ArticleNano letters2025
Single-Molecule SERS Discrimination of Proline from Hydroxyproline Assisted by a Deep Learning Model.
Article in Nano letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Plasmonic Optical Tweezers and Surface-Enhanced Raman Spectroscopy: Fundamentals, Single-Entity Applications, and the Evolving Role of Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2026Review
- Bioanalysis Using Surface-Enhanced Raman Spectroscopy: From the Perspective of Single Molecules.ACS nano · 2026Review
- Self-Assembled Rhodium Nanoantennas for Single-Protein UV SERS.Nanophotonics (Berlin, Germany) · 2026Article
- SERS-Based Immunoassay on Ag/ZnO Nanorod Substrates for Detection of CA125 Antigen.ACS measurement science au · 2026Article
- Single-Molecule Monitoring of Nucleic Acid Dynamics Using Raman Correlation Spectroscopy in Plasmonic Nanowells.ACS nano · 2025Article
- Single-Molecule SERS Detection of Phosphorylation in Serine and Tyrosine Using Deep Learning-Assisted Plasmonic Nanopore.The journal of physical chemistry letters · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discriminating low-abundance hydroxylation is a crucial and unmet need for early disease diagnostics and therapeutic development due to the small hydroxyl group with 17.01 Da. While single-molecule surface-enhanced Raman spectroscopy (SERS) sensors can detect hydroxylation, subsequent data analysis suffers from signal fluctuations and strong interference from citrates. Here, we used our plasmonic particle-in-pore sensor, occurrence frequency histogram of the single-molecule SERS spectra, and a one-dimensional convolutional neural network (1D-CNN) model to achieve single-molecule discrimination of hydroxylation. The histogram extracted spectral features of the whole data set to overcome the signal fluctuations and helped the citrate-replaced particle-in-pore sensor to generate clean signals of the hydroxylation for model training. As a result, the discrimination of single-molecule SERS signals of proline and hydroxyproline was successful by the 1D-CNN model with 96.6% accuracy for the first time. The histogram further validated that the features extracted by the 1D-CNN model corresponded to hydroxylation-induced spectral changes.
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Registered trials
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