Development of a Method for the Intelligent Analysis of Raman Spectra of Blood Serum
DOI:
https://doi.org/10.18287/JBPE26.12.030306Keywords:
alternating least squares, multidimensional curve resolution method, Raman spectroscopy, blood serum, chronic heart failure, machine learning, stackingAbstract
This paper presents a method for intelligent analysis of Raman spectra of blood serum for chronic heart failure (CHF) diagnosis using machine learning approaches. Raman spectra obtained from blood serum samples of 229 subjects (189 patients with CHF and 40 healthy volunteers) were analyzed using the multivariate curve resolution-alternating least squares (MCR-ALS) method. Eight spectral components were extracted and used as features for classification. Logistic regression, support vector machine, random forest, gradient boosting, and ensemble stacking methods were evaluated. Using only spectral features, the stacking model achieved an area under the receiver operating characteristic curve (ROC AUC) of 0.72 ± 0.04 with a validation accuracy of 0.83 ± 0.03. The inclusion of clinical parameters (systolic and diastolic blood pressure, final systolic size, total bilirubin, and hematocrit) significantly improved classification performance, increasing the ROC AUC to 1.00 ± 0.00 with a validation accuracy of 0.95 ± 0.01. Statistical analysis showed no significant differences between the stacking ensemble and individual classifiers (p > 0.05), indicating that classification performance was primarily determined by the informativeness of the extracted spectral and clinical features. The obtained results demonstrate the potential of combining Raman spectroscopy with machine learning methods for CHF screening and early diagnosis, particularly in settings where access to advanced imaging techniques is limited.
References
1. “Cardiovascular diseases (CVDs),” World Health Organization (WHO), 31 July 2025 (accessed 29 December 2025). [https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-%28cvds%29?utm_source].
2. V. Valente, R. Laborante, A. Abdin, P. M. Becher, M. Lainscak, M. Polovina, C. Gavina, and G. Savarese, “The global epidemiology of heart failure: a comprehensive and contemporary review,” European Journal of Heart Failure xuag121 (2026). DOI: https://doi.org/10.1093/ejhf/xuag121
3. P. A. Heidenreich, B. Bozkurt, D. Aguilar, L. A. Allen, J. J. Byun, M. M. Colvin, A. Deswal, M. H. Drazner, S. M. Dunlay, L. R. Evers, J. C. Fang, S. E. Fedson, G. C. Fonarow, S. S. Hayek, A. F. Hernandez, P. Khazanie, M. M. Kittleson, C. S. Lee, M. S. Link, C. A. Milano, L. C. Nnacheta, A. T. Sandhu, L. W. Stevenson, O. Vardeny, A. R. Vest, and C. W. Yancy, “2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the american college of cardiology/american heart association joint committee on clinical practice guidelines,” Circulation 145(18), (2022). DOI: https://doi.org/10.1161/CIR.0000000000001063
