{"title":"使用手持式光谱设备对钶钽铁矿石进行地理指纹识别","authors":"Samuel Kessinger, Jon Kellar, Prasoon Diwakar","doi":"10.1007/s42461-024-01030-1","DOIUrl":null,"url":null,"abstract":"<p>Following the enactment of the Dodd-Frank Act in 2010, specifically Sect. 1502, US companies have been required to report utilizing conflict minerals from the Democratic Republic of Congo (DRC). The conflict mineral coltan, an ore consisting of elements tantalum and niobium, is central to this issue and engenders the need to track and trace the mineral’s supply chain. X-ray fluorescence (XRF) and laser-induced breakdown spectroscopy (LIBS) have been used, in combination with both unsupervised and supervised machine learning, to accurately classify coltan samples with known provenances. Sample spectra were first used as input data into unsupervised machine learning clustering algorithms, upon which dendrogram and constellation plots were generated. The classification achieved via unsupervised machine learning provided the proof of concept necessary to further investigate classification using supervised machine learning algorithms. The sample’s raw spectra were then used to train a supervised machine learning algorithm, consisting of a voting classifier relying on the results from random forest classifier (RFC), linear regression classifier (LRC), support vector classifier (SVC), and multi-layer perceptron classifier (MLPC). The classification achieved using raw spectra was able to achieve accuracies up to ~ 97%. The samples’ raw spectra were pre-processed using principal component analysis (PCA), and the pre-processed data was fed into the same supervised machine learning classifier described above. Accuracies of ~ 98% and ~ 96%, respectively, were achieved. When reviewing the predicted classifications arising from the use of these two different types of spectra, specifically reviewing the confidence score associated with each predicted provenance classification, it was possible to account for the incorrect provenance classifications returned by the voting classifier. If the predicted provenance and associated confidence score obtained via each spectra type was compared to the resulting provenance prediction and confidence score obtained by the other spectra type, and only the prediction with the higher associated confidence score was used, classification accuracies of 100% could be achieved.</p>","PeriodicalId":18588,"journal":{"name":"Mining, Metallurgy & Exploration","volume":"19 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Geofingerprinting of Coltan Using Handheld Spectroscopic Devices\",\"authors\":\"Samuel Kessinger, Jon Kellar, Prasoon Diwakar\",\"doi\":\"10.1007/s42461-024-01030-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Following the enactment of the Dodd-Frank Act in 2010, specifically Sect. 1502, US companies have been required to report utilizing conflict minerals from the Democratic Republic of Congo (DRC). The conflict mineral coltan, an ore consisting of elements tantalum and niobium, is central to this issue and engenders the need to track and trace the mineral’s supply chain. X-ray fluorescence (XRF) and laser-induced breakdown spectroscopy (LIBS) have been used, in combination with both unsupervised and supervised machine learning, to accurately classify coltan samples with known provenances. Sample spectra were first used as input data into unsupervised machine learning clustering algorithms, upon which dendrogram and constellation plots were generated. The classification achieved via unsupervised machine learning provided the proof of concept necessary to further investigate classification using supervised machine learning algorithms. The sample’s raw spectra were then used to train a supervised machine learning algorithm, consisting of a voting classifier relying on the results from random forest classifier (RFC), linear regression classifier (LRC), support vector classifier (SVC), and multi-layer perceptron classifier (MLPC). The classification achieved using raw spectra was able to achieve accuracies up to ~ 97%. The samples’ raw spectra were pre-processed using principal component analysis (PCA), and the pre-processed data was fed into the same supervised machine learning classifier described above. Accuracies of ~ 98% and ~ 96%, respectively, were achieved. When reviewing the predicted classifications arising from the use of these two different types of spectra, specifically reviewing the confidence score associated with each predicted provenance classification, it was possible to account for the incorrect provenance classifications returned by the voting classifier. If the predicted provenance and associated confidence score obtained via each spectra type was compared to the resulting provenance prediction and confidence score obtained by the other spectra type, and only the prediction with the higher associated confidence score was used, classification accuracies of 100% could be achieved.</p>\",\"PeriodicalId\":18588,\"journal\":{\"name\":\"Mining, Metallurgy & Exploration\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mining, Metallurgy & Exploration\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s42461-024-01030-1\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mining, Metallurgy & Exploration","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42461-024-01030-1","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
Geofingerprinting of Coltan Using Handheld Spectroscopic Devices
Following the enactment of the Dodd-Frank Act in 2010, specifically Sect. 1502, US companies have been required to report utilizing conflict minerals from the Democratic Republic of Congo (DRC). The conflict mineral coltan, an ore consisting of elements tantalum and niobium, is central to this issue and engenders the need to track and trace the mineral’s supply chain. X-ray fluorescence (XRF) and laser-induced breakdown spectroscopy (LIBS) have been used, in combination with both unsupervised and supervised machine learning, to accurately classify coltan samples with known provenances. Sample spectra were first used as input data into unsupervised machine learning clustering algorithms, upon which dendrogram and constellation plots were generated. The classification achieved via unsupervised machine learning provided the proof of concept necessary to further investigate classification using supervised machine learning algorithms. The sample’s raw spectra were then used to train a supervised machine learning algorithm, consisting of a voting classifier relying on the results from random forest classifier (RFC), linear regression classifier (LRC), support vector classifier (SVC), and multi-layer perceptron classifier (MLPC). The classification achieved using raw spectra was able to achieve accuracies up to ~ 97%. The samples’ raw spectra were pre-processed using principal component analysis (PCA), and the pre-processed data was fed into the same supervised machine learning classifier described above. Accuracies of ~ 98% and ~ 96%, respectively, were achieved. When reviewing the predicted classifications arising from the use of these two different types of spectra, specifically reviewing the confidence score associated with each predicted provenance classification, it was possible to account for the incorrect provenance classifications returned by the voting classifier. If the predicted provenance and associated confidence score obtained via each spectra type was compared to the resulting provenance prediction and confidence score obtained by the other spectra type, and only the prediction with the higher associated confidence score was used, classification accuracies of 100% could be achieved.
期刊介绍:
The aim of this international peer-reviewed journal of the Society for Mining, Metallurgy & Exploration (SME) is to provide a broad-based forum for the exchange of real-world and theoretical knowledge from academia, government and industry that is pertinent to mining, mineral/metallurgical processing, exploration and other fields served by the Society.
The journal publishes high-quality original research publications, in-depth special review articles, reviews of state-of-the-art and innovative technologies and industry methodologies, communications of work of topical and emerging interest, and other works that enhance understanding on both the fundamental and practical levels.