{"id":6434,"date":"2021-02-08T13:22:00","date_gmt":"2021-02-08T12:22:00","guid":{"rendered":"https:\/\/ssef.ch\/?p=6434"},"modified":"2022-11-28T09:57:35","modified_gmt":"2022-11-28T08:57:35","slug":"new-research-on-emerald-origin-determination","status":"publish","type":"post","link":"https:\/\/www.ssef.ch\/jp\/new-research-on-emerald-origin-determination\/","title":{"rendered":"NEW RESEARCH ON EMERALD ORIGIN DETERMINATION"},"content":{"rendered":"\n<p>Origin determination of emeralds is a fascinating subject. In our efforts to gain greater insights into gemstones from different origins, SSEF has developed a new machine-learning-assisted chemical data visualization approach for emerald characterisation.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"946\" height=\"473\" src=\"https:\/\/ssef.ch\/wp-content\/uploads\/2021\/02\/General-Figure.jpg\" alt=\"\" class=\"wp-image-6438\" srcset=\"https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/General-Figure.jpg 946w, https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/General-Figure-300x150.jpg 300w, https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/General-Figure-768x384.jpg 768w, https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/General-Figure-700x350.jpg 700w\" sizes=\"(max-width: 946px) 100vw, 946px\" \/><\/figure>\n\n\n\n<p>The case study\ncompared 168 emeralds from different origins using a t-SNE (t-distributed\nstochastic neighbor embedding) algorithm. The image below shows how through\ncompilation of56 elements in the t-SNE calculation, clustered groups of\nemeralds from different origins can be characterised.<\/p>\n\n\n\n<p>This type of approach\nwill never replace the need for human gemmologists, but provides a deeper level\nof understanding into how emeralds form and the chemical fingerprinting of\nemeralds.<\/p>\n\n\n\n<p>This research has just been published in <a href=\"https:\/\/pubs.rsc.org\/en\/content\/articlelanding\/2021\/ja\/d0ja00484g#!divAbstract\">Journal of Analytical Atomic Spectrometry<\/a> of the Royal Society of Chemistry in the UK. <\/p>\n\n\n\n<p>The full article can be downloaded <a href=\"https:\/\/pubs.rsc.org\/en\/content\/articlelanding\/2021\/ja\/d0ja00484g#!divAbstract\">here<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"560\" src=\"https:\/\/ssef.ch\/wp-content\/uploads\/2021\/02\/Emerald-1024x560.png\" alt=\"\" class=\"wp-image-6441\" srcset=\"https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/Emerald-1024x560.png 1024w, https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/Emerald-300x164.png 300w, https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/Emerald-768x420.png 768w, https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/Emerald-700x383.png 700w, https:\/\/www.ssef.ch\/wp-content\/uploads\/2021\/02\/Emerald.png 1064w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<hr class=\"wp-block-separator\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Want to learn more about emeralds?<\/h2>\n\n\n\n<div class=\"wp-block-media-text alignwide\"><figure class=\"wp-block-media-text__media\"><img decoding=\"async\" src=\"https:\/\/ssef.ch\/wp-content\/uploads\/2021\/07\/402_Fig1_emerald_Riverina.jpg\" alt=\"\" class=\"wp-image-7292\"\/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Sign up for our free online course: <a href=\"https:\/\/ssef.ch\/courses\/emerald-en\/\">Introduction to emeralds<\/a><\/p>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-background has-vivid-red-background-color\" href=\"https:\/\/ssef.ch\/courses\/emerald-en\/\">Sign up for free<\/a><\/div>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Origin determination of emeralds is a fascinating subject. In our efforts to gain greater insights into gemstones from different origins, SSEF has developed a new machine-learning-assisted chemical data visualization approach for emerald characterisation. The case study compared 168 emeralds from different origins using a t-SNE (t-distributed stochastic neighbor embedding) algorithm. The image below shows how [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":6435,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[6],"tags":[],"library_category":[],"library_author":[],"class_list":["post-6434","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>NEW RESEARCH ON EMERALD ORIGIN DETERMINATION - SSEF<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ssef.ch\/new-research-on-emerald-origin-determination\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NEW RESEARCH ON EMERALD ORIGIN DETERMINATION - SSEF\" \/>\n<meta property=\"og:description\" content=\"Origin determination of emeralds is a fascinating subject. In our efforts to gain greater insights into gemstones from different origins, SSEF has developed a new machine-learning-assisted chemical data visualization approach for emerald characterisation. The case study compared 168 emeralds from different origins using a t-SNE (t-distributed stochastic neighbor embedding) algorithm. 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