Colorimetric Nanobiosensor Design for Determining Oxidase Enzyme Substrates in Food and Biological Samples

Biological enzymes have high catalytic activity and unique substrate selectivity; their immobilization may provide cost reduction and reusability. Using magnetic nanopartides (MNPs) as support materials for enzymes ensures easy separation from reaction media by an external magnetic field. Thus, MNPs were prepared by the coprecipitation method, coated with silanol groups, then -NH2-functionalized, and finally activated with glutaraldehyde. Finally, three different oxidase enzymes, i.e., uricase, glucose oxidase, and choline oxidase, were separately immobilized on their surfaces by covalent attachment. Hence, colorimetric nanobiosensors for the determination of three biologically important substrates, i.e., uric acid (UA), glucose (Glu), and choline (Ch), were developed. Hydrogen peroxide liberated from enzyme-substrate reactions was determined by the cupric ion reducing antioxidant capacity (CUPRAC) reagent, bis-neocuproine copper(II) chelate. In addition, UA-free total antioxidant capacity could also be measured via the developed system. Kinetic investigations were carried out for these nanobiosensors to enable the calculation of their Michaelis constants (K-m), revealing no affinity loss for the substrate as a result of immobilization. Enzyme-immobilized MNPs could be reused at least five times. The linear concentration ranges were 5.43-65.22 mu M for UA, 11.1-111.1 mu M for Glu, and 2.78-44.4 mu M for Ch, and the limit of detection (LOD) values with the same order were 0.34, 0.59, and 0.20 mu M, respectively. Besides phenolic and thiol-type antioxidants, UA could be determined with an error range of 0.18-4.87%. The method is based on a dear redox reaction with a definite stoichiometry for the enzymatically generated H2O2 (which minimizes chemical deviations from Beer's law of optical absorbances), and it was successfully applied to the determination of Glu and UA in fetal bovine serum and Ch in infant formula as real samples.