Raman-AI synergy in advanced spectroscopy for next-generation diagnostics: Current advances and future perspectives

Abstract

Raman spectroscopy, particularly in its enhanced form, Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a powerful, label-free analytical technique for applications spanning biomedical diagnostics, pharmaceutical analysis, and environmental monitoring. However, its widespread adoption has been hindered by challenges, including signal variability, spectral noise, and the complexity of data interpretation. The recent integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), is transforming these limitations into opportunities, enabling automated, high-throughput, and real-time diagnostics. This review explores the dynamic synergy between AI and SERS platforms, detailing how advanced algorithms are being used for spectral preprocessing, denoising, classification, and molecular pattern recognition. This review highlights major innovations such as AI-driven SERS biosensors, as well as portable and wearable diagnostic devices, and hyperspectral imaging platforms for ultrasensitive detection, including single-molecule resolution. Applications discussed include early cancer detection, pathogen identification, antimicrobial resistance profiling, environmental contaminant sensing, and pharmaceutical quality control. Furthermore, AI models like convolutional neural networks (CNNs), transformers, and generative algorithms are enabling enhanced interpretability, robustness to spectral variability, and greater automation across workflows. Critical challenges, including data standardization, reproducibility, and model transparency, are addressed in the current review, while emphasizing the need for interdisciplinary collaboration and ethical deployment. By bridging nanotechnology, spectroscopy, and intelligent computation, AI-enhanced Raman and SERS technologies are redefining the landscape of diagnostics and sensing. This convergence marks a paradigm shift toward personalized, accessible, and scalable healthcare and environmental solutions, ushering in a new era of smart diagnostics aligned with the principles of Healthcare 4.0 and digital medicine.

Keywords

• Artificial intelligence
• biomedical diagnostics
• biosensors
• Healthcare 4.0
• machine learning
• surface-enhanced Raman spectroscopy

References

1. Acquarelli, J., van Laarhoven, T., Gerretzen, J., Tran, T. N., Buydens, L. M. C., & Marchiori, E. (2017). Convolutional neural networks for vibrational spectroscopic data analysis. Analytica Chimica Acta, 954, 22-31.
2. Al-Shaebi, Z., Akdeniz, M., Ahmed, A. O., Altunbek, M., & Aydin, O. (2023). Breakthrough solution for antimicrobial resistance detection: Surface-enhanced Raman spectroscopy-based on artificial intelligence. Advanced Materials Interfaces, 2300664.
3. Avcu, F. M. (2025). Theoretical and applied potential of artificial intelligence and machine learning in analysing molecular data. Turkish Journal of Analytical Chemistry, 7(1), 61-70. https://dergipark.org.tr/ en/pub/turkjac/issue/90263/1607205.
4. Bantz, K. C., Meyer, A. F., Wittenberg, N. J., Im, H., Kurtuluş, Ö., Lee, S. H., Lindquist, N. C., Oh, S. H., & Haynes, C. L. (2011). Recent progress in SERS biosensing. Physical Chemistry Chemical Physics, 13(24), 11551-11567.
5. Beeram, R., Vepa, K. R., & Soma, V. R. (2023). Recent trends in SERS-based plasmonic sensors for disease diagnostics, biomolecules detection, and machine learning techniques. Biosensors, 13(3), 328. https://www.mdpi.com/2079-6374/13/3/328.
6. Bi, X., Ai, X., Wu, Z., Lin, L. L., Chen, Z., & Ye, J. (2025). Artificial intelligence-powered surface-enhanced Raman spectroscopy for biomedical applications. Analytical Chemistry, 97(13), 6826-6846.
7. Bi, X., Lin, L., Chen, Z., & Ye, J. (2024). Artificial intelligence for surface-enhanced Raman spectroscopy. Small Methods, 8(1), 2301243. https://onlinelibrary.wiley.com/doi/abs/10.1002/smtd.202301243.
8. Boateng, D. (2025). Advances in deep learning-based applications for Raman spectroscopy analysis: A mini-review of the progress and challenges. Microchemical Journal, 112692.

9. Boodaghidizaji, M., Milind Athalye, S., Thakur, S., Esmaili, E., Verma, M. S., & Ardekani, A. M. (2022). Characterizing viral samples using machine learning for Raman and absorption spectroscopy. MicrobiologyOpen, 11(6), e1336.
