The development of highly specific biomimetic recognition material is a challenge for rapid detection of harmful residues in foodstuff. In this study, a paper-based boronate affinity metal–organic framework/molecularly imprinted polymer microfluidic chip (FZS-BA@MIP) was constructed based on the in situ construction strategy, which was also designed as a highly specific biomimetic recognition module. Here, the homogeneous zeolitic imidazole framework-8 (ZIF-8) membrane served as a great scaffold and enrichment layer. Besides, the recognition layer of MIP was prepared based on a highly oriented boronate affinity surface imprinting strategy. With the aid of the liquid flow channel, the highly specific enrichment and visual detection for antibiotic residues like kanamycin in actual products were achieved on the paper chip module of an integrated lateral flow platform. The whole analysis process could be accomplished within 30 min. In brief, this study offered a new integrated biomimetic recognition platform for visually detecting harmful veterinary residues containing cis-diols, which demonstrated promising commercial value in point-of-care testing of foodborne hazardous compounds.
Linalool, a key monoterpene volatile organic compound, plays a vital role in plant stress signaling and agricultural quality evaluation. Herein, we developed a portable sensing platform by integrating molecularly imprinted metal–organic framework nanozymes (MIP@His-MOF) with smartphone-assisted colorimetric analysis. Histidine-functionalized MIL-101 was designed to mimic the catalytic environment of horseradish peroxidase, while molecular imprinting introduced selective recognition sites for linalool. This dual-functional design led to a 3.9-fold increase in catalytic efficiency and significantly improved selectivity. The optimized sensor achieved a limit of detection of 0.14 ppm in solution and retained high sensitivity in a paper-based format (0.62 ppm). As a proof of concept, the paper sensor successfully differentiated black tea quality grades, showing strong correlation with GC–MS results. This work highlights a modular, low-cost, and field-deployable approach for volatile compound detection in precision agriculture and food quality monitoring.