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A multi-component heavy metal detection method using UV-Vis superimposed spectrum and deep learning
Hailong Zhang1, Qiannan Duan2, Lehan Sun1
1Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi'an Key Laboratory of Environmental Simulation and Ecological Health in the Yellow River Basin, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China.
Journal of Hazardous Materials
|July 15, 2025
Summary
This study introduces an AI-powered method using UV-Vis spectroscopy and deep learning to detect multiple heavy metals (HMs) in environmental samples, overcoming spectral overlap challenges for efficient monitoring.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Artificial Intelligence
Background:
- Traditional heavy metal (HM) detection methods are costly and complex.
- Spectral overlap in environmental samples hinders accurate analysis.
- Artificial intelligence (AI) offers potential for intelligent spectral analysis.
Purpose of the Study:
- To develop a novel method for simultaneous multi-component HM detection.
- To address the challenge of spectral overlap in environmental samples.
- To integrate UV-Vis spectroscopy with deep learning for enhanced analysis.
Main Methods:
- Utilized combinatorial chemical probes to improve colorimetric reaction specificity.
- Collected high-throughput spectral data for model training.
- Employed a Transformer deep learning model for end-to-end spectral analysis.
Main Results:
- The model achieved high accuracy (R² = 0.936) for five HMs during development.
- Demonstrated scalability and robustness in real-world samples with ten HMs (R² = 0.681).
- Successfully transitioned spectral data to quantitative ecological risk profiles.
Conclusions:
- The AI-driven UV-Vis spectroscopic method enables accurate, simultaneous detection of multiple HMs.
- Developed AI software automates HM detection and risk assessment, enabling rapid environmental monitoring.
- Facilitates proactive early warning systems for heavy metal pollution control.
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