Artificial intelligence-aided electrochemical sensors for capturing and analyzing fingerprint profiles of medicinal materials

This study explores the application of artificial intelligence-aided electrochemical sensors for authenticating medicinal materials, focusing on sika deer antler cap powder. Utilizing differential pulse voltammetry and graphene-modified screen-printed electrodes, we developed a novel method to capture unique electrochemical fingerprints of authentic, counterfeit, and adulterated samples. Three machine learning models-Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM)-were evaluated using both full voltammogram and principal component analysis (PCA) reduced features. The SVM model with PCA-reduced features emerged as the optimal approach, achieving a classification accuracy of 97.9 % while reducing training time by 65.6 % (from 3.2 s to 1.1 s) and prediction time by 71.4 % (from 0.07 s to 0.02 s per sample) compared to using full voltammogram features. This reduction in computational complexity was achieved by decreasing the input dimensionality from 601 to 5 features through PCA, while maintaining high classification performance across all sample categories. This model demonstrated high sensitivity (>97 %) and specificity (>98 %) across all sample categories, with a notably low limit of detection for adulteration at 2.8 %. Characteristic peaks, such as the pantocrin peak at 0.25 V for authentic samples, provided a robust basis for differentiation. The method's effectiveness in detecting subtle adulterations was evidenced by its ability to identify samples with as low as 5 % adulteration. Furthermore, the approach showed excellent generalization, maintaining 97.0 % accuracy on an independent validation set. These findings highlight the potential of this technique for rapid, accurate, and cost-effective authentication of medicinal materials, addressing the growing challenge of counterfeit products in the pharmaceutical industry.