Kun Chen,Chaoqun Ma,Guoqing Chen,Taiqun Yang,Hui Gao,Lei Li,Zichen Yang,Jun Cao,Chenkai Zheng,Longyao Ma
This study presents the design of a Surface-enhanced Raman scattering (SERS) substrate, COF@Ag, for the sensitive detection of Amoxicillin (AMX) in lake water and honey. Furthermore, the study investigates the role of covalent organic frameworks (COFs) in SERS detection. The characterization results demonstrate the capability of COFs to efficiently enrich Ag nanoparticles (AgNPs), resulting in a more concentrated distribution of hotspots and an enhanced electromagnetic field on the substrate. By employing density functional theory (DFT) simulation, the frontier electronic orbitals of COFs and AMX were analyzed, and the chemical bonds and weak interactions in the system were examined using the Interaction Region Indicator (IRI) method to propose potential enhancement mechanisms. In aqueous solutions, the linear range is 1 μg/L-30 μg/L, with a limit of detection (LOD) 0.279 μg/L. In lake water, the linear range span from 100 μg/L to 500 μg/L, with a detection limit of 8.244 μg/L. For honey, the linear range extend from 20 ng/g to 100 ng/g, with a detection limit of 2.917 ng/g. This method holds key significance in facilitating the rapid detection of amoxicillin and advancing the application of COFs in SERS.
Letrozole, anastrozole and trimetazidine are all included in the prohibited list of the World Anti-Doping Agency (WADA). At present, there is still a lack of a systematic Surface-Enhanced Raman Spectroscopy (SERS) spectral database for these drugs, and traditional detection methods suffer from limitations such as insufficient sensitivity and cumbersome operation procedures. In this study, a novel COF@Ag composite was utilized as a SERS-enhanced substrate in combination with a portable Raman spectrometer, allowing for accurate identification and effective differentiation of the characteristic peaks of the three prohibited drugs. Moreover, this method exhibited excellent performance in detecting actual milk matrix samples, with the spiked recovery rate ranging from 98.9% to 101.1%, thereby directly meeting the practical demands of on-site rapid screening. This work offers a reliable and convenient strategy for both sports food safety monitoring and pharmaceutical analysis.
Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy industry. Currently, the problem of excessive antibiotics in dairy products is very serious. As an ultra-sensitive detection technology, Surface-Enhanced Raman Scattering (SERS) was applied to the detection of quinolone antibiotics in this work. In order to classify and quantify three antibiotics (Ciprofloxacin, Norfloxacin, Levofloxacin) with highly similar molecular structures, a combination of magnetic COF-based SERS substrate and machine learning algorithms (PCA-k-NN, PCA-SVM, PCA-Decision Tree) was used. The classification accuracy of the spectral dataset could reach 100% and the results of LOD calculation were: CIP: 5.61 × 10 −9 M, LEV: 1.44 × 10 −8 M, NFX: 1.56 × 10 −8 M. This provides a new method for the detection of antibiotics in dairy products.
危险品化学品经营许可证（带存储）