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Cho, Kyung Hwa
Water-Environmental Informatics Lab.
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A novel method for micropollutant quantification using deep learning and multi-objective optimization

Author(s)
Yun, DaeunKang, DaehoJang, JiyiAngeles, Anne TheresePyo, JongCheolJeon, JunhoBaek, Sang-SooCho, Kyung Hwa
Issued Date
2022-04
DOI
10.1016/j.watres.2022.118080
URI
https://scholarworks.unist.ac.kr/handle/201301/57652
Fulltext
https://linkinghub.elsevier.com/retrieve/pii/S0043135422000434
Citation
WATER RESEARCH, v.212, pp.118080
Abstract
Micropollutants (MPs) released into aquatic ecosystems have adverse effects on public health. Hence, monitoring and managing MPs in aquatic systems are imperative. MPs can be quantified by high-resolution mass spectrometry (HRMS) with stable isotope-labeled (SIL) standards. However, high cost of SIL solutions is a significant issue. This study aims to develop a rapid and cost-effective analytical approach to estimate MP concentrations in aquatic systems based on deep learning (DL) and multi-objective optimization. We hypothesized that internal standards could quantify the MP concentrations other than the target substance. Our approach considered the precision of intra-/inter-day repeatability and natural organic matter information to reduce instrumental error and matrix effect. We selected standard solutions to estimate the concentrations of 18 MPs. Among the optimal DL models, DarkNet-53 using nine standard solutions yielded the highest performance, while ResNet-50 yielded the lowest. Overall, this study demonstrated the capability of DL models for estimating MP concentrations.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
ISSN
0043-1354
Keyword (Author)
MicropollutantSurrogate methodHigh resolution mass spectrometryDeep learningConvolutional neural network
Keyword
PHARMACEUTICAL COMPOUNDSGENETIC ALGORITHMMASS-SPECTROMETRYORGANIC-MATTERNEURAL-NETWORKLC-MS/MSWATERRIVERSTANDARDSPLASMA

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