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Citation: Lingyu Chang, Yanfang Lang, Yuyan Zhu, Jie Wang, Ying Guo, Die Wang, Peng Ding, Yueming Zhou, Zhixiang Gong, Shujuan Liu. Machine Learning-Optimized Microcolumn Ion Exchange Chromatography for Trace Arsenic Determination[J]. University Chemistry, ;2026, 41(1): 76-84. doi: 10.12461/PKU.DXHX202506023 shu

Machine Learning-Optimized Microcolumn Ion Exchange Chromatography for Trace Arsenic Determination

Lingyu Chang ¹ ,
Yanfang Lang ¹ ,
Yuyan Zhu ¹ ,
Jie Wang ¹ ,
Ying Guo ¹ ,
Die Wang ² ,
Peng Ding ³ ,
Yueming Zhou ^1,, ,
Zhixiang Gong ¹ ,
Shujuan Liu ¹

1.
School of Chemistry and Materials Science, East China University of Technology, Nanchang 330013, China;
2.
School of Modern Agriculture, Xiangxi Vocational and Technical College for Nationalities, Xiangxi 416099, Hunan Province, China;
3.
School of Information Engineering, East China University of Technology, Nanchang 330013, China

Corresponding author: Yueming Zhou, ymzhou@ecut.edu.cn
Received Date: 3 June 2025
Revised Date: 20 October 2025

Microcolumn ion exchange chromatography demands rigorous standardization and operational proficiency from students. This study investigates a machine vision-based approach to capture time-series behavioral data during on-column operations, establishing a comprehensive experimental variable database. By integrating experimentally acquired data with machine learning algorithms, we elucidate underlying patterns and employ digital twin technology for real-time process monitoring. This methodology enables identification of error sources and critical factors influencing result stability. Implemented as an instructional experiment, this approach cultivates students’ ability to apply digital technologies to address analytical chemistry challenges.
- Machine learning,
- Microcolumn chromatography,
- Trace arsenic,
- Machine vision
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