10. Cho, J. H., Bae, G., & An, K. S. (2024). Surface-engineered graphene surface-enhanced Raman scattering platform with machine-learning enabled classification of mixed analytes. Journal of Sensor Science and Technology, 33(3), 139-146.
11. Ciloglu, F. U., Caliskan, A., Saridag, A. M., Kilic, I. H., Tokmakci, M., Kahraman, M., & Aydin, O. (2021). Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques. Scientific Reports, 11(1), 18444.
12. Coca-Lopez, N., Alcolea-Rodriguez, V., Bañares, M. A., Brockhauser, S., Gorenflot, J., Henderson, A., Hildebrandt, R., Jeliazkova, N., Kochev, N., Lozano-Diz, E., & Pilat, Z. (2025). Open and FAIR Raman spectroscopy: Paving the way for artificial intelligence.
13. Conforti, P. M., Lazzini, G., Russo, P., & D’Acunto, M. (2024). Raman spectroscopy and AI applications in cancer grading: An overview. IEEE Access, 12, 54816-54852.
14. Cui, F., Yue, Y., Zhang, Y., Zhang, Z., & Zhou, H. S. (2020). Advancing biosensors with machine learning. ACS Sensors, 5(11), 3346-3364.
15. Ding, Y., Sun, Y., Liu, C., Jiang, Q. Y., Chen, F., & Cao, Y. (2023). SERS-based biosensors combined with machine learning for medical application. ChemistryOpen, 12(1), e202200192.
16. Domingos, C., Fantoni, A., Fernandes, M., Fidalgo, J., & Pereira, S. A. (2025). Low-cost Raman spectroscopy setup combined with a machine learning model. Sensors, 25(3), 659. https://www.mdpi.com/1424-8220/25/3/659
17. Dos Santos, D. P., Sena, M. M., Almeida, M. R., Mazali, I. O., Olivieri, A. C., & Villa, J. E. (2023). Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: Principles, progress, and trends. Analytical and Bioanalytical Chemistry, 415(18), 3945-3966.
18. Ebrahimi, F., Kumari, A., & Dellinger, K. (2025). Integration of nanoengineering with artificial intelligence and machine learning in surface-enhanced Raman spectroscopy (SERS) for the development of advanced biosensing platforms. Advanced Sensor Research, 4(2), 2400155.
19. Fan, X., Ming, W., Zeng, H., Zhang, Z., & Lu, H. (2019). Deep learning-based component identification for the Raman spectra of mixtures. Analyst, 144(5), 1789-1798.
20. Fan, X., Wang, Y., Yu, C., Lv, Y., Zhang, H., Yang, Q., Wen, M., Lu, H., & Zhang, Z. (2023). A universal and accurate method for easily identifying components in Raman spectroscopy based on deep learning. Analytical Chemistry, 95(11), 4863-4870.
21. Flanagan, A. R., Dalal, D., & Glavin, F. G. (2025). Exploring generative artificial intelligence and data augmentation techniques for spectroscopy analysis. Chemical Reviews.
22. Guo, K., Shen, Y., Gonzalez-Montiel, G. A., Huang, Y., Zhou, Y., Surve, M., Guo, Z., Das, P., Chawla, N. V., Wiest, O., & Zhang, X. (2025). Artificial intelligence in spectroscopy: Advancing chemistry from prediction to generation and beyond. arXiv.
23. Guo, S., Popp, J., & Bocklitz, T. (2021). Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling. Nature Protocols, 16(12), 5426-5459.
24. Han, R., Ketkaew, R., & Luber, S. (2022). A concise review on recent developments of machine learning for the prediction of vibrational spectra. The Journal of Physical Chemistry A, 126(6), 801-812.
25. Hanlon, E. B., Manoharan, R., Koo, T., Shafer, K. E., Motz, J. T., Fitzmaurice, M., Kramer, J. R., Itzkan, I., Dasari, R. R., & Feld, M. S. (2000). Prospects for in vivo Raman spectroscopy. Physics in Medicine & Biology, 45(2), R1.
26. Hassan, M., Zhao, Y., & Zughaier, S. M. (2024). Recent advances in bacterial detection using surface-enhanced Raman scattering. Biosensors, 14(8), 375. https://www.mdpi.com/2079-6374/14/8/375.
