A dual-enzymatic activity/SERS dual-mode sensor array based on BSA-Cu nanoflowers for sensitive detection of various...

Rapid and accurate detection of multiple foodborne pathogens is critical for public health and food safety. Although various sensing methods exist, many still face challenges in simultaneously discriminating and quantifying several pathogens in a single, easy-to-implement format, particularly when high throughput and field adaptability are required.

In this study, a novel multimodal sensing platform based on bovine serum albumin-templated copper nanoflowers (BSA-Cu NFs) was developed for the simultaneous discrimination and quantification of five common foodborne pathogens: Escherichia coli, Staphylococcus aureus, Listeria monocytogenes, Streptococcus mutans, and Pseudomonas aeruginosa. The BSA-Cu NFs, synthesized via a facile self-assembly approach, exhibited a well-defined flower-like hierarchical structure, uniform elemental distribution, and mixed Cu(II)/Cu(I) valence states, contributing to excellent peroxidase-like and laccase-like activities as well as intrinsic surface-enhanced Raman scattering (SERS) capability. By integrating multichannel colorimetric responses (using TMB, ABTS, and 2,4-DP as substrates) with the SERS fingerprint signal of oxidized TMB, a four-channel sensor array was constructed. This array enabled distinct clustering and differentiation of the five target pathogens using linear discriminant analysis and hierarchical cluster analysis, with a good signal response over the concentration range of 1 × 10⁶ to 1 × 10⁹ CFU/mL. The platform also reliably identified binary bacterial mixtures and maintained stable performance in complex food matrices such as milk tea, coffee, fruit juice, and tea, as validated by spike-recovery tests.

This work deepens the potential of nanozyme-based multimodal sensor arrays for multiplex foodborne pathogen detection. By combining multiple colorimetric channels with SERS fingerprint signals in a single BSA-Cu NF platform, the proposed strategy overcomes the limitation of single-signal methods and enables high-throughput, field-adaptable screening. The reliable performance in real-world food matrices highlights its promise for on-site, multiplex food safety monitoring.