摘要
In this work,a portable sensor based on ionic liquids-carboxylated carbon nanotubes/screen-printed carbon electrodes(IL-COOH-CNTs/SPCE)was constructed and coupled with machine learning for sensitive sulfamethoxazole(SMZ)detection.The IL-COOH-CNTs/SPCE exhibited low impedance,a large effective surface area,and excellent stability.The sensor displayed a wide linear range from 1.04 to 233.00μmol/L,with a limit of detection(LOD)of 0.009μmol/L and a limit of quantification(LOQ)of 0.030μmol/L.The sensor coupled with least square support vector machine(LSSVM)achieved superior predictive performance than when coupled with artificial neural network(ANN).Furthermore,the recoveries for spiked food samples using this method showed no significant difference from those obtained using highperformance liquid chromatography(HPLC),confirming the high accuracy and practicality of the developed method.In summary,the IL-COOH-CNTs/SPCE sensor provides a reliable,portable,and efficient alternative for SMZ detection in aquatic and livestock products.
本研究构建了一种基于离子液体-羧基化碳纳米管/丝网印刷电极(ionic liquids-carboxylated carbon nanotubes/screen-printed carbon electrodes,IL-COOH-CNTs/SPCE)的便携式传感器,并结合机器学习技术实现对磺胺甲噁唑(sulfamethoxazole,SMZ)的灵敏检测。IL-COOH-CNTs/SPCE电极具有阻抗低、电化学有效表面积大和稳定性出色等电化学特性。该传感器的线性范围宽(1.04~233.00μmol/L),检测限为0.009μmol/L,定量限为0.030μmol/L。该传感器采用的最小二乘支持向量机模型相比于人工神经网络模型具有更好的预测性能。此外,实际样品中的加标回收结果与高效液相色谱法结果无显著差异,证明本方法具有较高的准确性和实用性。综上,ILCOOH-CNTs/SPCE传感器可为水产品和畜产品中SMZ检测提供一种可靠、便携和高效的替代方案。
出处
《食品科学》
北大核心
2025年第24期29-43,共15页
Food Science
基金
国家自然科学基金地区科学基金项目(32360624)
江西省自然科学基金重点项目(20242BAB26108)。
