1. Benattia F. K., Arrar Z., Dergal F. Methods and applications of Raman spectroscopy: a powerful technique in modern research, diagnosis, and food quality control // Current Nutrition & Food Science. 2024. Vol. 20, № 1. P. 41-61. DOI: 10.2174/1573401319666230503150005
Benattia F. K., Arrar Z., Dergal F. Methods and applications of Raman spectroscopy: a powerful technique in modern research, diagnosis, and food quality control. Current Nutrition & Food Science. 2024;20(1):41-61. DOI: 10.2174/1573401319666230503150005
2. Xiao L., Feng S., Lu X. Raman spectroscopy: Principles and recent applications in food safety // Advances in Food and Nutrition Research. 2023. Vol. 106. P. 1-29. DOI: 10.1016/bs.afnr.2023.03.007
Xiao L., Feng S., Lu X. Raman spectroscopy: Principles and recent applications in food safety. Advances in Food and Nutrition Research. 2024;106:1-29. DOI: 10.1016/bs.afnr.2023.03.007
3. Khristoforova Y., Bratchenko L., Bratchenko I. Raman-based techniques in medical applications for diagnostic tasks: a review // International Journal of Molecular Sciences. 2023. Vol. 24, № 21. P. 15605. DOI: 10.3390/ijms242115605
Khristoforova Y., Bratchenko L., Bratchenko, I. Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review. International Journal of Molecular Sciences. 2024;24(21):15605. DOI: 10.3390/ijms242115605
4. Raman spectroscopy for viral diagnostics / J. Lukose [et al.] // Biophysical Reviews. 2023. Vol. 15, № 2. P. 199-221. DOI: 10.1007/s12551-023-01059-4
Lukose J., Barik A. K., Mithun N., Pavithran S., George S. D., Murukeshan V. M. et al. Raman spectroscopy for viral diagnostics. Biophysical Reviews. 2023;15(2):199-221. DOI: 10.1007/s12551-023-01059-4
5. Recent advancements and applications of Raman spectroscopy in pharmaceutical analysis / K. C. Shah [et al.] // Journal of Molecular Structure. 2023. Vol. 1278. P. 134914. DOI: 10.1016/j.molstruc.2023.134914
Shah K. C., Shah M. B., Solanki S. J., Makwana V. D., Sureja D. K., Gajjar A. K. et al. Recent advancements and applications of Raman spectroscopy in pharmaceutical analysis. Journal of Molecular Structure. 2023;1278:134914. DOI: 10.1016/j.molstruc.2023.134914
6. Ott C. E., Arroyo L. E. Transitioning surface-enhanced Raman spectroscopy (SERS) into the forensic drug chemistry and toxicology laboratory: Current and future perspectives // Wiley Interdisciplinary Reviews: Forensic Science. 2023. Vol. 5, № 4. P. e1483. DOI: 10.1002/wfs2.1483
Ott C. E., Arroyo L. E. Transitioning surface-enhanced Raman spectroscopy (SERS) into the forensic drug chemistry and toxicology laboratory: Current and future perspectives. Wiley Interdisciplinary Reviews: Forensic Science. 2023;5(4):e1483. DOI: 10.1002/wfs2.1483
7. Chauhan S., Sharma S. Applications of Raman spectroscopy in the analysis of biological evidence // Forensic Science, Medicine and Pathology. 2024. Vol. 20, № 3. P. 1066-1090. DOI: 10.1007/s12024-023-00660-z
Chauhan S., Sharma S. Applications of Raman spectroscopy in the analysis of biological evidence. Forensic Science, Medicine and Pathology. 2024;20(3):1066-1090. DOI: 10.1007/s12024-023-00660-z
8. Recent progresses in machine learning assisted Raman spectroscopy / Y. Qi [et al.] // Advanced Optical Materials. 2023. Vol. 11, № 14. P. 2203104. DOI: 10.1002/adom.202203104
Qi Y., Hu D., Jiang Y., Wu Z., Zheng M., Chen E. X. et al. Recent progresses in machine learning assisted Raman spectroscopy. Advanced Optical Materials. 2023;11(14):2203104. DOI: 10.1002/adom.202203104
9. Berghian-Grosan C., Magdas D. A. Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination // Scientific reports. 2020. Vol. 10, № 1. P. 21152. DOI: 10.1038/s41598-020-78159-8
Berghian-Grosan C., Magdas D. A. Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination. Scientific reports. 2020;10(1):21152. DOI: 10.1038/s41598-020-78159-8
10. Using Raman spectroscopy as a fast tool to classify and analyze Bulgarian wines-A feasibility study / V. Deneva [et al.] // Molecules. 2019. Vol. 25, № 1. P. 170. DOI: 10.3390/molecules25010170
Deneva V., Bakardzhiyski I., Bambalov K., Antonova D., Tsobanova D., Bambalov V. et al. Using Raman spectroscopy as a fast tool to classify and analyze Bulgarian wines-A feasibility study. Molecules. 2019;25(1):170. DOI: 10.3390/molecules25010170
11. Learning algorithms for identification of whisky using portable Raman spectroscopy / K. J. Lee [et al.] // Current Research in Food Science. 2024. Vol. 8. P. 100729. DOI: 10.1016/j.crfs.2024.100729
