{"id":6645,"date":"2020-07-22T08:19:33","date_gmt":"2020-07-22T08:19:33","guid":{"rendered":"https:\/\/www.istp.cnr.it\/?post_type=product&#038;p=6645"},"modified":"2022-06-21T09:57:15","modified_gmt":"2022-06-21T09:57:15","slug":"macro-classification-of-meteorites-by-portable-energy-dispersive-x-ray-fluorescence-spectroscopy-ped-xrf-principal-component-analysis-pca-and-machine-learning-algorithms","status":"publish","type":"product","link":"https:\/\/www.istp.cnr.it\/it\/research-product\/macro-classification-of-meteorites-by-portable-energy-dispersive-x-ray-fluorescence-spectroscopy-ped-xrf-principal-component-analysis-pca-and-machine-learning-algorithms\/","title":{"rendered":"Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms"},"content":{"rendered":"<p>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 &#8220;meteor-wrongs&#8221;. 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Allegretta, Ignazio; Marangoni, Bruno; Manzari, Paola; Porfido, Carlo; Terzano, Roberto; De Pascale, Olga; Senesi, Giorgio S.<\/p>\n","protected":false},"featured_media":1294,"comment_status":"closed","ping_status":"open","template":"","meta":[],"product_cat":[574],"product_tag":[1016,1017,1018,1019,1020],"class_list":["post-6645","product","type-product","status-publish","has-post-thumbnail","hentry","product_cat-journal-articles","product_tag-meteorite-meteor-wrong","product_tag-portable","product_tag-ed-xrf","product_tag-pca","product_tag-machine-learning","prodpage-style2"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product\/6645","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/comments?post=6645"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/media\/1294"}],"wp:attachment":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/media?parent=6645"}],"wp:term":[{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product_cat?post=6645"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product_tag?post=6645"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}