Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy
Park S. Lee J. Khan S. Wahab A. Kim M.
January-2 2022MDPI
Sensors
2022#22Issue 2
Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.
Heavy-metal ion , Machine learning , Neural network , Pattern classification , Random forest , Surface-enhanced raman spectroscopy (SERS) , SVM
Text of the article Перейти на текст статьи
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi, 39177, South Korea
Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, South Korea
Department of Mathematics, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Department of Bio and Brain Engineering
Department of Mechanical System Engineering
Department of Aeronautics
Department of Mathematics
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026