Ana Gomez Cardoso, Hoda Mozaffari, Syed Rahin Ahmed, Herlys Viltres, Greter A. Ortega, Seshasai Srinivasan and Amin Reza Rajabzadeh
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At optimized conditions, the proposed electrochemical sensor enabled the detection of cocaine with a limit of detection of 1.73 ng mL<small><sup>−1</sup></small> in PBS buffer (pH ∼7.4). Additionally, to facilitate detection in saliva, a machine learning strategy was introduced to analyze sensor analytical responses to overcome saliva-related complications in electrochemical sensing and challenges emanating from saliva-to-saliva variation. The data processing results allowed us to distinguish between cocaine concentrations ranging from 0 to over 50 ng mL<small><sup>−1</sup></small> in saliva with an accuracy of 85%. 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引用次数: 0
摘要
使用可卡因会导致几种严重的健康状况,而过量使用往往会导致死亡。目前,可卡因检测装置需要潜伏期、训练有素的人员和不适合路边应用的昂贵做法。本文提出了一种用于复杂基质中可卡因检测的新型电化学无生物分子传感器,该传感器利用可卡因分子的电活性特性,不需要任何生物分子或化学物质进行检测。本研究利用循环伏安法在缓冲溶液和人唾液中检测可卡因,实现了可卡因修饰碳工作电极。在优化的条件下,所提出的电化学传感器能够在PBS缓冲液(pH ~ 7.4)中检测可卡因,检测限为1.73 ng mL−1。此外,为了便于在唾液中进行检测,研究人员引入了一种机器学习策略来分析传感器的分析响应,以克服电化学传感中与唾液相关的并发症和唾液-唾液差异带来的挑战。数据处理结果使我们能够区分唾液中从0到超过50 ng mL - 1的可卡因浓度,准确率为85%。此外,在存在各种干扰的情况下成功地检测了可卡因,这表明m-Z-COC传感器具有高度特异性,是开发路边口服液可卡因检测试剂盒的有前途的传感器。
Target analyte assisted sensitive electrochemical detection of cocaine on screen printed electrodes†
The use of cocaine leads to several severe health conditions, and overconsumption often leads to death. Currently, cocaine detection devices require incubation periods, highly trained personnel, and expensive practices not suitable for roadside applications. Herein, a novel electrochemical biomolecule-free sensor for cocaine detection in complex matrices is presented using the electroactive characteristics of the cocaine molecule that does not require any biomolecules or chemicals for detection. This study implements the cocaine-modified carbon working electrodes to detect cocaine using cyclic voltammetry in a buffer solution and human saliva. At optimized conditions, the proposed electrochemical sensor enabled the detection of cocaine with a limit of detection of 1.73 ng mL−1 in PBS buffer (pH ∼7.4). Additionally, to facilitate detection in saliva, a machine learning strategy was introduced to analyze sensor analytical responses to overcome saliva-related complications in electrochemical sensing and challenges emanating from saliva-to-saliva variation. The data processing results allowed us to distinguish between cocaine concentrations ranging from 0 to over 50 ng mL−1 in saliva with an accuracy of 85%. Further, the successful detection of cocaine in the presence of various interferences was achieved, revealing that the m-Z-COC sensor is highly specific and a promising sensor for the development of a roadside oral fluid cocaine detection kit.