A dendrimer-based platform integrating surface-enhanced Raman scattering and class-incremental learning for rapidly detecting four pathogenic bacteria

作者：Jieru Qiu, Yi Zhong, Yuming Shao, Guoliang Zhang, Jihong Yang, Zhenhao Li, Yiyu Cheng · 发表于：Chemical Engineering Journal · 年份：2024 · DOI：10.1016/j.cej.2024.155987 · 被引用次数：21 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、SARS-CoV-2 detection and testing、COVID-19 diagnosis using AI

Illustration of a novel dendrimer-based platform for detecting multiple foodborne pathogens. Poly(amidoamine) (PAMAM) dendrimers were combined with gold nanoparticles (Au NPs) to prepare PAMAM-based gold nanoassemblies (PGNAs). These PGNAs were then applied to a PAMAM-treated silicon wafer (Si) to fabricate the PGNAs/Si substrate for analyzing both spiked and real samples. Surface-enhanced Raman scattering (SERS) spectra obtained from this setup were processed using the LightGBM machine learning algorithm for bacterial classification. Key features contributing to accurate classification were identified using the Shapley additive exPlanations (SHAP) method. • A new bacterial detection platform integrating SERS with CIL model. • Precise nanostructure control using PAMAM to improve SERS performance. • Efficient CIL models were developed for accurate categorization of pathogens. • The SHAP method was utilized on CIL model for improving model interpretability. Rapid monitoring of pathogens is crucial for preventing foodborne diseases, thus making it an urgent need to develop efficient, fast, and simple methods for on-site detection of multiple pathogens. With advances in SERS-based label-free biosensors and machine learning, progress is evident, yet challenges such as complex substrates and limited model interpretability persist. In this work, we reported a novel dendrimer-based platform that integrated surface-enhanced Raman scattering (SERS) with class-incremental learning (CIL)...