Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy
Park S. Wahab A. Kim M. Khan S.
10 February 2023Royal Society of Chemistry
Analyst
2023#148Issue 71473 - 1482 pp.
Surface-enhanced Raman scattering (SERS) spectroscopy is still considered poorly reproducible despite its numerous advantages and is not a sufficiently robust analytical technique for routine implementation outside of academia. In this article, we present a self-supervised deep learning-based information fusion technique to minimize the variance in the SERS measurements of multiple laboratories for the same target analyte. In particular, a variation minimization model, coined the minimum-variance network (MVNet), is designed. Moreover, a linear regression model is trained using the output of the proposed MVNet. The proposed model showed improved performance in predicting the concentration of the unseen target analyte. The linear regression model trained on the output of the proposed model was evaluated by several well-known metrics, such as root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and coefficient of determination (R2). The leave-one-lab-out cross-validation (LOLABO-CV) results indicate that the MVNet also minimizes the variance of completely unseen laboratory datasets while improving the reproducibility and linear fit of the regression model. The Python implementation of MVNet and the code for the analysis can be found on the GitHub page https://github.com/psychemistz/MVNet.
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Asan Medical Center, University of Ulsan, College of Medicine, Department of Anesthesiology and Pain Medicine, 88 Olympic-ro 43-gil, Songpa-Gu, Seoul, 05505, South Korea
Department of Mathematics, Nazarbayev University, 53 Kabanbay Batyr Avenue, Astana, 010000, Kazakhstan
Department of Mechanical System Engineering, Kumoh National Institute of Technology, 61, Daehak-ro, Gumi, Gyeongsangbuk-do, 39177, South Korea
Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, 61, Daehak-ro, Gumi, Gyeongsangbuk-do, 39177, South Korea
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea
Siemens Healthineers, 755 College Rd E, Princeton, 08540, NJ, United States
Asan Medical Center
Department of Mathematics
Department of Mechanical System Engineering
Department of Aeronautics
Department of Bio and Brain Engineering
Siemens Healthineers
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