Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS

Ba-based ion interference with Eu in coal and coal combustion products during quadrupole-based inductively coupled plasma mass spectrometry procedures is problematic. Thus, this paper proposes machine-learning-based prediction models for determination of the threshold value of Ba interference with E...

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Main Authors: Na Xu, Qing Li
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Minerals
Subjects:
Online Access:https://www.mdpi.com/2075-163X/9/5/259
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spelling doaj-63239c8db2bd42129cad45d4477263112020-11-25T02:11:58ZengMDPI AGMinerals2075-163X2019-04-019525910.3390/min9050259min9050259Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MSNa Xu0Qing Li1College of Geoscience and Survey Engineering, China University of Mining and Technology, Beijing 100083, ChinaDepartment of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, Hong Kong, ChinaBa-based ion interference with Eu in coal and coal combustion products during quadrupole-based inductively coupled plasma mass spectrometry procedures is problematic. Thus, this paper proposes machine-learning-based prediction models for determination of the threshold value of Ba interference with Eu, which can be used to predict such interference in coal. The models are trained for Eu, Ba, Ba/Eu, and Ba interference with Eu. Under different user-defined parameters, different prediction models based on the corresponding model tree can be applied to Ba interference with Eu. We experimentally show the effectiveness of these different prediction models and find that, when the Ba/Eu value is less than 2950, the Ba-Eu interference prediction model is <inline-formula> <math display="inline"> <semantics> <mrow> <mi>y</mi> <mo>=</mo> <mo>&#8722;</mo> <mn>0.18419411</mn> <mo>+</mo> <mn>0.00050737</mn> <mo>&#215;</mo> <mi>x</mi> <mo>,</mo> <mtext>&nbsp;</mtext> <mn>0</mn> <mo>&lt;</mo> <mi>x</mi> <mo>&lt;</mo> <mn>2950</mn> <mo>.</mo> </mrow> </semantics> </math> </inline-formula> Further, when the Ba/Eu value is between 2950 and 189,523, the Ba-Eu interference prediction model of <inline-formula> <math display="inline"> <semantics> <mrow> <mi>y</mi> <mtext>&nbsp;</mtext> <mo>=</mo> <mtext>&nbsp;</mtext> <mn>0.293982186</mn> <mtext>&nbsp;</mtext> <mo>+</mo> <mtext>&nbsp;</mtext> <mn>0.00000181729975</mn> <mtext>&nbsp;</mtext> <mo>&#215;</mo> <mtext>&nbsp;</mtext> <mi>x</mi> <mo>,</mo> <mtext>&nbsp;</mtext> <mn>2950</mn> <mtext>&nbsp;</mtext> <mo>&lt;</mo> <mtext>&nbsp;</mtext> <mi>x</mi> <mtext>&nbsp;</mtext> <mo>&lt;</mo> <mtext>&nbsp;</mtext> <mn>189</mn> <mo>,</mo> <mn>523</mn> </mrow> </semantics> </math> </inline-formula> yields the best result. Based on the optimal model, a threshold value of 363 is proposed; i.e., when the Ba/Eu value is less than 363, Ba interference with Eu can be neglected during Eu data interpretation. Comparison of this threshold value with a value proposed in earlier works reveals that the proposed prediction model better determines the threshold value for Ba interference with Eu.https://www.mdpi.com/2075-163X/9/5/259europiumICP-Q-MSpolyatomic ion inferencecoalmachine learningregression
collection DOAJ
language English
format Article
sources DOAJ
author Na Xu
Qing Li
spellingShingle Na Xu
Qing Li
Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS
Minerals
europium
ICP-Q-MS
polyatomic ion inference
coal
machine learning
regression
author_facet Na Xu
Qing Li
author_sort Na Xu
title Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS
title_short Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS
title_full Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS
title_fullStr Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS
title_full_unstemmed Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS
title_sort threshold value determination using machine learning algorithms for ba interference with eu in coal and coal combustion products by icp-ms
publisher MDPI AG
series Minerals
issn 2075-163X
publishDate 2019-04-01
description Ba-based ion interference with Eu in coal and coal combustion products during quadrupole-based inductively coupled plasma mass spectrometry procedures is problematic. Thus, this paper proposes machine-learning-based prediction models for determination of the threshold value of Ba interference with Eu, which can be used to predict such interference in coal. The models are trained for Eu, Ba, Ba/Eu, and Ba interference with Eu. Under different user-defined parameters, different prediction models based on the corresponding model tree can be applied to Ba interference with Eu. We experimentally show the effectiveness of these different prediction models and find that, when the Ba/Eu value is less than 2950, the Ba-Eu interference prediction model is <inline-formula> <math display="inline"> <semantics> <mrow> <mi>y</mi> <mo>=</mo> <mo>&#8722;</mo> <mn>0.18419411</mn> <mo>+</mo> <mn>0.00050737</mn> <mo>&#215;</mo> <mi>x</mi> <mo>,</mo> <mtext>&nbsp;</mtext> <mn>0</mn> <mo>&lt;</mo> <mi>x</mi> <mo>&lt;</mo> <mn>2950</mn> <mo>.</mo> </mrow> </semantics> </math> </inline-formula> Further, when the Ba/Eu value is between 2950 and 189,523, the Ba-Eu interference prediction model of <inline-formula> <math display="inline"> <semantics> <mrow> <mi>y</mi> <mtext>&nbsp;</mtext> <mo>=</mo> <mtext>&nbsp;</mtext> <mn>0.293982186</mn> <mtext>&nbsp;</mtext> <mo>+</mo> <mtext>&nbsp;</mtext> <mn>0.00000181729975</mn> <mtext>&nbsp;</mtext> <mo>&#215;</mo> <mtext>&nbsp;</mtext> <mi>x</mi> <mo>,</mo> <mtext>&nbsp;</mtext> <mn>2950</mn> <mtext>&nbsp;</mtext> <mo>&lt;</mo> <mtext>&nbsp;</mtext> <mi>x</mi> <mtext>&nbsp;</mtext> <mo>&lt;</mo> <mtext>&nbsp;</mtext> <mn>189</mn> <mo>,</mo> <mn>523</mn> </mrow> </semantics> </math> </inline-formula> yields the best result. Based on the optimal model, a threshold value of 363 is proposed; i.e., when the Ba/Eu value is less than 363, Ba interference with Eu can be neglected during Eu data interpretation. Comparison of this threshold value with a value proposed in earlier works reveals that the proposed prediction model better determines the threshold value for Ba interference with Eu.
topic europium
ICP-Q-MS
polyatomic ion inference
coal
machine learning
regression
url https://www.mdpi.com/2075-163X/9/5/259
work_keys_str_mv AT naxu thresholdvaluedeterminationusingmachinelearningalgorithmsforbainterferencewitheuincoalandcoalcombustionproductsbyicpms
AT qingli thresholdvaluedeterminationusingmachinelearningalgorithmsforbainterferencewitheuincoalandcoalcombustionproductsbyicpms
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