27. He, H., Yan, S., Lyu, D., Xu, M., Ye, R., Zheng, P., Lu, X., Wang, L., & Ren, B. (2021). Deep learning for biospectroscopy and biospectral imaging: State-of-the-art and perspectives.
28. He, H. Z., Zhang, L., Wen, Y. L., Wang, Y. Y., Zhang, J. Y., Yao, F. H., Yu, J. Y., Wu, J. X., Peng, Q. L., & Jiang, N. (2025). Integrating surface-enhanced Raman scattering with machine learning: Pioneering a comprehensive diagnostic and therapeutic platform for cancer management. Interdisciplinary Medicine, e20250037.
29. Ho, C. S., Jean, N., Hogan, C. A., Blackmon, L., Jeffrey, S. S., Holodniy, M., Banaei, N., Saleh, A. A., Ermon, S., & Dionne, J. (2019). Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nature Communications, 10(1), 4927. https://www. nature.com/articles/s41467-019-12898-9.
30. Horgan, C. C., Jensen, M., Nagelkerke, A., St-Pierre, J. P., Vercauteren, T., Stevens, M. M., & Bergholt, M. S. (2021). High-throughput molecular imaging via deep-learning-enabled Raman spectroscopy. Analytical Chemistry, 93(48), 15850-15860.
31. Horta-Velázquez, A., Arce, F., Rodríguez-Sevilla, E., & Morales-Narváez, E. (2023). Toward smart diagnostics via artificial intelligence-assisted surface-enhanced Raman spectroscopy. TrAC Trends in Analytical Chemistry, 169, 117378.
32. Huang, L., Duan, Q., Liu, Y., Wu, Y., Li, Z., Guo, Z., Liu, M., Lu, X., Wang, P., Liu, F., & Ren, F. (2025). Artificial intelligence: A key fulcrum for addressing complex environmental health issues. Environment International, 109389.
33. Huang, L., Sun, H., Sun, L., Shi, K., Chen, Y., Ren, X., Ge, Y., Jiang, D., Liu, X., Knoll, W., & Zhang, Q. (2023). Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning. Nature Communications, 14(1), 48. https://www.nature.com/articles/s41467-022-35696-2
34. Isaev, I., Trifonov, N., Sarmanova, O., Burikov, S., Dolenko, T., Laptinskiy, K., & Dolenko, S. (2020, April). Joint application of Raman and optical absorption spectroscopy to determine concentrations of heavy metal ions in water using artificial neural networks. In Saratov Fall Meeting 2019: Laser Physics, Photonic Technologies, and Molecular Modeling (Vol. 11458, pp. 171-179). SPIE.
35. Jayaprakash, V., You, J. B., Kanike, C., Liu, J., McCallum, C., & Zhang, X. (2024). Determination of trace organic contaminant concentration via machine classification of surface-enhanced Raman spectra. Environmental Science & Technology, 58(35), 15619-15628.
36. Kazemzadeh, M., Hisey, C. L., Zargar-Shoshtari, K., Xu, W., & Broderick, N. G. (2022a). Deep convolutional neural networks as a unified solution for Raman spectroscopy-based classification in biomedical applications. Optics Communications, 510, 127977.
37. Kazemzadeh, M., Martinez-Calderon, M., Xu, W., Chamley, L. W., Hisey, C. L., & Broderick, N. G. (2022b). Cascaded deep convolutional neural networks as improved methods of preprocessing Raman spectroscopy data. Analytical Chemistry, 94(37), 12907-12918.
38. Khan, S., Ullah, R., Khan, A., Ashraf, R., Ali, H., Bilal, M., & Saleem, M. (2018b). Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning. Photodiagnosis and Photodynamic Therapy, 23, 89-93.
39. Khan, S., Ullah, R., Khan, A., Wahab, N., Bilal, M., & Ahmed, M. (2016). Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM). Biomedical Optics Express, 7(6), 2249-2256. https://opg.optica.org/abstract.cfm?uri=boe-7-6-2249
40. Khan, S., Ullah, R., Shahzad, S., Anbreen, N., Bilal, M., & Khan, A. (2018a). Analysis of tuberculosis disease through Raman spectroscopy and machine learning. Photodiagnosis and Photodynamic Therapy, 24, 286-291.
