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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.18935 |
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| _version_ | 1866912170117169152 |
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| author | Zhao, Yingqi Zhan, Kuo Xin, Pei-Lin Chen, Zuyan Li, Shuai De Angelis, Francesco Huang, Jianan |
| author_facet | Zhao, Yingqi Zhan, Kuo Xin, Pei-Lin Chen, Zuyan Li, Shuai De Angelis, Francesco Huang, Jianan |
| contents | Discriminating the low-abundance hydroxylated proline from hydroxylated proline is crucial for monitoring diseases and eval-uating therapeutic outcomes that require single-molecule sensors. While the plasmonic nanopore sensor can detect the hydrox-ylation with single-molecule sensitivity by surface enhanced Raman spectroscopy (SERS), it suffers from intrinsic fluctuations of single-molecule signals as well as strong interference from citrates. Here, we used the occurrence frequency histogram of the single-molecule SERS peaks to extract overall dataset spectral features, overcome the signal fluctuations and investigate the citrate-replaced plasmonic nanopore sensors for clean and distinguishable signals of proline and hydroxylated proline. By ligand exchange of the citrates by analyte molecules, the representative peaks of citrates decreased with incubation time, prov-ing occupation of the plasmonic hot spot by the analytes. As a result, the discrimination of the single-molecule SERS signals of proline and hydroxylated proline was possible with the convolutional neural network model with 96.6% accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18935 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model Zhao, Yingqi Zhan, Kuo Xin, Pei-Lin Chen, Zuyan Li, Shuai De Angelis, Francesco Huang, Jianan Chemical Physics Machine Learning Biological Physics Discriminating the low-abundance hydroxylated proline from hydroxylated proline is crucial for monitoring diseases and eval-uating therapeutic outcomes that require single-molecule sensors. While the plasmonic nanopore sensor can detect the hydrox-ylation with single-molecule sensitivity by surface enhanced Raman spectroscopy (SERS), it suffers from intrinsic fluctuations of single-molecule signals as well as strong interference from citrates. Here, we used the occurrence frequency histogram of the single-molecule SERS peaks to extract overall dataset spectral features, overcome the signal fluctuations and investigate the citrate-replaced plasmonic nanopore sensors for clean and distinguishable signals of proline and hydroxylated proline. By ligand exchange of the citrates by analyte molecules, the representative peaks of citrates decreased with incubation time, prov-ing occupation of the plasmonic hot spot by the analytes. As a result, the discrimination of the single-molecule SERS signals of proline and hydroxylated proline was possible with the convolutional neural network model with 96.6% accuracy. |
| title | Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model |
| topic | Chemical Physics Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2412.18935 |