Pseudomonas aeruginosa (P. aeruginosa) is the common infection-causing bacterial pathogen. Conventional methods for the detection of P. aeruginosa are time-consuming, and therefore, a more rapid analytical method is required. Here, monoclonal antibodies (Mabs) against P. aeruginosa (CICC 10419) were prepared and based on paired Mabs, an immunochromatographic assay (ICA) was developed. The ICA strip showed a limit of detection of 2.41 × 104 CFU/mL and the linear range of detection was 3.13 × 104-1.0 × 106 CFU/mL. No cross-reactivity was observed when other common Gram-negative and Gram-positive bacteria were used. The analytical performance of the ICA strip indicated that the developed ICA had good specificity and stability. Moreover, the feasibility of the ICA strip was verified by detecting P. aeruginosa (CICC 10419) in spiked water and food samples. The ICA strip could detect samples contaminated with a low-level of P. aeruginosa (CICC 10419) after 8 h enrichment.

In this work, we present a low-field magnetic resonance imaging (LF-MRI) aptasensor based on the difference in magnetic behavior of two magnetic nanoparticles with diameters of 10 (MN₁₀) and 400 nm (MN₄₀₀) for the rapid detection of Pseudomonas aeruginosa (P. aeruginosa). First, specific anti-P. aeruginosa aptamers were covalently immobilized onto magnetic nanoparticles via 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/N-hydroxysuccinimide chemistry for the capture of the target bacteria. In the presence of P. aeruginosa, an MN₁₀–bacteria–MN₄₀₀ (MBM) complex was formed after binding between the aptamers on magnetic nanoparticles and P. aeruginosa cells. When a magnetic field was applied, the MBM complex and free MN₄₀₀ were rapidly magnetically separated, and free MN₁₀ left in the solution worked as a T₂ (transverse relaxation time) single readout in MRI measurement. Under optimum conditions, the LF-MRI platform provides both image analysis and quantitative detection of P. aeruginosa, with a detection limit of 100 cfu/mL. The feasibility and specificity of the aptasensor were demonstrated in detecting real food, orange juice, and drinking water samples and validated using plate counting methods.