41. Khondakar, K. R., Mazumdar, H., Das, S., & Kaushik, A. (2025). Machine learning (ML)-assisted surface-enhanced Raman spectroscopy (SERS) technologies for sustainable health. Advances in Colloid and Interface Science, 103594.
42. Kumar, A., Islam, M. R., Zughaier, S. M., Chen, X., & Zhao, Y. (2024). Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 320, 124627.
43. Kumar, P. P. P., Kaushal, S., & Lim, D. K. (2023). Recent advances in nano/microfabricated substrate platforms and artificial intelligence for practical surface-enhanced Raman scattering-based bioanalysis. TrAC Trends in Analytical Chemistry, 168, 117341.
44. Leng, H., Chen, C., Chen, C., Chen, F., Du, Z., Chen, J., Yang, B., Zuo, E., Xiao, M., Lv, X., & Liu, P. (2023). Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 285, 121839.
45. Lin, C., Li, Y., Peng, Y., Zhao, S., Xu, M., Zhang, L., Huang, Z., Shi, J., & Yang, Y. (2023). Recent development of surface-enhanced Raman scattering for biosensing. Journal of Nanobiotechnology, 21(1), 149. https://doi.org/10.1186/s12951-023-01890-7
46. Lin, L. L., Alvarez-Puebla, R., Liz-Marzán, L. M., Trau, M., Wang, J., Fabris, L., Wang, X., Liu, G., Xu, S., Han, X. X., & Yang, L. (2025). Surface-enhanced Raman spectroscopy for biomedical applications: Recent advances and future challenges. ACS Applied Materials & Interfaces, 17(11), 16287-16379.
47. Liu, J., Osadchy, M., Ashton, L., Foster, M., Solomon, C. J., & Gibson, S. J. (2017). Deep convolutional neural networks for Raman spectrum recognition: A unified solution. Analyst, 142(21), 4067-4074. https://doi.org/10.1039/C7AN01371J
48. Liu, W., Tang, J.-W., Mou, J.-Y., Lyu, J.-W., Di, Y.-W., Liao, Y.-L., Luo, Y.-F., Li, Z.-K., Wu, X., & Wang, L. (2023). Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms. Frontiers in Microbiology, 14, 1101357.
49. Liu, X., An, H., Cai, W., & Shao, X. (2024a). Deep learning in spectral analysis: Modeling and imaging. TrAC Trends in Analytical Chemistry, 172, 117612. https://doi.org/10.1016/j.trac.2024.117612
50. Liu, Y., Chen, S., Xiong, X., Wen, Z., Zhao, L., Xu, B., Guo, Q., Xia, J., & Pei, J. (2025). Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects. Journal of Pharmaceutical Analysis, 15(11), 101271.
51. Liu, Y., Gao, Y., Niu, R., Zhang, Z., Lu, G.W., Hu, H., Liu, T. and Cheng, Z. (2024b). Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy. Analytica Chimica Acta, 1332, p.343376.
52. Lu, W., Chen, X., Wang, L., Li, H., & Fu, Y. V. (2020). Combination of an artificial intelligence approach and laser tweezers Raman spectroscopy for microbial identification. Analytical Chemistry, 92(9), 6288-6296. https://doi.org/10.1021/acs.analchem.9b04946
53. Lu, W., Li, H., Qiu, H., Wang, L., Feng, J., & Fu, Y. V. (2023). Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning. Frontiers in Microbiology, 13, 1076965.
54. Lu, X. Y., Wu, H. P., Ma, H., Li, H., Li, J., Liu, Y. T., Pan, Z. Y., Xie, Y., Wang, L., Ren, B., & Liu, G. K. (2024). Deep learning-assisted spectrum-structure correlation: State-of-the-art and perspectives. Analytical Chemistry, 96(20), 7959-7975.
55. Luo, R., Popp, J., & Bocklitz, T. (2022). Deep learning for Raman spectroscopy: A review. Analytica, 3(3), 287-301.
56. Lussier, F., Thibault, V., Charron, B., Wallace, G. Q., & Masson, J.-F. (2020). Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering. TrAC Trends in Analytical Chemistry, 124, 115796. https://doi.org/10.1016/j.trac.2019.115796
57. Maruthamuthu, M. K., Raffiee, A. H., De Oliveira, D. M., Ardekani, A. M., & Verma, M. S. (2020). Raman spectra-based deep learning: A tool to identify microbial contamination. MicrobiologyOpen, 9(11), e1122.
