Hyperspectral imaging based smart process analytical technology for rapid and nondestructive muti-objective monitoring in fluidized bed granulation of Ginkgo ester

Fluid-bed granulation (FBG) is a critical unit in the production of Ginkgo ester , a widely recognized therapeutic agent in cardiovascular medicine. However, persistent challenges such as moisture-induced bed collapse and heterogeneous binder distribution necessitate the need for advanced real-time monitoring solutions. This study presents a novel integration of hyperspectral imaging (HSI) with convolutional neural networks (CNNs) to establish a smart process analytical technology (PAT) platform for multi-objective quality assessment during Ginkgo ester granulation. A comprehensive dataset was obtained through analysis of 99 samples from ten batches of granules, where seven active pharmaceutical ingredients were quantitatively determined using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-TQ-MS). Critical quality attributes, including particle size distribution, content uniformity, specific surface area, moisture content, Hausner ratio, and angle of repose, were systematically characterized. Through comparative evaluation of seven CNN architectures and a Competitive Adaptive Reweighted Sampling-Partial Least Squares Regression (CARS-PLSR) model, DenseNet121 emerged as the optimal architecture, demonstrating superior predictive performance across 15 quality attributes ( R p 2 : 0.9557–0.9933; RMSEP: 0.0149–0.0284). The proposed HSI-CNN framework represents a significant advancement in real-time process monitoring capabilities, offering enhanced predictive accuracy and addressing critical limitations in current FBG operations. This research establishes a robust technological foundation for implementing advanced process analytical technologies in pharmaceutical manufacturing.