Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques
Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/13/3/328 |
| _version_ | 1797613233087971328 |
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| author | Reshma Beeram Kameswara Rao Vepa Venugopal Rao Soma |
| author_facet | Reshma Beeram Kameswara Rao Vepa Venugopal Rao Soma |
| author_sort | Reshma Beeram |
| collection | DOAJ |
| description | Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience. |
| first_indexed | 2024-03-11T06:51:59Z |
| format | Article |
| id | doaj.art-d808aba42fb342dbbbc9a8ed5359fabe |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-11T06:51:59Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-d808aba42fb342dbbbc9a8ed5359fabe2023-11-17T09:53:49ZengMDPI AGBiosensors2079-63742023-02-0113332810.3390/bios13030328Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning TechniquesReshma Beeram0Kameswara Rao Vepa1Venugopal Rao Soma2Advanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia—Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, IndiaAdvanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia—Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, IndiaAdvanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia—Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, IndiaSurface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.https://www.mdpi.com/2079-6374/13/3/328biosensingSERSplasmonicsdisease diagnosisbiomoleculesmicroorganisms |
| spellingShingle | Reshma Beeram Kameswara Rao Vepa Venugopal Rao Soma Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques Biosensors biosensing SERS plasmonics disease diagnosis biomolecules microorganisms |
| title | Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques |
| title_full | Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques |
| title_fullStr | Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques |
| title_full_unstemmed | Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques |
| title_short | Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques |
| title_sort | recent trends in sers based plasmonic sensors for disease diagnostics biomolecules detection and machine learning techniques |
| topic | biosensing SERS plasmonics disease diagnosis biomolecules microorganisms |
| url | https://www.mdpi.com/2079-6374/13/3/328 |
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