58. Meza Ramirez, C. A., Greenop, M., Ashton, L., & Rehman, I. U. (2021). Applications of machine learning in spectroscopy. Applied Spectroscopy Reviews, 56(8-10), 733-763.
59. Olaetxea, I., Valero, A., Lopez, E., Lafuente, H., Izeta, A., Jaunarena, I., & Seifert, A. (2020). Machine learning-assisted Raman spectroscopy for pH and lactate sensing in body fluids. Analytical Chemistry, 92(20), 13888-13895. https://doi.org/10.1021/acs.analchem.0c02625
60. Orlando, A., Franceschini, F., Muscas, C., Pidkova, S., Bartoli, M., Rovere, M., & Tagliaferro, A. (2021). A comprehensive review on Raman spectroscopy applications. Chemosensors, 9(9), 262.
61. Pan, L., Pipitsunthonsan, P., Daengngam, C., Channumsin, S., Sreesawet, S., & Chongcheawchamnan, M. (2021). Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network. IEEE Sensors Journal, 21(9), 10834-10843. https://doi.org/10.1109/JSEN.2021.3059849
62. Pan, L., Zhang, P., Daengngam, C., Peng, S., & Chongcheawchamnan, M. (2022). A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances. Journal of Raman Spectroscopy, 53(1), 6-19. https://doi.org/10.1002/jrs.6225
63. Pérez-Jiménez, A. I., Lyu, D., Lu, Z., Liu, G., & Ren, B. (2020). Surface-enhanced Raman spectroscopy: Benefits, trade-offs and future developments. Chemical Science, 11(18), 4563-4577.
64. Qi, Y., Hu, D., Jiang, Y., Wu, Z., Zheng, M., Chen, E. X., Liang, Y., Sadi, M. A., Zhang, K., & Chen, Y. P. (2023a). Recent progresses in machine learning assisted Raman spectroscopy. Advanced Optical Materials, 11(14), Article 2203104. https://doi.org/10.1002/adom.202203104
65. Qi, Y., Liu, Y., & Luo, J. (2023b). Recent application of Raman spectroscopy in tumor diagnosis: From conventional methods to artificial intelligence fusion. PhotoniX, 4(1), Article 22.
66. Quarin, S. M., Vang, D., Dima, R. I., Stan, G., & Strobbia, P. (2025). AI in SERS sensing moving from discriminative to generative. npj Biosensing, 2(1), Article 9.
67. Ralbovsky, N. M., & Lednev, I. K. (2020). Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chemical Society Reviews, 49(20), 7428-7453. https://doi.org/10.1039/D0CS01019G
68. Raturi, P., & Kumar, S. (2025). Multifunctional SERS substrates for smart sensing: Principles, applications, and emerging trends. In M. K. Shukla, P. K. Malik, A. Jain, & N. K. Mishra (Eds.), Smart sensors: Design, challenges, and applications (pp. 1-33). CRC Press.
69. Rial, R. C. (2024). AI in analytical chemistry: Advancements, challenges, and future directions. Talanta, 274, 125949.
70. Sahin, F., Celik, N., Camdal, A., Sakir, M., Ceylan, A., Ruzi, M., & Onses, M. S. (2022). Machine learning-assisted pesticide detection on a flexible surface-enhanced Raman scattering substrate prepared by silver nanoparticles. ACS Applied Nano Materials, 5(9), 13112-13122. https://doi.org/10.1021/acsanm.2c02897
71. Shin, H., Oh, S., Hong, S., Kang, M., Kang, D., Ji, Y.-G., Choi, B. H., Kang, K.-W., Jeong, H., Park, Y., Hong, S., Kim, H. K., & Choi, Y. (2020). Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes. ACS Nano, 14(5), 5435-5444. https://pubs.acs.org/doi/abs/10.1021/acsnano.9b09119
72. Siddhanta, S., Kuzmin, A. N., Pliss, A., Baev, A. S., Khare, S. K., Chowdhury, P. K., Ganguli, A. K., & Prasad, P. N. (2023). Advances in Raman spectroscopy and imaging for biomedical research. Advances in Optics and Photonics, 15(2), 318-384. https://doi.org/10.1364/AOP.479884
73. Signoroni, A., Savardi, M., Baronio, A., & Benini, S. (2019). Deep learning meets hyperspectral image analysis: A multidisciplinary review. Journal of Imaging, 5(5), 52. https://doi.org/10.3390/jimaging5050052.
