Ai Assisted Spectral Deconvlution for Rapid Drug Identification in Complex Matrices

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in pharmaceutical and bioanalytical research, particularly in spectral deconvolution for rapid drug identification in complex matrices. This study evaluates the effectiveness of AI-assisted spectral deconvolution techniques in improving analytical accuracy, sensitivity, and efficiency compared to conventional methods. A quantitative research design was adopted with a sample size of 94 spectral datasets collected using systematic sampling. Clinically approved bioanalytical validation parameters such as accuracy and recovery, precision (intra-day and inter-day), limit of detection (LOD), limit of quantification (LOQ), and specificity and selectivity were employed. AI-based models, including machine learning algorithms, were used to resolve overlapping spectral signals and minimize matrix interference. The findings indicate that AI-assisted methods significantly enhance peak resolution, reduce noise, and improve detection sensitivity. Additionally, AI models demonstrated strong predictive capability and reproducibility across varying conditions. The study concludes that AI-assisted spectral deconvolution provides a reliable, efficient, and scalable analytical approach for rapid drug identification in pharmaceutical, clinical, and forensic applications.