SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods
In this study, a non-labeled sensor system for direct determining human glycated albumin levels for medical application is proposed. Using machine learning methods applied to surface-enhanced Raman scattering (SERS) spectra of human glycated albumin and serum human albumin enabled the avoidance of c...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/10/12/520 |
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| author | Ekaterina A. Slipchenko Irina A. Boginskaya Robert R. Safiullin Ilya A. Ryzhikov Marina V. Sedova Konstantin N. Afanasev Natalia L. Nechaeva Ilya N. Kurochkin Alexander M. Merzlikin Andrey N. Lagarkov |
| author_facet | Ekaterina A. Slipchenko Irina A. Boginskaya Robert R. Safiullin Ilya A. Ryzhikov Marina V. Sedova Konstantin N. Afanasev Natalia L. Nechaeva Ilya N. Kurochkin Alexander M. Merzlikin Andrey N. Lagarkov |
| author_sort | Ekaterina A. Slipchenko |
| collection | DOAJ |
| description | In this study, a non-labeled sensor system for direct determining human glycated albumin levels for medical application is proposed. Using machine learning methods applied to surface-enhanced Raman scattering (SERS) spectra of human glycated albumin and serum human albumin enabled the avoidance of complex sample preparation. By implementing linear discriminant analysis and regularized linear regression, classification and regression problems were solved based on the spectra obtained as a result of the experiment. The results show that, coupled with data augmentation and a special cross-validation procedure, the methods we employed yield better results in the corresponding tasks in comparison with popular random forest methods and the support vector method. The results show that SERS, in combination with machine learning methods, can be a powerful and effective tool for the simple and direct assay of protein mixtures. |
| first_indexed | 2024-03-09T17:12:31Z |
| format | Article |
| id | doaj.art-8fd0f2aac61d4a86a01dc3681e9d7dd0 |
| institution | Directory Open Access Journal |
| issn | 2227-9040 |
| language | English |
| last_indexed | 2024-03-09T17:12:31Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj.art-8fd0f2aac61d4a86a01dc3681e9d7dd02023-11-24T13:59:22ZengMDPI AGChemosensors2227-90402022-12-01101252010.3390/chemosensors10120520SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning MethodsEkaterina A. Slipchenko0Irina A. Boginskaya1Robert R. Safiullin2Ilya A. Ryzhikov3Marina V. Sedova4Konstantin N. Afanasev5Natalia L. Nechaeva6Ilya N. Kurochkin7Alexander M. Merzlikin8Andrey N. Lagarkov9Institute for Theoretical and Applied Electromagnetics RAS, Moscow 125412, RussiaInstitute for Theoretical and Applied Electromagnetics RAS, Moscow 125412, RussiaMoscow Institute of Physics and Technology, Dolgoprudny, Moscow 141700, RussiaInstitute for Theoretical and Applied Electromagnetics RAS, Moscow 125412, RussiaInstitute for Theoretical and Applied Electromagnetics RAS, Moscow 125412, RussiaInstitute for Theoretical and Applied Electromagnetics RAS, Moscow 125412, RussiaEmanuel Institute of Biochemical Physics RAS, Moscow 119334, RussiaEmanuel Institute of Biochemical Physics RAS, Moscow 119334, RussiaInstitute for Theoretical and Applied Electromagnetics RAS, Moscow 125412, RussiaInstitute for Theoretical and Applied Electromagnetics RAS, Moscow 125412, RussiaIn this study, a non-labeled sensor system for direct determining human glycated albumin levels for medical application is proposed. Using machine learning methods applied to surface-enhanced Raman scattering (SERS) spectra of human glycated albumin and serum human albumin enabled the avoidance of complex sample preparation. By implementing linear discriminant analysis and regularized linear regression, classification and regression problems were solved based on the spectra obtained as a result of the experiment. The results show that, coupled with data augmentation and a special cross-validation procedure, the methods we employed yield better results in the corresponding tasks in comparison with popular random forest methods and the support vector method. The results show that SERS, in combination with machine learning methods, can be a powerful and effective tool for the simple and direct assay of protein mixtures.https://www.mdpi.com/2227-9040/10/12/520human serum albuminsurface-enhanced Raman spectroscopyprotein mixturesmachine learning methodsnon-labeled sensor systemmedical sensor system |
| spellingShingle | Ekaterina A. Slipchenko Irina A. Boginskaya Robert R. Safiullin Ilya A. Ryzhikov Marina V. Sedova Konstantin N. Afanasev Natalia L. Nechaeva Ilya N. Kurochkin Alexander M. Merzlikin Andrey N. Lagarkov SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods Chemosensors human serum albumin surface-enhanced Raman spectroscopy protein mixtures machine learning methods non-labeled sensor system medical sensor system |
| title | SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods |
| title_full | SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods |
| title_fullStr | SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods |
| title_full_unstemmed | SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods |
| title_short | SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods |
| title_sort | sers sensor for human glycated albumin direct assay based on machine learning methods |
| topic | human serum albumin surface-enhanced Raman spectroscopy protein mixtures machine learning methods non-labeled sensor system medical sensor system |
| url | https://www.mdpi.com/2227-9040/10/12/520 |
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