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

1,* and 2
1
College of Geoscience and Survey Engineering, China University of Mining and Technology, Beijing 100083, China
2
Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, Hong Kong, China
*
Author to whom correspondence should be addressed.
Minerals 2019, 9(5), 259; https://doi.org/10.3390/min9050259
Received: 15 March 2019 / Revised: 24 April 2019 / Accepted: 24 April 2019 / Published: 29 April 2019
(This article belongs to the Collection Minerals in Coal and Coal Combustion Products)
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Abstract

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 y = 0.18419411 + 0.00050737 × x ,   0 < x < 2950 . Further, when the Ba/Eu value is between 2950 and 189,523, the Ba-Eu interference prediction model of y   =   0.293982186   +   0.00000181729975   ×   x ,   2950   <   x   <   189 , 523 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. View Full-Text
Keywords: europium; ICP-Q-MS; polyatomic ion inference; coal; machine learning; regression europium; ICP-Q-MS; polyatomic ion inference; coal; machine learning; regression
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Xu, N.; Li, Q. Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS. Minerals 2019, 9, 259.

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