The Effect of Noise in Raman Spectra on the Reconstruction of the Concentration of Amino Acids in the Mixture by Multivariate Curve Resolution (MCR) Analysis

Irina A. Matveeva (Login required)
Samara National Research University, 34 Moskovskoe shosse, Russia

Lyudmila A. Bratchenko
Samara National Research University, 34 Moskovskoe shosse, Russia

Oleg O. Myakinin
Samara National Research University, 34 Moskovskoe shosse, Russia

Elena N. Tupikova
Samara National Research University, 34 Moskovskoe shosse, Russia

Valery P. Zakharov
Samara National Research University, 34 Moskovskoe shosse, Russia

Paper #3424 received 23 May 2021; revised manuscript received 25 Jun 2021; accepted for publication 26 Jun 2021; published online 30 Jun 2021.

DOI: 10.18287/JBPE21.07.020309


Changes in the concentration of free amino acids in biological tissues is a sign of impaired protein metabolism in patients with cancer. Recently, Raman spectroscopy has been used for early diagnostics of oncological diseases. The concentrations of individual components of biological tissue (for instance, the concentrations of amino acids) can be obtained by decomposing the tissue Raman spectrum. This study was designed to evaluate the effect of noise in the Raman spectra of individual amino acids on the result of the decomposition of the spectra of an amino acid mixture. As a decomposition method, we used Multivariate Curve Resolution-Alternating Least Squares (MCR–ALS) analysis and investigate experimental Raman spectra of amino acids and mathematically simulated Raman spectra of amino acid mixtures. Noise with different signal-to-noise ratios (SNR) was artificially added to both the experimental spectra of pure amino acids and the spectra of the mixtures. Concentration values for each amino acid obtained as a result of applying the MCR–ALS analysis have been compared with the corresponding true values and the correlation coefficients have been calculated. The results show a less pronounced negative effect of noise in the case when the spectra of pure amino acids (which were used as a basis for the MCR–ALS analysis) are noisy, and a more pronounced negative effect when the spectrum of the mixture is noisy. The accuracy of reconstruction of an amino acid is also negatively affected by strong background fluorescence in the amino acid spectrum. Moreover, the results indicate that using the basis spectra with a high SNR (SNR = 5) makes it possible to successfully estimate the amino acid concentrations in a mixture even when the Raman spectrum of the mixture is noisy and has a low SNR (SNR < 5).


Signal-to-noise ratio; Raman spectrum; Raman scattering; multivariate curve resolution; free amino acids

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1. Cancer, World Health Organisation, 3 March 2021 (accessed 5 April 2021). [].

2. A. Kubota, M. M. Meguid, and D. C. Hitch, “Amino acid profiles correlate diagnostically with organ site in three kinds of malignant tumors,” Cancer 69(9), 2343–2348 (1992).

3. Y. Miyagi, M. Higashiyama, A. Gochi, M. Akaike, T. Ishikawa, T. Miura, and M. Moriyama, “Plasma free amino acid profiling of five types of cancer patients and its application for early detection,” PloS One 6(9), e24143 (2011).

4. M. Shingyoji, T. Iizasa, M. Higashiyama, F. Imamura, N. Saruki, A. Imaizumi, and M. Yamakado, “The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer,” BMC Cancer 13(1), 77 (2013).

5. Q. H. Zhao, Y. Cao, Y. Wang, C. L. Hu, A. L. Hu, L. Ruan, and M. Ren, “Plasma and tissue free amino acid profiles and their concentration correlation in patients with lung cancer,” Asia Pacific journal of clinical nutrition 23(3), 429–436 (2014).

6. A. M. Proenza, J. Oliver, A. Palou, and P. Roca, “Breast and lung cancer are associated with a decrease in blood cell amino acid content,” The Journal of Nutritional Biochemistry 14(3), 133–138 (2003).

7. I. A. Bratchenko, L. A. Bratchenko, A. A., Moryatov, Y. A., Khristoforova, D. N. Artemyev, O. O. Myakinin, A. E. Orlov, S. V. Kozlov, and V. P. Zakharov, “In vivo diagnosis of skin cancer with a portable Raman spectroscopic device,” Experimental Dermatology 30(5), 652–663 (2021).

8. A. de Juan, R. Tauler, “Multivariate Curve Resolution: 50 years addressing the mixture analysis problem – A review,” Analytica Chimica Acta 1145(8), 59–78 (2020).

9. M. Garrido, F. X. Rius, and M. S. Larrechi, “Multivariate curve resolution–alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes,” Analytical and Bioanalytical Chemistry 390(8), 2059–2066 (2008).

10. H. Noothalapati, K. Iwasaki, and T. Yamamoto, “Biological and medical applications of multivariate curve resolution assisted Raman spectroscopy,” Analytical Sciences 33(1), 15–22 (2017).

11. H. Xu, B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” Journal of Biomedical Optics 14(6), 064011 (2009).

12. P. H. Chen, R. Shimada, S. Yabumoto, H. Okajima, M. Ando, C. T. Chang, L. T. Lee, Y. K. Wong, A. Chiou, and H. O. Hamaguchi, “Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis,” Scientific Reports 6(1), 1–9 (2016).

13. K. Iwasaki, A. Araki, C. M. Krishna, R. Maruyama, T. Yamamoto, and H. Noothalapati, “Identification of Molecular Basis for Objective Discrimination of Breast Cancer Cells (MCF-7) from Normal Human Mammary Epithelial Cells by Raman Microspectroscopy and Multivariate Curve Resolution Analysis,” International Journal of Molecular Sciences 22(2), 800 (2021).

14. J. Felten, H. Hall, J. Jaumot, R. Tauler, A. De Juan, and A. Gorzsás, “Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS),” Nature protocols 10(2), 217–240 (2015).

15. L. A. Bratchenko, I. A. Bratchenko, A. A. Lykina, M. V. Komarova, D. N. Artemyev, O. O. Myakinin, A. A. Moryatov, I. L. Davydkin, S. V. Kozlov, and V. P. Zakharov, “Comparative study of multivariative analysis methods of blood Raman spectra classification,” Journal of Raman Spectroscopy 51(2), 279–292 (2020).

16. SpectraSuite, Ocean Insight (accessed 29 July 2020). [].

17. I. A. Matveeva, O. O. Myakinin, Y. A. Khristoforova, I. A. Bratchenko, E. N. Tupikova, and V. P. Zakharov, “Possibilities for decomposing Raman spectra of amino acids mixture by Multivariate Curve Resolution (MCR) analysis,” SPIE Proceedings 11582, 115821G (2020).

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