74. Singh, S., Kumbhar, D., Reghu, D., Venugopal, S. J., Rekha, P. T., Mohandas, S., Rao, S., Rangaiah, A., Chunchanur, S. K., Saini, D. K., & Umapathy, S. (2022). Culture-independent Raman spectroscopic identification of bacterial pathogens from clinical samples using deep transfer learning. Analytical Chemistry, 94(42), 14745-14754. https://doi.org/10.1021/acs.analchem.2c03391
75. Srivastava, S., Wang, W., Zhou, W., Jin, M., & Vikesland, P. J. (2024). Machine learning-assisted surface-enhanced Raman spectroscopy detection for environmental applications: A review. Environmental Science & Technology, 58(47), 20830-20848.
76. Sun, J., Lai, W., Zhao, J., Xue, J., Zhu, T., Xiao, M., Man, T., Wan, Y., Pei, H., & Li, L. (2024). Rapid identification of drug mechanisms with deep learning-based multichannel surface-enhanced Raman spectroscopy. ACS Sensors, 9(8), 4227-4235.
77. Sunil, M., Pallikkavaliyaveetil, N., Mithun, N., Gopinath, A., Chidangil, S., Kumar, S., & Lukose, J. (2024). Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics. Journal of Water Process Engineering, 60, 105150.
78. Tahir, M. A., Dina, N. E., Cheng, H., Valev, V. K., & Zhang, L. (2021). Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis. Nanoscale, 13(27), 11593-11634.
79. Tang, J. W., Yuan, Q., Wen, X. R., Usman, M., Tay, A. C. Y., & Wang, L. (2024). Label-free surface-enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives. Interdisciplinary Medicine, 2(3), e20230060. https://doi.org/10.1002/inmd.20230060
80. Tang, J. W., Yuan, Q., Zhang, L., Marshall, B. J., Tay, A. C. Y., & Wang, L. (2025). Application of machine learning-assisted surface-enhanced Raman spectroscopy in medical laboratories: Principles, opportunities, and challenges. TrAC Trends in Analytical Chemistry, 184, 118135. https://doi.org/10.1016/j.trac.2025.118135
81. Tang, J. W., Li, J. Q., Yin, X. C., Xu, W. W., Pan, Y. C., Liu, Q. H., Gu, B., Zhang, X., & Wang, L. (2022). Rapid discrimination of clinically important pathogens through machine learning analysis of surface enhanced Raman spectra. Frontiers in Microbiology, 13, 843417.
82. Tas, Z., Ciftci, F., Icoz, K., & Unal, M. (2025). Emerging biomedical applications of surface-enhanced Raman spectroscopy integrated with artificial intelligence and microfluidic technologies. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 339, 126285. https://doi.org/10.1016/j.saa.2025.126285
83. Tseng, Y. M., Chen, K. L., Chao, P. H., Han, Y. Y., & Huang, N. T. (2023). Deep learning-assisted surface-enhanced Raman scattering for rapid bacterial identification. ACS Applied Materials & Interfaces, 15(22), 26398-26406. https://doi.org/10.1021/acsami.3c03212
84. Ullah, U., Tahir, Z., Qazi, O., Mirza, S., & Cheema, M. I. (2022). Raman spectroscopy and machine learning-based optical probe for tuberculosis diagnosis via sputum. Tuberculosis, 136, 102251.
85. Vázquez-Iglesias, L., Casagrande, G. M. S., García-Lojo, D., Leal, L. F., Ngo, T. A., Pérez-Juste, J., Reis, R. M., Kant, K., & Pastoriza-Santos, I. (2024). SERS sensing for cancer biomarker: Approaches and directions. Bioactive Materials, 34, 248-268.
86. Vulchi, R. T., Morgunov, V., Junjuri, R., & Bocklitz, T. (2024). Artifacts and anomalies in Raman spectroscopy: A review on origins and correction procedures. Molecules, 29(19), 4748. Wahl, J., Sjödahl, M., & Ramser, K. (2020). Single-step preprocessing of Raman spectra using convolutional neural networks. Applied Spectroscopy, 74(4), 427-438. https://doi.org/10.1177/0003702819888949
87. Wan, Y., Jiang, Y., Zheng, W., Li, X., Sun, Y., Yang, Z., Qi, C., & Zhao, X. (2025). Rapid and high accuracy identification of culture medium by CNN of Raman spectra. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 329, 125608.
