A sensitive immunosensor based on graphene-PAMAM composites for rapid detection of the CP4-EPSPS protein in genetically modified crops. (1st November 2021)

• Record Type: Journal Article
• Authors: Zeng, Haijuan; Yang, Qianwen; Liu, Hua; Wu, Guogan; Jiang, Wei; Liu, Xiaofeng; Wang, Jinbin; Tang, Xueming
• Is Part Of: Food chemistry. Volume 361(2021)
• Publication Date: 2021-11-01
• Subjects: Immunosensor -- Gold nanoparticle-antibody -- Graphene and PAMAM composites -- Genetically modified crops; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.129901
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A facile electrochemical sensing platform was constructed. The immunosensor could amplify the signal by gold nanoparticles-labeled antibody. The sensor was used to monitor CP4-EPSPS protein in GM crops. The sensor was with high specificity, reproducibility and accuracy. The sensor could detect soybean RRS and maize NK603 at contents of 0.01% and 0.03%. Abstract: A simple electrochemical immunosensor based on nitrogen-doped graphene and polyamide-amine (GN-PAM) composites was proposed for the detection of the CP4-EPSPS protein in genetically modified (GM) crops. In this immunosensor, the amplification of the detection signal was realized through antibodies labeled with gold nanoparticles (AuNPs). The electrochemical responses of the immunosensor were linear (R 2 = 0.9935 and 0.9912) when the GM soybean RRS and maize NK603 content ranged from 0.025% to 1.0% and 0.05% to 1.5%, respectively. The limits of detection for the GM soybean RRS and maize NK603 were as low as 0.01% and 0.03%, respectively. The immunosensor also exhibited high specificity, and satisfactory stability, reproducibility, and accuracy. Our findings indicated that the constructed immunosensor provides a new approach for the sensitive detection of the CP4-EPSPS protein. Notably, the sensor may be applied to other proteins or pathogenic bacteria by simply changing the antibodies, and may also be used for multi-component analysis.