AbstractIn this article, a facile and sensitive electrochemical method for quantification of Salmonella Pullorum and Salmonella Gallinarum (S. Pullorum and S. Gallinarum) was established by monitoring glucose consumption with a… Click to show full abstract
AbstractIn this article, a facile and sensitive electrochemical method for quantification of Salmonella Pullorum and Salmonella Gallinarum (S. Pullorum and S. Gallinarum) was established by monitoring glucose consumption with a personal glucose meter (PGM). Antibody-functionalized magnetic nanoparticles (IgG-MNPs) were used to capture and enrich S. Pullorum and S. Gallinarum, and IgG-MNPs-S. Pullorum and IgG-MNPs-S. Gallinarum complexes were magnetically separated from a sample using a permanent magnet. The trace tag was prepared by loading polyclonal antibodies and high-content glucose oxidase on amino-functionalized silica nanoparticles (IgG-SiNPs-GOx). With a sandwich-type immunoassay format, IgG-SiNPs-GOx were added into the above mixture solution and conjugated to the complexes, forming sandwich composites IgG-MNPs/S. Pullorum and S. Gallinarum/IgG-SiNPs-GOx. The above sandwich composites were dispersed in glucose solution. Before and after the hydrolysis of glucose, the concentration of glucose was measured using PGM. Under optimal conditions, a linear relationship between the decrease of glucose concentration and the logarithm of S. Pullorum and S. Gallinarum concentration was obtained in the concentration range from 1.27 × 102 to 1.27 × 105 CFU mL−1, with a detection limit of 7.2 × 101 CFU mL−1 (S/N = 3). This study provides a portable, low-cost, and quantitative analytical method for bacteria detection; thus, it has a great potential in the prevention of disease caused by S. Pullorum and S. Gallinarum in poultry. Graphical abstractA schematic illustration of the fabrication process of IgG-SiNPs-GOD nanomaterials (A) and IgG-MNPs (B) and experimental procedure of detection of S. Pullorum and S. Gallinarum using GOD-functionalized silica nanospheres as trace tags based on PGM (C).
               
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