Selective approaches in analytical chemistry are essential in drug analysis. Spectroscopic methods are often performed to identify drugs, and electroanalysis is mostly used in quantitative assays. However, chemometrics are tools that can be applied to analytical methods in order to enhance selectivity. Thus, the aim of this work was to propose a novel drug identification approach with electroanalysis with several classification algorithms and to compare those results with Raman spectroscopy. The commercial tablets and analytical standards of diclofenac, amoxicillin, hydrochlorothiazide, bromazepam, piroxicam, sulfadiazine, albendazole, cyclobenzaprine and ibuprofen were studied. The classification approaches of linear discrimination analysis (LDA), partial least squares discriminant analysis (PLSDA), support vector machine and k-nearest neighbours (KNN) were employed. Differential pulse voltammetry and Raman spectroscopy were used. The models AS, CT and AS-CT were proposed by the division of the dataset in three parts, being only the analytical standards (AS), only the commercial tablets (CT) and both analytical standards and commercial tablets (AS-CT). For voltammetry and Raman LDA and PLSDA yielded the best accuracy in model AS. For the commercial tablets (model CT), voltammetry showed better performance than Raman. In model AS-CT the KNN classifications displayed better accuracy for both voltammetry and Raman. Voltammetry showed a great performance in the identification of analytical standards and commercial tablet drugs, higher versatility and easier employment due to the nonrequirement of variable selection in comparison to Raman and showed a better performance than Raman for the identification of commercial tablets.

分析化学中的选择性方法在药物分析中是必不可少的。通常使用光谱方法来识别药物，而电分析主要用于定量分析。然而，化学计量学是可应用于分析方法以提高选择性的工具。因此，这项工作的目的是提出一种具有多种分类算法的电分析新药物识别方法，并将这些结果与拉曼光谱进行比较。研究了双氯芬酸、阿莫西林、氢氯噻嗪、溴马西泮、吡罗昔康、磺胺嘧啶、阿苯达唑、环苯扎林和布洛芬的市售片剂和分析标准品。线性判别分析（LDA）、偏最小二乘判别分析（PLSDA）的分类方法，采用了支持向量机和 k 最近邻 (KNN)。使用微分脉冲伏安法和拉曼光谱。模型AS、CT和AS-CT是通过将数据集分为三部分提出的，分别是仅分析标准品（AS）、仅商业片剂（CT）和分析标准品和商业片剂（AS-CT）。对于伏安法和拉曼 LDA 和 PLSDA 在模型 AS 中产生最佳精度。对于商业片剂（模型 CT），伏安法显示出比拉曼更好的性能。在模型 AS-CT 中，KNN 分类对伏安法和拉曼法都显示出更好的准确性。伏安法在分析标准品和市售片剂药物的鉴定方面表现出色，