Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6181 |
| _version_ | 1797517237555298304 |
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| author | Inês Alves Carvalho Nuno Azevedo Silva Carla C. Rosa Luís C. C. Coelho Pedro A. S. Jorge |
| author_facet | Inês Alves Carvalho Nuno Azevedo Silva Carla C. Rosa Luís C. C. Coelho Pedro A. S. Jorge |
| author_sort | Inês Alves Carvalho |
| collection | DOAJ |
| description | The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies. |
| first_indexed | 2024-03-10T07:13:57Z |
| format | Article |
| id | doaj.art-e8b9efe5807749f5a85f078bc6e07bea |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:57Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e8b9efe5807749f5a85f078bc6e07bea2023-11-22T15:13:07ZengMDPI AGSensors1424-82202021-09-012118618110.3390/s21186181Particle Classification through the Analysis of the Forward Scattered Signal in Optical TweezersInês Alves Carvalho0Nuno Azevedo Silva1Carla C. Rosa2Luís C. C. Coelho3Pedro A. S. Jorge4Centre for Applied Photonics, INESC TEC, Rua do Campo Alegre 687, 4169-007 Porto, PortugalCentre for Applied Photonics, INESC TEC, Rua do Campo Alegre 687, 4169-007 Porto, PortugalCentre for Applied Photonics, INESC TEC, Rua do Campo Alegre 687, 4169-007 Porto, PortugalCentre for Applied Photonics, INESC TEC, Rua do Campo Alegre 687, 4169-007 Porto, PortugalCentre for Applied Photonics, INESC TEC, Rua do Campo Alegre 687, 4169-007 Porto, PortugalThe ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.https://www.mdpi.com/1424-8220/21/18/6181optical tweezersoptical trappingparticle identificationBrownian motionprincipal component analysis |
| spellingShingle | Inês Alves Carvalho Nuno Azevedo Silva Carla C. Rosa Luís C. C. Coelho Pedro A. S. Jorge Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers Sensors optical tweezers optical trapping particle identification Brownian motion principal component analysis |
| title | Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers |
| title_full | Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers |
| title_fullStr | Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers |
| title_full_unstemmed | Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers |
| title_short | Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers |
| title_sort | particle classification through the analysis of the forward scattered signal in optical tweezers |
| topic | optical tweezers optical trapping particle identification Brownian motion principal component analysis |
| url | https://www.mdpi.com/1424-8220/21/18/6181 |
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