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

Abstract

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.