"Sweet Nanosheet": An Antibody Mimic for Machine Learning-Assisted Ultra-Sensitive Immunochromatographic Assay for
Pengyu Chen1, Guo Hao2, Bingzhi Li1
1College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
Abstract:
The bacterial surface is rich in diverse molecular features, and fully exploiting these natural recognition mechanisms provides innovative avenues for multimechanism detection of foodborne pathogens. Here, we developed a label-free, dual-modal LFIA platform based on the glycan-cluster effect for the efficient capture of Salmonella. The platform employs dextran-functionalized tungsten diselenide nanosheets (Dex-WSe2) as core probes, where dextran coatings provide antibody-like high-affinity capture through multivalent glycan-bacteria interactions, while WSe2 nanosheets act as dual signal transducers. Benefiting from exciton-plasmon coupling and charge-transfer effects, WSe2 nanosheets not only possess inherent surface-enhanced Raman scattering (SERS) activity but also display distinct visible coloration due to their unique optical properties. Leveraging these dual features, the platform enables highly sensitive bimodal detection, achieving a visual detection limit of 103 CFU/mL and an ultralow SERS detection limit of 52 CFU/mL. Furthermore, machine learning was introduced for multidimensional signal analysis: k-nearest neighbors (KNN) for qualitative concentration classification and random forest (RF) regression for quantitative prediction. The integrated model achieved 100% classification accuracy and an R2 of 0.9977, demonstrating outstanding robustness. By combining glycan-based molecular recognition with machine learning strategies, the Dex-WSe2 probe offers an efficient, stable, and intelligent platform for rapid on-site pathogen detection and food safety monitoring.
