Work

Nitrilotriacetic acid functionalized gold nanopillars enable stochastic detection and deep learning analysis of prolines and hydroxyprolines by surface enhanced Raman spectroscopy

Year: 2025

Type: article

Abstract: ) to form the NTA-Ni structure for reversible and transient binding of Pro/Hyp. By analyzing the SERS time series of NTA-Ni-Pro/Hyp using the event occurrence frequency, we extracted their SERS feature for the study of binding kinetics and quantification with the detection limits down to 0.20 nM for Pro and 0.23 nM for Hyp in mixture. To overcome the signal fluctuation, we developed a one-dimensional convolutional neural network model to identify NTA-Ni-Pro and NTA-Ni-Hyp with high accuracies of 86.9% and 89.6%, respectively. Our study demonstrated a new SERS strategy of hydroxylation detection by combining stochastic sensing and deep learning analysis. The excellent practicality of our method is promising for binding and analyzing post-translational modifications in biofluids for biomedical complex quantitative analysis and early diagnosis of diseases.

Source: Nanoscale

DOI: 10.1039/d5nr04125b