An Integrated Engineering Framework for Real-Time Quality Monitoring and Dynamic Blending Optimization in Safflower Processing Using NIR-HSI and Reinforcement Learning

Abstract

The natural heterogeneity of raw materials, such as fluctuations in hydroxysafflor yellow A concentration, poses significant engineering challenges in standardizing safflower (Carthamus tinctorius L.) processing. This study proposes an integrated data-driven framework combining near-infrared hyperspectral imaging and reinforcement learning to achieve dynamic blending control. First, a rapid, non-destructive monitoring system was developed using a SNV-CARS-SVR model, achieving excellent predictive accuracy for HSYA content (R_c² = 0.9808, R_p² = 0.9218). Subsequently, the industrial blending process was modeled as a Markov Decision Process. A dynamic batching strategy utilizing the Proximal Policy Optimization algorithm was implemented to optimize the feedstock sequence. Experimental validation demonstrated that the proposed RL-driven blending system effectively suppressed feed concentration fluctuations, reducing the standard deviation of HSYA concentration from 0.4730 to 0.086 (an 81.8% reduction). This framework provides a scalable, intelligent control solution for continuous quality assurance in the processing of complex natural products.