4. D. Glick (Ed.), Methods of Biochemical Analysis, Hoboken, John Wiley & Sons, NJ, USA (2009).
5. C. G. Atkins, K. Buckley, M. W. Blades, and R. F. Turner, “Raman spectroscopy of blood and blood components,” Applied Spectroscopy 71(5), 767–793 (2017). DOI: https://doi.org/10.1177/0003702816686593
6. H. J. Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J. Fullwood, B. Gardner, P. L. Martin-Hirsch, M. J. Walsh, M. R. McAinsh, N. Stone, and F. L. Martin, “Using Raman spectroscopy to characterize biological materials,” Nature Protocols 11(4), 664–687 (2016). DOI: https://doi.org/10.1038/nprot.2016.036
7. N. Kuhar, S. Sil, T. Verma, and S. Umapathy, “Challenges in application of Raman spectroscopy to biology and materials,” RSC Advances 8(46), 25888–25908 (2018). DOI: https://doi.org/10.1039/C8RA04491K
8. J. Dybas, F. C. Alcicek, A. Wajda, M. Kaczmarska, A. Zimna, K. Bulat, A. Blat, T. Stepanenko, T. Mohaissen, E. Szczesny-Malysiak, D. Perez-Guaita, B. R. Wood, and K. M. Marzec, “Trends in biomedical analysis of red blood cells – Raman spectroscopy against other spectroscopic, microscopic and classical techniques,” TrAC Trends in Analytical Chemistry 146, 116481 (2022). DOI: https://doi.org/10.1016/j.trac.2021.116481
9. Y. Khristoforova, L. Bratchenko, and I. Bratchenko, “Raman-based techniques in medical applications for diagnostic tasks: a review,” International Journal of Molecular Sciences 24(21), 15605 (2023). DOI: https://doi.org/10.3390/ijms242115605
10. J. Servert Lerdo De Tejada, J. Vališ, L. Hrubčík, D. Janstová, J. Tomeš, T. Uhlíková, M. Vazdar, J. Mareš, and P. Matějka, “Mathematical and machine learning-assisted modelling of Raman spectroscopy for biomedical applications,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 362, 128199 (2026). DOI: https://doi.org/10.1016/j.saa.2026.128199
11. S. Guo, J. Popp, and T. Bocklitz, “Chemometric analysis in Raman spectroscopy from experimental design to machine learning–based modeling,” Nature Protocols 16(12), 5426–5459 (2021). DOI: https://doi.org/10.1038/s41596-021-00620-3
12. E. D. Omar, H. Mat, A. Z. Abd Karim, R. Sanaudi, F. Ibrahim, M. A. Omar, M. Z. H. Ismail, V. Jayaraj, and B. L. Goh, “Comparative analysis of logistic regression, gradient boosted trees, SVM, and random forest algorithms for prediction of acute kidney injury requiring dialysis after cardiac surgery,” International Journal of Nephrology and Renovascular Disease 17, 197–204 (2024). DOI: https://doi.org/10.2147/IJNRD.S461028
13. J.-M. Yin, Y. Li, J.-T. Xue, G.-W. Zong, Z.-Z. Fang, and L. Zou, “Explainable machine learning-based prediction model for diabetic nephropathy,” Journal of Diabetes Research 2024(1), 8857453 (2024). DOI: https://doi.org/10.1155/2024/8857453
14. Md. S. Hossain, Md. A. Talukder, and Md. Z. Mahmud, “Advancements in cardiovascular disease detection: leveraging data mining and machine learning,” bioRxiv 09, 584222 (2024). DOI: https://doi.org/10.1101/2024.03.09.584222
15. J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142(21), 4067–4074 (2017). DOI: https://doi.org/10.1039/C7AN01371J
16. M. Kazemzadeh, C. L. Hisey, K. Zargar-Shoshtari, W. Xu, and N. G. R. Broderick, “Deep convolutional neural networks as a unified solution for Raman spectroscopy-based classification in biomedical applications,” Optics Communications 510, 127977 (2022). DOI: https://doi.org/10.1016/j.optcom.2022.127977
17. N. Blake, R. Gaifulina, L. D. Griffin, I. M. Bell, and G. M. H. Thomas, “Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature,” Diagnostics 12(6), 1491 (2022). DOI: https://doi.org/10.3390/diagnostics12061491
18. Z. Zhang, A. Castelló, “Principal components analysis in clinical studies,” Annals of Translational Medicine 5(17), 351–351 (2017) DOI: https://doi.org/10.21037/atm.2017.07.12
