Dual-Mode Fluorescent and Colorimetric Sensors Powered by Machine Learning for High-Security Authentication and Anti-Counterfeiting

The research paper investigates the development of dual-mode fluorescent and colorimetric sensors enhanced by machine learning techniques for high-security authentication and anti-counterfeiting applications. Counterfeiting remains a significant global issue, necessitating the advancement of robust and reliable authentication technologies. This study focuses on the integration of dual-mode sensors, which leverage both fluorescence and colorimetric responses to provide a comprehensive authentication solution. The sensors are designed to operate effectively under various conditions, offering enhanced detection capabilities compared to traditional methods. Machine learning algorithms are employed to analyze sensor data, improving the accuracy and efficiency of distinguishing between authentic and counterfeit items. The research encompasses the design and fabrication of sensors, including material selection and the dual-mode functionality, followed by extensive data acquisition and preprocessing. Various machine learning models, including Random Forest and Support Vector Machines (SVM), are trained and validated to optimize classification performance. The study provides detailed performance metrics and visualizations, such as confusion matrices and feature distributions, to illustrate the efficacy of the proposed system. The results demonstrate a significant improvement in authentication accuracy, highlighting the potential of integrating dual-mode sensors with machine learning for high-security applications. This work contributes to the advancement of anti-counterfeiting technologies, offering a novel approach to secure authentication in various industries.