Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms
Ignazio Allegretta, Bruno Marangoni, Paola Manzari, Carlo Porfido, Roberto Terzano, Olga De Pascale, Giorgio S.Senesi
Talanta
Available online 25 January 2020
“Highlights
• pED-XRF analysis were used to discriminate and classify between meteorites and meteor-wrongs.
• PCA of the whole ED-XRF spectrum was used to find the class clusterization in the scores plot.
• Five types of machine learning algorithms were tested to classify the samples into macro-groups.”
“The research on meteorites from hot and cold deserts is gaining advantages from the recent improvements of portable technologies such as X-ray fluorescence spectroscopy (XRF). The main advantages of portable instruments include the fast recognition of meteorites through their classification in macro-groups and discrimination from materials such as industrial slags, desert varnish covered rocks and iron oxides, named “meteor-wrongs”. In this study, 18 meteorite samples of different nature and origin were discriminated and preliminarily classified into characteristic macro-groups: iron meteorites, stony meteorites and meteor-wrongs, combining a portable energy dispersive XRF instrument (pED-XRF), principal component analysis (PCA) and some machine learning algorithms applied to the XRF spectra. The results showed that 100% accuracy in sample classification was obtained by applying the cubic support vector machine (CSVM), fine kernel nearest neighbor (FKNN), subspace discriminant-ensemble classifiers (SD-EC) and subspace discriminant KNN-EC (SKNN-EC) algorithms on standardized spectra.”
