Inflammatory diseases increase has recently sparked the research interest for drugs diagnostic tools development. At therapeutic doses, acetylsalicylic acid (ASA or aspirin) is widely used for these diseases' treatment. ASA overdoses can however give rise to adverse side effects including ulcers, gastric damage. Hence, development of simple, portable and sensitive methods for ASA detection is desirable. This paper reports aspirin analysis in urine, saliva and pharmaceutical tablet using an electrochemical sensor and a voltammetric electronic tongue (VE-Tongue). The electrochemical sensor was fabricated by self-assembling chitosan capped with gold nanoparticles (Cs + AuNPs) on a screen-printed carbon electrode (SPCE). It exhibits a logarithmic-linear relationship between its response and the ASA concentration in the range between 1 pg/mL and 1 μg/mL. A low detection limit (0.03 pg/mL), good selectivity against phenol and benzoic acid interference, and successful practical application were demonstrated. Qualitative analysis was performed using the VE-Tongue based unmodified metal electrodes combined with two chemometric approaches to classify urine samples spiked with different aspirin concentrations. Partial least squares (PLS) method provided prediction models obtained from the data of both devices with a regression correlation coefficient R² = 0.99. Correspondingly, the SPCE/(Cs + AuNPs) electrochemical sensor and VE-Tongue could be viable tools for biological analysis of drugs.

炎症性疾病的增加最近激发了药物诊断工具开发的研究兴趣。在治疗剂量下，乙酰水杨酸（ASA或阿司匹林）被广泛用于这些疾病的治疗。但是，ASA过量会引起不良副作用，包括溃疡，胃部损害。因此，期望开发用于ASA检测的简单，便携式和灵敏的方法。本文报告了使用电化学传感器和伏安电子舌（VE-Tongue）对尿液，唾液和药物片剂中的阿司匹林进行分析。电化学传感器是通过在丝网印刷的碳电极（SPCE）上自组装被金纳米颗粒（Cs + AuNPs）覆盖的壳聚糖组装而成的。它的响应和ASA浓度在1 pg / mL和1μg/ mL之间显示出对数线性关系。证明了低检测限（0.03 pg / mL），对苯酚和苯甲酸干扰的良好选择性以及成功的实际应用。使用基于VE-Tongue的未修饰金属电极结合两种化学计量学方法进行定性分析，以对掺有不同阿司匹林浓度的尿液样品进行分类。偏最小二乘（PLS）方法为从两个设备的数据获得的预测模型提供了回归相关系数R 使用基于VE-Tongue的未修饰金属电极结合两种化学计量学方法进行定性分析，以对掺有不同阿司匹林浓度的尿液样品进行分类。偏最小二乘（PLS）方法为从两个设备的数据获得的预测模型提供了回归相关系数R 使用基于VE-Tongue的未修饰金属电极结合两种化学计量学方法进行定性分析，以对掺有不同阿司匹林浓度的尿液样品进行分类。偏最小二乘（PLS）方法为从两个设备的数据获得的预测模型提供了回归相关系数R²  = 0.99。相应地，SPCE /（Cs + AuNPs）电化学传感器和VE-舌头可能是药物生物学分析的可行工具。