Hide
Show all
Decomposition Method for Raman Spectra of Dentine
Oleg O. Frolov
(Login required)
Samara National Research University, Russian Federation
Samara State Medical University, Russian Federation
Paper #9074 received 3 Mar 2024; revised manuscript received 6 Aug 2024; accepted for publication 10 Aug 2024; published online 7 Sep 2024.
DOI: 10.18287/JBPE24.10.030303
Abstract
This article presents a developed two-stage method for decomposing a spectral contour with a high degree of overlap of Raman scattering (RS) lines. The algorithm allows you to take into account the error in the position of the Raman bands and other parameters, and work with asymmetric lines. By determining the final model based on multiple spectra, it allows the Raman lines to be correctly identified. A model experiment was carried out to reconstruct the composition and parameters of elementary lines based on synthetic spectra. The error in determining the line parameters was characterized by MAPE (mean absolute percentage error) for peak intensity being 0.3%, MAPE for half-width dx being 0.3%, and MAE (mean absolute error) for the peak position x0 being 0.1 cm−1. The algorithm was applied to the real problem of analyzing the spectra of demineralized dentin.
Keywords
Full Text:
PDFReferences
1. F. Alsmeyer, W. Marquardt, “Automatic Generation of Peak-Shaped Models,” Applied Spectroscopy 58(8), 986–994 (2004).
2. F. Alsmeyer, H.-J. Koß, and W. Marquardt, “Indirect Spectral Hard Modeling for the Analysis of Reactive and Interacting Mixtures,” Applied Spectroscopy 58(8), 975–985 (2004).
3. R. J. Meier, “On art and science in curve-fitting vibrational spectra,” Vibrational Spectroscopy 39(2), 266–269 (2005).
4. Y. Chen, L. Dai “Automated decomposition algorithm for Raman spectra based on a Voigt line profile model,” Applied Optics 55(15), 4085–4094 (2016).
5. Z. Mou-Yan, R. Unbehauen, “A deconvolution method for spectroscopy,” Measurement Science and Technology 6(5), 482–487 (1995).
6. H. Liu, S. Liu, Z. Zhang, J. Sun, and J. Shu, “Adaptive total variation-based spectral deconvolution with the split Bregman method,” Applied Optics 53(35), 8240–8248 (2014).
7. H. Liu, T. Zhang, L. Yan, H. Fang, and Y. Chang, “A MAP-based algorithm for spectroscopic semi-blind deconvolution,” Analyst 137(16), 3862–3873 (2012).
8. G. A. Kupriyanov, I. V. Isaev, I. V. Plastinin, T. A. Dolenko, and S. A. Dolenko, “Decomposition of Spectral Contour into Gaussian Bands using Gender Genetic Algorithm,” Moscow University Physics Bulletin 78(S1), S236–S242 (2023).
9. Q. Zhou, Z. Zou, and L. Han, “Deep Learning-Based Spectrum Reconstruction Method for Raman Spectroscopy,” Coatings 12(8), 1229 (2022).
10. X. Bian, Z. Shi, Y. Shao, Y. Chu, and X. Tan, “Variational Mode Decomposition for Raman Spectral Denoising,” Molecules 28(17), 6406 (2023).
11. N. Schmid, S. Bruderer, F. Paruzzo, G. Fischetti, G. Toscano, D. Graf, M. Fey, A. Henrici, V. Ziebart, B. Heitmann, H. Grabner, J. D. Wegner, R. K. O. Sigel, and D. Wilhelm, “Deconvolution of 1D NMR spectra: A deep learning-based approach,” Journal of Magnetic Resonance 347, 107357 (2023).
12. S. Kaneki, Y. Kouketsu, M. Aoya, Y. Nakamura, S. R. Wallis, Y. Shimura, and K. Yamaoka, “An automatic peak deconvolution code for Raman spectra of carbonaceous material and a revised geothermometer for intermediate- to moderately high-grade metamorphism,” Progress in Earth and Planetary Science 11(1), 35 (2024).
13. R. J. Barnes, M. S. Dhanoa, and S. J. Lister, “Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra,” Applied Spectroscopy 43(5), 772–777 (1989).
14. S. J. Barton, T. E. Ward, and B. M. Hennelly, “Algorithm for optimal denoising of Raman spectra,” Analytical Methods 10(30), 3759–3769 (2018).
15. M. A. Branch, T. F. Coleman, and Y. Li, “A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems,” SIAM Journal on Scientific Computing 21(1), 1–23 (1999).
16. K. Levenberg, “A Method for the Solution of Certain Non-Linear Problems in Least Squares,” Quarterly of Applied Mathematics 2(2), 164–168 (1944).
17. D. Marquardt, “An Algorithm for Least-Squares Estimation of Nonlinear Parameters,” Journal of the Society for Industrial and Applied Mathematics 11(2), 431–441 (1963).
18. J. A. Nelder, R. Mead, “A simple method for function minimization,” Computer Journal 7(4), 308–313 (1965).
19. C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, “L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization,” ACM Transactions on Mathematical Software 23(4), 550–560 (1997).
20. M. J. D. Powell, “An efficient method for finding the minimum of a function of several variables without calculating derivatives,” Computer Journal 7(2), 155–162 (1964).
21. M. R. Hestenes, E. Stiefel, “Methods of Conjugate Gradients for Solving Linear Systems,” Journal of Research of the National Bureau of Standards 49(6), 409 (1952).
22. R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, “A Limited Memory Algorithm for Bound Constrained Optimization,” SIAM Journal on Scientific Computing 16(5), 1190–1208 (1995).
23. R. S. Dembo, S. C. Eisenstat, and T. Steihaug, “Inexact newton methods,” SIAM Journal on Numerical Analysis 19(2), 400–408 (1982).
24. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining 226–231 (1996).
© 2014-2025 Authors
Public Media Certificate (RUS). 12+