In-line near infrared spectroscopy monitoring of pharmaceutical powder moisture in a fluidised bed dryer: An efficient methodology for chemometric model development

Accuracy Improvement of In-line Near-Infrared Spectroscopic Moisture Monitoring in a Fluidized Bed Drying Process

An exploratory analysis of a large representative dataset obtained in a fluidized bed drying process of a pharmaceutical powder has revealed a significant correlation of spectral intensity with granulate humidity in the whole studied range of 1091.8–2106.5 nm. This effect was explained by the dependence of powder refractive properties, and hence light penetration depth, on the water content. The phenomenon exhibited a close spectral similarity to the well-known stochastic variation of spectral intensities caused by the process turbulence (the so-called “scatter effect”). Therefore, any traditional scatter-corrective preprocessing incidentally eliminates moisture-correlated variance from the data. To preserve this additional information for a more precise moisture calibration, a time-domain averaging of spectral variables has been suggested. Its application resulted in a distinct improvement of prediction accuracy, as compared to the scatter-corrected data. Further improvement of the model performance was achieved by the application of a dynamic focusing strategy when adjusting the model to a drying process stage. Probe fouling was shown to have a minor effect on prediction accuracy. The study resulted in a considerable reduction of the root-mean-square error of in-line moisture monitoring to 0.1%, which is close to the reference method's reproducibility and significantly better than previously reported results.

Near-infrared spectroscopy monitoring and control of the fluidized bed granulation and coating processes-A review

The fluidized bed granulation and pellets coating technologies are widely used in pharmaceutical industry, because the particles made in a fluidized bed have good flowability, compressibility, and the coating thickness of pellets are homogeneous. With the popularization of process analytical technology (PAT), real-time analysis for critical quality attributes (CQA) was getting more attention. Near-infrared (NIR) spectroscopy, as a PAT tool, could realize the real-time monitoring and control during the granulating and coating processes, which could optimize the manufacturing processes. This article reviewed the application of NIR spectroscopy in CQA (moisture content, particle size and tablet/pellet thickness) monitoring during fluidized bed granulation and coating processes. Through this review, we would like to provide references for realizing automated control and intelligent production in fluidized bed granulation and pellets coating of pharmaceutical industry.

Multi-scale model of food drying: Current status and challenges

For a long time, food engineers have been trying to describe the physical phenomena that occur during food processing especially drying. Physics-based theoretical modeling is an important tool for the food engineers to reduce the hurdles of experimentation. Drying of food is a multi-physics phenomenon such as coupled heat and mass transfer. Moreover, food structure is multi-scale in nature, and the microstructural features play a great role in the food processing specially in drying. Previously simple macroscopic model was used to describe the drying phenomena which can give a little description about the smaller scale. The multiscale modeling technique can handle all the phenomena that occur during drying. In this special kind of modeling approach, the single scale models from bigger to smaller scales are interconnected. With the help of multiscale modeling framework, the transport process associated with drying can be studied on a smaller scale and the resulting information can be transferred to the bigger scale. This article is devoted to discussing the state of the art multi-scale modeling, its prospect and challenges in the field of drying technology. This article has also given some directions to how to overcome the challenges for successful implementation of multi-scale modeling.