19. L. A. Bratchenko, I. A. Bratchenko, “Avoiding overestimation and the ‘black box’ problem in biofluids multivariate analysis by Raman spectroscopy: interpretation and transparency with the SP-LIME algorithm,” Journal of Raman Spectroscopy 56(4), 353–364 (2025). DOI: https://doi.org/10.1002/jrs.6764
20. Y. Liu, Y. Wu, J. Wang, J. Qi, C. Zhou, and Y. Xue, “Recent Advances in Raman Spectral Classification with Machine Learning,” Sensors 26(1), 341 (2026). DOI: https://doi.org/10.3390/s26010341
21. R. S. Senger, D. Scherr, “Resolving complex phenotypes with Raman spectroscopy and chemometrics,” Current Opinion in Biotechnology 66, 277–282 (2020). DOI: https://doi.org/10.1016/j.copbio.2020.09.007
22. 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). DOI: https://doi.org/10.1038/nprot.2015.008
23. M. Ando, H. Hamaguchi, “Molecular component distribution imaging of living cells by multivariate curve resolution analysis of space-resolved Raman spectra,” Journal of Biomedical Optics 19(1), 011016 (2013). DOI: https://doi.org/10.1117/1.JBO.19.1.011016
24. O. Sagi, L. Rokach, “Ensemble learning: A survey,” WIREs Data Mining and Knowledge Discovery 8(4), e1249 (2018). DOI: https://doi.org/10.1002/widm.1249
25. M. A. Pramudito, Y. N. Fuadah, Y. S. Kim, and K. M. Lim, “Stacking Ensemble Machine Learning for Cardiac Safety Assessment Using hiPSC-CM MEA Data,” Annals of Biomedical Engineering 54(5), 1334–1344 (2026). DOI: https://doi.org/10.1007/s10439-026-03978-1
26. A. Chaichi, A. Prasad, and M. R. Gartia, “Raman Spectroscopy and Microscopy Applications in Cardiovascular Diseases: From Molecules to Organs,” Biosensors 8(4), 107 (2018). DOI: https://doi.org/10.3390/bios8040107
27. S. Z. Al-Sammarraie, L. A. Bratchenko, E. N. Typikova, V. P. Zakharov, I. A. Bratchenko, and P. A. Lebedev, “Silver nanoparticles-based substrate for blood serum analysis under 785 nm laser excitation,” Journal of Biomedical Photonics & Engineering 8(1), 010301 (2022). DOI: https://doi.org/10.18287/JBPE22.08.010301
28. Y. A. Khristoforova, L. A. Bratchenko, M. A. Skuratova, E. A. Lebedeva, P. A. Lebedev, and I. A. Bratchenko, “Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis,” Journal of Biophotonics 16(7), e202300016 (2023). DOI: https://doi.org/10.1002/jbio.202300016
29. I. A. Matveeva, A. I. Komlev, O. I. Kaganov , A. A. Moryatov, V. P. Zakharov, “Multidimensional analysis of dermoscopic images and spectral information for the diagnosis of skin tumors,” Journal of Biomedical Photonics & Engineering 10(1), (2024). DOI: https://doi.org/10.18287/JBPE24.10.010307
30. J. Jaumot, A. De Juan, and R. Tauler, “MCR-ALS GUI 2.0: New features and applications,” Chemometrics and Intelligent Laboratory Systems 140, 1–12 (2015). DOI: https://doi.org/10.1016/j.chemolab.2014.10.003
31. 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). DOI: https://doi.org/10.2116/analsci.33.15
32. T. G. Nick, K. M. Campbell, “Logistic Regression,” Topics in Biostatistics 404, 273–301 (2007). DOI: https://doi.org/10.1007/978-1-59745-530-5_14
33. S. Domínguez-Almendros, N. Benítez-Parejo, and A. R. Gonzalez-Ramirez, “Logistic regression models,” Allergologia et Immunopathologia 39(5), 295–305 (2011). DOI: https://doi.org/10.1016/j.aller.2011.05.002
34. E. Y. Boateng, D. A. Abaye, “A review of the logistic regression model with emphasis on medical research,” Journal of Data Analysis and Information Processing 07(04), 190–207 (2019). DOI: https://doi.org/10.4236/jdaip.2019.74012
35. L. Breiman, “Random forests,” Machine Learning 45(1), 5–32 (2001). DOI: https://doi.org/10.1023/A:1010933404324
