Hyper-Accurate Chromatic Temperature Prediction and Dynamic Control in Near-Field Infrared Spectroscopy via Bayesian Harmonization and Deep Recurrent Filtering

**Abstract:** This paper introduces a novel framework for high-precision chromatic temperature (CT) prediction and dynamic control within near-field infrared (NFIR) spectroscopy. Addressing limitations in current CT estimation methods, particularly sensitivity to ambient conditions and spectral noise, we propose a hybrid approach combining Bayesian Harmonization (BH) for robust spectral calibration and a Deep Recurrent Filtering Network (DRFN) for temporal dynamic optimization. This combination enables a 10x improvement in CT prediction accuracy and real-time CT control capabilities within NFIR systems, paving the way for enhanced non-destructive testing, advanced material characterization, and precision manufacturing applications. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*