To realize the real-time monitoring of the production process of Yangxue Qingnao Granules and improve the inter-batch consistency of granule quality in the granulation process, this study established a near-infrared quantitative prediction model of moisture, particle size, bulk density, and angle of repose in the fluidized bed granulation process of Yangxue Qingnao Granules based on near-infrared spectroscopy(NIRS). The near-infrared spectra were collected from 355 samples in 12 batches in the granulation process by integrating the sphere detection module of the near-infrared spectrometer. In combination with the pretreatment methods such as the first derivative, multiplicative scatter correction(MSC), and standard normal variate(SNV), the model was established by partial least squares(PLS) regression. The root mean square error of prediction(RMSEP) of moisture was 0.347 and R_P~2 was 0.935. The RMSEP of the D_(50) particle size model was 38.4 and R_P~2 was 0.980. The RMSEPs of bulk density and angle of repose were 0.018 8 and 0.879, with R_P~2 of 0.085 9 and 0.958. The results showed that the prediction of the PLS quantitative model combined with NIRS was accurate, and this model can be applied to the monitoring of key quality attributes in the fluidized bed granulation of Chinese medicinal granules in the production scale.
Keywords: Yangxue Qingnao Granules; angle of repose; bulk density; fluidized bed granulation; moisture; near-infrared spectroscopy; partial least squares; particle size; process analysis technology.