A hydrogel-based SERS sensor: Detecting anthracycline anticancer drugs in plasma without pretreatment and enabling intelligent identification

A hydrogel SERS platform requiring no pretreatment has been developed. • The LOD for DOX and MTO were as low as 3.4 × 10 −8  M and 3.6 × 10 −9  M. • Machine learning achieved a 100% identification for DOX and MTO plasma samples. Rapid quantitative detection and classification of trace drugs directly in complex biological fluids are crucial in modern analytical chemistry. This paper proposes a label-free, interference-resistant smart surface-enhanced Raman scattering (SERS) sensing strategy. A machine learning-assisted Au@Ag/C-CNF/PEGDA hydrogel SERS sensor is developed for monitoring and identifying anthracycline anticancer drugs in plasma. Addressing the susceptibility to contamination and oxidation inherent in conventional SERS sensors, this sensor leverages the pore-trapping effect and nanoparticle encapsulation properties of hydrogel. This approach not only eliminates macromolecular interference but also maintains a high signal output of 71.05% for the internal Au@Ag SERS probe after 50 days. Leveraging the electrostatic interactions of carboxylated cellulose (C-CNF), the sensor exhibits excellent signal reproducibility with detection limits as low as 3.4 × 10 −8  M for doxorubicin (DOX) and 3.6 × 10 −9  M for mitoxantrone (MTO). Furthermore, this study employed machine learning models for high-dimensional feature extraction of SERS data, achieving up to 100% classification accuracy for DOX and MTO in spiked plasma samples. This approach holds promise as a robust solution for clinical drug concentration monitoring and species identification, potentially advancing precision treatment strategies in clinical practice. Download: Download high-res image (183KB) Download: Download full-size image