Beatriz Galindo-Prieto, Ian S. Mudway, Johan Linderholm, Paul Geladi
{"title":"Multi-block chemometric approaches to the unsupervised spectral classification of geological samples","authors":"Beatriz Galindo-Prieto, Ian S. Mudway, Johan Linderholm, Paul Geladi","doi":"arxiv-2409.04466","DOIUrl":null,"url":null,"abstract":"In this paper, the potential use of multi-block chemometric methods to\nprovide improved unsupervised classification of compositionally complex\nmaterials through the integration of multi-modal spectrometric data sets (one\nXRF, two NIR, and two FT-Raman) was tested. We concluded that multi-block HPLS\nmodels are effective at combining multi-modal spectrometric data to provide a\nmore comprehensive classification of compositionally complex samples, and VIP\ncan reduce HPLS model complexity, while increasing its data interpretability.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"43 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Geophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.04466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, the potential use of multi-block chemometric methods to
provide improved unsupervised classification of compositionally complex
materials through the integration of multi-modal spectrometric data sets (one
XRF, two NIR, and two FT-Raman) was tested. We concluded that multi-block HPLS
models are effective at combining multi-modal spectrometric data to provide a
more comprehensive classification of compositionally complex samples, and VIP
can reduce HPLS model complexity, while increasing its data interpretability.