Square Error of Prediction) value for quantification or the classification rate for qualitative analysis. Our ultimate goal is, of course, to select the model that appears to be the most accurate, which ultimately boils down to searching for the lowest RMSEP value or the highest classification rate. This is precisely where the whole problem lies because even if we observe a different level of error for two models, for example, this difference is not necessarily statistically significant. In such a case, we are therefore not allowed to say that the lower error indicates the best predictive model to consider. The purpose of this article is to provide a tutorial on introducing a statistical model comparison procedure, whether they are quantitative or qualitative. Two LIBS data sets have been used to illustrate the principles of the proposed method.
dc.description.sponsorshipMéthode pour la datation des mortiers de chaux archéologiques : caractérisation, extraction, datation, validation - ANR-22-CE27-0017
dc.description.sponsorshipDiagnostique médical par intelligence artificielle appliquée à la microscopie LIBS élémentaire - ANR-20-CE17-0021
dc.language.isoen
dc.publisherElsevier
dc.subject.enLaser-induced breakdown spectroscopy (LIBS)
dc.subject.enQuantitative analysis
dc.subject.enClassification
dc.subject.enModel comparison
dc.subject.enStatitiscal test
dc.subject.enSignificance
dc.subject.enChemometrics
dc.title.enStatistical comparison of predictive models for quantitative analysis and classification in the framework of LIBS spectroscopy: A tutorial
dc.typeArticle de revue
dc.identifier.doi10.1016/j.sab.2023.106776
dc.subject.halChimie/Matériaux
dc.subject.halChimie/Chimie théorique et/ou physique
dc.subject.halStatistiques [stat]/Applications [stat.AP]
bordeaux.journalSpectrochimica Acta Part B: Atomic Spectroscopy
bordeaux.page106776
bordeaux.volume208
bordeaux.peerReviewedoui
hal.identifierhal-04191568
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04191568v1
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