Deep residual network enabled smart hyperspectral image analysis and its application to monitoring moisture, size distribution and contents of four bioactive compounds of granules in the fluid-bed granulation process of Guanxinning tablets

Abstract

Bed collapse is a serious problem in a fluid-bed granulation process of traditional Chinese medicine. Moisture content and size distribution are regarded as two pivotal influencing factors. Herein, a smart hyperspectral image analysis methodology was established via deep residual network (ResNet) algorithm, which was then applied to monitoring moisture content, size distribution and contents of four bioactive compounds of granules in the fluid-bed granulation process of Guanxinning tablets. First, a hyperspectral imaging camera was utilized to acquire hyperspectral images of 132 real granule samples in the spectral region of 389–1020 nm. Second, the moisture content and size distribution of the granules were measured with a laser particle sizer and a fast moisture analyzer, respectively. Moreover, the contents of danshensu, ferulic acid, rosmarinic acid and salvianolic acid B of the granules were determined by using high-performance liquid chromatography-diode array detection. Third, ResNet quantitative calibration models were built, which consisted of convolutional layer, maxpooling layer, four convolutional blocks with residual learning function and two fully connected layers. As a result, the Rc2 values for the moisture content, granule sizes and contents of four bioactive compounds are determined to be 0.957, 0.986, 0.936, 0.959, 0.937, 0.938, 0.956, 0.889, 0.914 and 0.928, whereas the Rp2 values are calculated as 0.940, 0.969, 0.904, 0.930, 0.925, 0.928, 0.896, 0.849, 0.844, and 0.905, respectively. The predicted values matched well with the measured values. These findings indicated that ResNet algorithm driven hyperspectral image analysis is feasible for monitoring both the physical and chemical properties of Guanxinning tablets at the same time.