The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@ [Ru(bpy)] with laccase-like activity was designed to develop a versatile machine learning-assisted colorimetric and fluorescence dual-modal assay for efficient onsite intelligent decision recognition and quantification of PPs residues. In the presence of alkaline phosphatase (ALP), the laccase-like activity of Cu-ATP@ [Ru(bpy)] was enhanced to oxidize colorless o-phenylenediamine (OPD) into dark-yellow 2,3-diaminophenazine (DAP) via electron transfer, appearing a new yellow fluorescence at 550 nm. Meanwhile, the red fluorescence of Cu-ATP@ [Ru(bpy)] at 600 nm was quenched due to the internal filter effect (IFE) of DAP towards Cu-ATP@ [Ru(bpy)]. However, the selective inhibition of PPs toward ALP activity enabled to observe a dual-modal response of PPs concentration-dependent decrease in colorimetric signal and enhancement in the fluorescence intensity ratio of F/F. On this basis, both the colorimetric and fluorescence images were captured and processed with a home-made WeChat applet-installed smartphone to extract the corresponding image color information, thus achieving machine learning-assisted onsite real-time and dynamic intelligent decision recognition and quantification of PPs residues in real samples, which shows a promising potential in safeguarding food safety and environmental health.
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