文献类型：期刊文献

英文题名：Comprehensive SERS-LFIA Platform for Ultrasensitive Detection and Automated Discrimination of Chloramphenicol Residues in Aquatic Products

作者：Zhao, Shuai[1,2,3];Lin, Chenglong[1,2,3];Xu, Meimei[1,2,3];Zhang, Weida[1,2,3];Li, Dan[1,2,3,4];Peng, Yusi[1,2];Huang, Zhengren[1];Yang, Yong[1,2]

机构：[1]Chinese Acad Sci, Shanghai Inst Ceram, State Key Lab High Performance Ceram & Superfine M, 1295 Dingxi Rd, Shanghai 200050, Peoples R China;[2]Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China;[3]Chinese Acad Sci, Grad Sch, Beijing 100049, Peoples R China;[4]East China Univ Sci & Technol, Shanghai 200237, Peoples R China

年份：2024

卷号：7

期号：16

起止页码：19368

外文期刊名：ACS APPLIED NANO MATERIALS

收录：;EI(收录号：20243216832853);WOS:【SCI-EXPANDED(收录号:WOS:001282974600001)】；

基金：This work was supported by the finical support of the National Key Research and Development Program of China (grant no. 2022YFE0110100), National Natural Science Foundation of China (no. 52172167), and Shanghai Science and Technology Program (nos. 22DX1900300 and 22XD1404000).

语种：英文

外文关键词：chloramphenicol; SERS-LFIA technology; specific-modified Au nanostars; support vector machine

摘要：With the increasing misuse of antibiotic drugs, the convenient and ultrasensitive detection of chloramphenicol (CAP) is essential for ensuring food safety and human health. In this study, a comprehensive platform was developed integrating detection and discrimination capabilities by combining surface-enhanced Raman scattering (SERS) with lateral flow immunoassay (LFIA) technology, along with a support vector machine (SVM) model for data analysis, to detect CAP residues in aquatic products. The CAP-specific antibody-modified gold nanostars (Au NSs) were synthesized as SERS-LFIA nanoprobes, achieving an ultralow limit of detection (LOD) of 10 pg/mL and a theoretical LOD of 1.6 pg/mL for simulated aquatic samples. This sensitivity surpasses that of the currently available commercial strips and demonstrates high specificity. Additionally, the automated discrimination model exhibited high sensitivity (95.83%) and specificity (96.43%) as well as an accuracy of 96% on the test set, marking an 8% improvement over traditional Raman intensity discrimination methods. This developed platform is a simple, fast, and automated technique that eliminates the need for sample pretreatment, offering a promising approach for the trace detection of CAP in aquatic products.