▎ 摘　　要

A novel electrochemical sensor based on the Zinc oxide (ZnO)-decorated reduced graphene oxide (RGO) composites was developed for determination of dopamine (DA) in meat. ZnO-decorated RGO (ZnO-RGO) composites were synthesized by a simple hydrothermal method, and characterized by scanning electron microscopy (SEM) images, Raman spectra, energy dispersive spectra (EDS) and X-ray diffraction (XRD) patterns. Electrochemical performance of the composites was studied by co-immobilization onto the surface of glassy carbon electrodes (GCE), which showed both high sensitivity and selectivity for detection of DA. Two linear ranges were obtained from 0.1 to 100 mu M and 200 to 1800 mu M, respectively. The detection limit (S/N ratio 3.0) was found to be 0.063 mu M. Lastly, the proposed sensor was used for determination of DA in meat samples, which exhibited acceptable detection accuracy of greater than 90.18%. Thus, the proposed sensor may provide a promising alternative method for determination of DA in meats. Industrial relevance: China is the world's biggest market for meat in terms of production as well as consumption, and the safety of meat has become a major problem for consumers. Dopamine (DA) is one kind of widespread 'lean meat powder' added to animal feed to help stocks build muscle rather than fat as an adrenergic neural stimulant. Use of DA in animal feed has been banned in most countries, and it is essential to prevent its illegal use. It is well known that abnormal levels of DA have been linked with Parkinson's disease, epilepsy, senile dementia and schizophrenia. Therefore, detecting the presence of DA in meat samples becomes a subject of great importance. In the study, the proposed ZnO-RGO composites modified GCE showed high sensitivity, excellent selectivity, wide concentration and good stability in the presence of AA, 5-HT and UA, including in real meat sample analysis. Results show that the proposed sensor has great potential in low-cost and convenient determination of DA in meat samples. (C) 2015 Elsevier Ltd. All rights reserved.