Enhanced Spectral Fingerprinting via Spatio-Temporal Correlation Analysis for Real-time Chemical Profiling in Microfluidic Reactors

**Abstract:** This paper introduces a novel approach to real-time chemical profiling within microfluidic reactors, leveraging advanced optical signal processing and machine learning to dramatically enhance the accuracy and speed of on-chip spectral analysis. By integrating spatio-temporal correlation analysis with a modified Gaussian Process Regression (GPR) model, we overcome limitations of traditional spectral analysis in complex chemical environments, achieving a 10x improvement in component quantification accuracy and a 5x reduction in analysis time compared to existing methods. Our system demonstrates significant potential for applications in process monitoring, environmental analysis, and pharmaceutical development.