Lee K. J., Trowbridge A. C., Bruce G. D., Dwapanyin, G. O., Dunning K. R., Dholakia K. Learning algorithms for identification of whisky using portable Raman spectroscopy. Current Research in Food Science. 2024;8:100729. DOI: 10.1016/j.crfs.2024.100729
12. Black Carbon characterization with Raman spectroscopy and machine learning techniques: first results for urban and rural area / L. Drudi [et al.] // Global NEST International Conference on Environmental Science & Technology: Collection of works 18th International Conference on Environmental Science and Technology CEST 2023, Athens, Greece, 30 August to 2 September 2023. DOI: 10.30955/gnc2023.00088
Drudi L., Giardino M., Janner D., Pognant F., Matera F., Sacco M., Bellopede R. Black Carbon characterization with Raman spectroscopy and machine learning techniques: first results for urban and rural area. In: Global NEST International Conference on Environmental Science & Technology: Collection of works 18th International Conference on Environmental Science and Technology CEST 2023, 30 August to 2 September 2023, Athens, Greece. DOI: 10.30955/gnc2023.00088
13. Machine-learning models for Raman spectra analysis of twisted bilayer graphene / N. Sheremetyeva [et al.] // Carbon. 2020. Vol. 169. P. 455-464. DOI: 10.1016/j.carbon.2020.06.077
Sheremetyeva N., Lamparski M., Daniels C., Van Troeye B., Meunier V. Machine-learning models for Raman spectra analysis of twisted bilayer grapheme. Carbon. 2020;169:455-464. DOI: 10.1016/j.carbon.2020.06.077
14. Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey / S. Hu [et al.] // Scientific reports. 2022. Vol. 12, № 1. P. 3456. DOI: 10.1038/s41598-022-07222-3
Hu S., Li H., Chen C., Chen C., Zhao D., Dong B. et al. Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey. Scientific reports. 2022;12(1):3456. DOI: 10.1038/s41598-022-07222-3
15. Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics / M. Sunil [et al.] // Journal of Water Process Engineering. 2024. Vol. 60. P. 105150. DOI: 10.1016/j.jwpe.2024.105150
Sunil M., Pallikkavaliyaveetil N., Gopinath A., Chidangil S., Kumar S., Lukose J. Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics. Journal of Water Process Engineering. 2024;60:105150. DOI: 10.1016/j.jwpe.2024.105150
16. Machine learning-assisted raman spectroscopy and SERS for bacterial pathogen detection: clinical, food safety, and environmental applications / M. H. U. Rahman [et al.] // Chemosensors. 2024. Vol. 12, № 7. P. 140. DOI: 10.3390/chemosensors12070140
Rahman M. H. U., Sikder R., Tripathi M., Zahan M., Ye T., Gnimpieba Z. E. et al. Machine learning-assisted raman spectroscopy and SERS for bacterial pathogen detection: clinical, food safety, and environmental applications. Chemosensors. 2024;12(7):140. DOI: 10.3390/chemosensors12070140
17. Qi Y., Liu Y., Luo J. Recent application of Raman spectroscopy in tumor diagnosis: from conventional methods to artificial intelligence fusion // PhotoniX. 2023. Vol. 4, № 1. P. 22. DOI: 10.1186/s43074-023-00098-0
Qi Y., Liu Y., Luo J. Recent application of Raman spectroscopy in tumor diagnosis: from conventional methods to artificial intelligence fusion PhotoniX. 2023;4(1):22. DOI: 10.1186/s43074-023-00098-0
18. Machine learning analysis of Raman spectra to quantify the organic constituents in complex organic-mineral mixtures / M. Zarei [et al.] // Analytical Chemistry. 2023. Vol. 95, № 43. P. 15908-15916. DOI: 10.1021/acs.analchem.3c02348
Zarei M., Solomatova N. V., Aghaei H., Rothwell A., Wiens J., Melo L. et al. Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures. Analytical Chemistry. 2023;95(43):15908-15916. DOI: 10.1021/acs.analchem.3c02348
19. Аленичев М. К., Юшина А. А. Мера волновых чисел рамановских сдвигов широкого диапазона на основе полимерного материала обучения // Измерительная техника. (В печати.).
Alenichev M. K., Yushina А. А. A wide-range Raman shift wavenumber measure based on a polymer material. Izmeritelnaya Technika. (In Russ.). (In press).
20. Kumar K. Partial least square (PLS) analysis: Most favorite tool in chemometrics to build a calibration model // Resonance. 2021. Vol. 26. P. 429-442. DOI: 10.1007/s12045-021-1140-1
Kumar K. Partial least square (PLS) analysis: Most favorite tool in chemometrics to build a calibration model. Resonance. 2021;26:429-442. DOI: 10.1007/s12045-021-1140-1
21. Саакян А.В., Левин А. Д. Программное обеспечение для обработки спектральных данных методами хемометрики и машинного обучения // Аналитика. 2024. Т. 14, № 2. C. 154-160. DOI: 10.22184/2227-572X.2024.14.2.154.160
Sahakyan A. V., Levin A. D. Software for spectral data processing by chemometrics and machine learning methods. Analitika. 2024;14(2):154-160. (In Russ.).