88. Wang, H. P., Chen, P., Dai, J. W., Liu, D., Li, J. Y., Xu, Y. P., & Chu, X. L. (2022). Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues. TrAC Trends in Analytical Chemistry, 153, 116648.
89. Wang, Z., Li, Y., Zhai, J., Yang, S., Sun, B., & Liang, P. (2024). Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach. Talanta, 275, 126138.
90. Weng, S., Yuan, H., Zhang, X., Li, P., Zheng, L., Zhao, J., & Huang, L. (2020). Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy. Analyst, 145(14), 4827-4835. Workman, J., Jr., & Mark, H. (2023, June 1). Artificial intelligence in analytical spectroscopy, part II: Examples in spectroscopy. Spectroscopy.
91. Xiong, C.-C., Zhu, S.-S., Yan, D.-H., Yao, Y.-D., Zhang, Z., Zhang, G.-J., & Chen, S. (2023). Rapid and precise detection of cancers via label-free SERS and deep learning. Analytical and Bioanalytical Chemistry, 415(17), 3449-3462.
92. Xue, X., Sun, H., Yang, M., Liu, X., Hu, H.-Y., Deng, Y., & Wang, X. (2023). Advances in the application of artificial intelligence-based spectral data interpretation: A perspective. Analytical Chemistry, 95(37), 13733-13745. https://doi.org/10.1021/acs.analchem.3c02540
93. Yan, C. (2025). A review on spectral data preprocessing techniques for machine learning and quantitative analysis. iScience, 28(7), 112759.
94. Yang, J., Xu, J., Zhang, X., Wu, C., Lin, T., & Ying, Y. (2019). Deep learning for vibrational spectral analysis: Recent progress and a practical guide. Analytica Chimica Acta, 1081, 6-17.
95. Yang, Y., Xu, B., Murray, J., Haverstick, J., Chen, X., Tripp, R. A., & Zhao, Y. (2022). Rapid and quantitative detection of respiratory viruses using surface-enhanced Raman spectroscopy and machine learning. Biosensors and Bioelectronics, 217, 114721.
96. Yi, J., You, E.-M., Hu, R., Wu, D.-Y., Liu, G.-K., Yang, Z.-L., Zhang, H., Gu, Y., Wang, Y.-H., Wang, X., Ma, H., Yang, Y., Liu, J.-Y., Fan, F. R., Zhan, C., Tian, J.-H., Qiao, Y., Wang, H., Luo, S.-H., ... Tian, Z.-Q. (2025). Surface-enhanced Raman spectroscopy: A half-century historical perspective. Chemical Society Reviews, 54, 1453-1551.
97. Yu, M., Li, L., You, R., Ma, X., Zheng, C., Zhu, L., & Zhang, T. (2024). A general framework for qualitative analysis of Raman spectroscopy based on deep learning. Microchemical Journal, 199, 109990.
98. Zhang, X., Xu, J., Yang, J., Chen, L., Zhou, H., Liu, X., Li, H., Lin, T., & Ying, Y. (2020). Understanding the learning mechanism of convolutional neural networks in spectral analysis. Analytica Chimica Acta, 1119, 41-51. https://doi.org/10.1016/j.aca.2020.03.055
99. Zhou, B., Sun, L., Fang, T., Li, H., Zhang, R., & Ye, A. (2022). Rapid and accurate identification of pathogenic bacteria at the single-cell level using laser tweezers Raman spectroscopy and deep learning. Journal of Biophotonics, 15(7), e202100312.
100. Zhou, S., Xie, Y., Feng, X., Li, Y., Shen, L., & Chen, Y. (2025). Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications. Cancer Letters, 614, 217555.
101. Zhu, J., Sharma, A. S., Xu, J., Xu, Y., Jiao, T., Ouyang, Q., Li, H., & Chen, Q. (2021). Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 246, 118994.
102. Zhu, L., Yang, Y., Xu, F., Lu, X., Shuai, M., An, Z., Chen, X., Li, H., Martin, F. L., Vikesland, P. J., & Ren, B. (2025). Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments. Science Advances, 11(2), eadp7991.