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With the promulgation of industry guidelines, near infrared spectroscopy (NIRS) monitoring the uniformity of solid oral preparations has become one of the most reported studies. The purpose of the present study is to develop a near infrared (NIR) method for the in-line assessment of the active pharmaceutical ingredient (API) content in the fluidized bed mixing process, as well as to introduce a portable NIR sensor to monitor the fluidized bed production visually. A portable NIR sensor was used to monitor the content of the API in the fluidized bed mixing process. An experimental fluidized bed was performed and the corresponding NIR spectra were collected in-line. The spectrum selection method of cosine distance for in-line spectra was performed, and four wavelength optimization methods were compared to improve the accuracy of the model. Throughout the mixing process, the content uniformity of the API was a critical quality attribute (CQA). The partial least squares regression (PLSR) quantitative model of API content was established. The results showed that the cosine distance combined with the multi-variable selection methods (CC, VIP, RATC, and UVE) could obtain more useful information than the single method. The root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) values of the optimal PLSR model were 1.8588% and 1.5296%, respectively. Based on the results obtained, this study could be used as a reference for the pilot of in-line monitoring of miniature NIR sensors and the connection of key process parameters to realize intelligent production of solid preparations.

The application of process analysis and control is essential to enhance process understanding and ensure output material quality. The present study focuses on the stability of the feedback control system for a fluidized bed granulation process. Two strategies of dynamic moisture control (DMC) and static moisture control (SMC) were established based on the in-line moisture value obtained from the near-infrared sensor and control algorithm. The performance of these strategies on quality consistency control was examined using process moisture similarity analysis and principal component analysis. The stable moisture control performance and low batch-to-batch variability indicated that the DMC method was significantly better than other granulation methods. In addition, the investigation of robustness further showed that the implemented DMC method was able to produce predetermined target moisture values by varying process parameters. This study provides an advanced and simple control method for fluidized bed granulation quality assurance.