Dual-Channel Catalytic Immunochromatography Empowered by Machine Learning: Ultrasensitive Detection of <i>Escherichia coli</i> O157:H7 via Magnetic CoFe<sub>2</sub>O<sub>4</sub>@HRP Nanocomposites
Huiqi Yan, Ying Wang, Yuting Zhuang, Yuanyuan Cao, Boyang Sun, Qinlin Feng, Haiyu Wu, Jinbo Cao, Chenyu Xuan, Zeyu Lu, Kaixuan Ma, Le Zhou, Li Wang
Abstract
Traditional immunochromatographic test strips face significant limitations in detecting trace levels of Escherichia coli O157:H7 due to insufficient sensitivity and reliability. To address this challenge, we developed a novel “three-In-One” nanoplatform based on magnetic CoFe 2 O 4 NPs functionalized with horseradish peroxidase (HRP) for dual-channel lateral flow immunoassay (LFIA). The secondary catalytic channel, leveraging HRP-mediated oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB), enables signal amplification, achieving an unprecedented detection limit of 9 CFU/mL─a 100-fold improvement over conventional gold nanoparticle-based LFIA (930 CFU/mL) and a 10-fold enhancement compared to the noncatalyzed CoFe 2 O 4 system (93 CFU/mL). The CoFe 2 O 4 @HRP nanocomposite demonstrates remarkable synergistic effects, combining the magnetic separation capability of CoFe 2 O 4 with the catalytic activity of HRP. This integration not only enhances detection sensitivity but also improves the aqueous stability and antibody loading capacity. In real food sample analyses (pork and milk), the system exhibits excellent accuracy (recovery rate: 89.29–110.71%) and precision (RSD: 3.31–7.93%). To further optimize detection performance, we implemented a robust machine learning framework incorporating deep neural networks (DNN), random forest regression, and k -nearest neighbors algorithms. This predictive model achieved exceptional agreement with experimental results ( R 2 > 0.999), 100% classification accuracy at the order-of-magnitude level, and >95% of predictions within Bland–Altman agreement limits. This work establishes a new paradigm for foodborne pathogen detection by synergistically combining nanomaterial engineering with artificial intelligence, offering a novel paradigm in rapid, ultrasensitive, and quantitative diagnostics for food safety monitoring and clinical applications.