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

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 c 2 = 0.9808, R p 2 = 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.