36. B. Talekar, “A detailed review on decision tree and random forest,” Bioscience Biotechnology Research Communications 13(14), 245–248 (2020). DOI: https://doi.org/10.21786/bbrc/13.14/57
37. H. A. Salman, A. Kalakech, and A. Steiti, “Random forest algorithm overview,” Babylonian Journal of Machine Learning 2024, 69–79 (2024). DOI: https://doi.org/10.58496/BJML/2024/007
38. C. Cortes, V. Vapnik, “Support-vector networks,” Machine Learning 20(3), 273–297 (1995). DOI: https://doi.org/10.1023/A:1022627411411
39. J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing 408, 189–215 (2020). DOI: https://doi.org/10.1016/j.neucom.2019.10.118
40. S. Winters-Hilt, A. Yelundur, C. McChesney, and M. Landry, “Support vector machine implementations for classification & clustering,” BMC Bioinformatics 7(S2), S4 (2006). DOI: https://doi.org/10.1186/1471-2105-7-S2-S4
41. J. H. Friedman, “Stochastic gradient boosting,” Computational Statistics & Data Analysis 38(4), 367–378 (2002). DOI: https://doi.org/10.1016/S0167-9473(01)00065-2
42. A. Natekin, A. Knoll, “Gradient boosting machines, a tutorial,” Frontiers in Neurorobotics 7, (2013). DOI: https://doi.org/10.3389/fnbot.2013.00021
43. H. Belyadi, A. Haghighat, “Supervised learning,” Machine Learning Guide for Oil and Gas Using Python (Elsevier) 169–295 (2021). DOI: https://doi.org/10.1016/B978-0-12-821929-4.00004-4
44. X. Yue, H. Li, J. Tang, J. Liu, and J. Jiao, “Rapid and label-free screening of echinococcosis serum profiles through surface-enhanced Raman spectroscopy,” Analytical and Bioanalytical Chemistry 412(2), 279–288 (2020). DOI: https://doi.org/10.1007/s00216-019-02234-x
45. J. Wang, D. Lin, J. Lin, Y. Yu, Z. Huang, Y. Chen, J. Lin, S. Feng, B. Li, N. Liu, and R. Chen, “Label-free detection of serum proteins using surface-enhanced Raman spectroscopy for colorectal cancer screening,” Journal of Biomedical Optics 19(8), 087003 (2014). DOI: https://doi.org/10.1117/1.JBO.19.8.087003
46. T. K. Kim, “T test as a parametric statistic,” Korean Journal of Anesthesiology 68(6), 540 (2015). DOI: https://doi.org/10.4097/kjae.2015.68.6.540
47. F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin 1(6), 80 (1945). DOI: https://doi.org/10.2307/3001968
48. E. Sorokina, Y. Khristoforova, I. Bratchenko, M. Skuratova, P. Lebedev, and V. Zakharov, “Comparison of Machine Learning Methods for the Analysis of Serum Raman Spectra in the Detection of Chronic Heart Failure,” Journal of Biomedical Photonics & Engineering 10(4), 040319 (2024). DOI: https://doi.org/10.18287/JBPE24.10.040319
49. Y. Gao, X. Bai, J. Lu, L. Zhang, X. Yan, X. Huang, H. Dai, Y. Wang, L. Hou, S. Wang, A. Tian, and J. Li, “Prognostic value of multiple circulating biomarkers for 2-year death in acute heart failure with preserved ejection fraction,” Frontiers in Cardiovascular Medicine 8, 779282 (2021). DOI: https://doi.org/10.3389/fcvm.2021.779282
50. M. De Bakker, T. B. Petersen, A. J. Rueten-Budde, K. M. Akkerhuis, V. A. Umans, J. J. Brugts, T. Germans, M. J. T. Reinders, P. D. Katsikis, P. J. Van Der Spek, R. Ostroff, R. She, D. Lanfear, F. W. Asselbergs, E. Boersma, D. Rizopoulos, and I. Kardys, “Machine learning–based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure,” European Heart Journal - Digital Health 4(6), 444–454 (2023). DOI: https://doi.org/10.1093/ehjdh/ztad056
51. R. J. Woolley, D. Ceelen, W. Ouwerkerk, J. Tromp, S. M. Figarska, S. D. Anker, K. Dickstein, G. Filippatos, F. Zannad, M. Metra, L. Ng, N. Samani, D. J. Van Veldhuisen, C. Lang, C. S. Lam, and A. A. Voors, “Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction,” European Journal of Heart Failure 23(6), 983–991 (2021). DOI: https://doi.org/10.1002/ejhf.2144
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