Design and optimization of a colorimetric biosensor using functionalized gold nanoparticles and bi-functional linker for agriculture and food industry

• Rapid screening of food borne pathogens (2 h) with the f-AuNPs and BLs assay. • Optical, instrument-free detection ideal for on-site food security. • Confirmed LODs for diverse food contaminants: 20 nM proteins, 10 2 CFU/mL bacteria. • Flexible system that can be adapted to a wide range of food contaminants. This study aims to develop a highly sensitive colorimetric detection system utilizing functionalized gold nanoparticles (f-AuNPs) and bifunctional linkers (BLs) for the detection of pathogens and contaminants in food matrices. The primary objective was to explore and optimize key parameters, including reaction time, system volume and the concentration of bifunctional linkers, in order to enhance the system’s detection capabilities. The core mechanism of the bi-functional linker-based detection system is based on the aggregation of streptavidin-functionalized AuNPs (stAuNPs), which varies in accordance with the concentration of the bifunctional linkers (BLs) and reflects the quantity of effective linkers (EALs) available in the system. The process comprises the binding of the targets and BLs, a reduction in EAL concentration, an increase in the BLs concentration required for the aggregation of the stAuNPs, and a shift in the range exhibiting a visible color change (REVC). In systems without a target, all BLs induce aggregation. In contrast, in systems with a target present, some BLs are bound by the target, reducing EAL concentration and altering the optimal BLs concentration for stAuNP aggregation. The system was shown to be capable of detecting protein in PBS at concentrations as low as 2 nM, as well as Salmonella at 10 1 CFU/mL. In whole milk, the detection limits were 20 nM for protein and 10 2 CFU/mL for Salmonella . The principal conclusion is that the BLs-based system provides a robust and adaptable platform for pathogen detection, even in complex food matrices, obviating the need for preprocessing. The findings demonstrate the system’s potential for practical applications in the fields of food safety and agricultural contaminant detection, offering clear visual signals and high sensitivity.