ATR/FT-IR and NIR Auto-correlation Spectroscopic Analysis of Powder Blending Uniformity of Low-content Magnesium Stearate and Potato Starch

Establishment of a NIR-based methodology for tracking the blend homogeneity of HTPB propellant slurry in the mixing process

The hydroxyl-terminated polybutadiene (HTPB) propellants with high level of solid loadings from 80 wt% to 90 wt% consist of aluminum (Al) powder, ammonium perchlorate (AP) and HTPB. The Al/AP/HTPB adhesive system full of solid grains appears high viscosity against flow. Therefore, the mixing is a crucial procedure in the production as it directly affects the structural integrity of the finished product. This work focused on the feasibility of tracking the blend homogeneity of Al/AP/HTPB adhesive system in the mixing process through using the near-infrared (NIR) spectroscopy and orthogonal partial least squares discrimination analysis (OPLS-DA). The OPLS-DA classification models were created by variable selection, spectral pretreatment and latent variables (LVs) optimization. It had been demonstrated that the developed models presented an excellent predictability with the root mean square error of cross-validation (RMSECV) for slurries in Ⅰ, Ⅱ groups of 0.1261 and 0.0789, respectively. Meanwhile, the well-fitted models for slurries in Ⅰ, Ⅱ groups with the squared correlation coefficient (R²) of 0.806 and 0.980, exhibited separately an acceptable predictive capability with the predictive squared correlation coefficient (Q²) > 0.5. Furthermore, Euclidean distance and move block standard deviation (MBSD) as reference methods were used to validate the predictive performance of the developed models with respect to the blend homogeneity of HTPB propellant slurry. The experimental results showed that the terminal time for each batch of slurry reaching to ideal uniformity predicted by Euclidean distance/MBSD and OPLS-DA were both at 26-30 min. Therefore, it had been proved that the method we proposed was a potential tool to monitor the variation of the uniform state of HTPB propellant slurry in the mixing process.

Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing of a Support Vector Machine

The objective of this work is to demonstrate the potential of near-infrared spectroscopy for common screening of falsified medicines in the field by means of a device-independent universal discrimination approach. In order to provide a useful discrimination tool to protect people from low-quality medical products, not only is a low-cost and portable screening device necessary, but a reference library is also essential. The authors believe that a device-dependent reference library inhibits near-infrared spectroscopy from becoming a popular screening tool. In this study, to develop a device-independent method, discrimination performance is evaluated using different devices for training and testing. The training data sets for the reference library were prepared using a bench-top Fourier transform near-infrared spectrophotometer, and predictive discrimination was performed using the spectral data by a low-cost and portable wavelength dispersive near-infrared spectrophotometer. A near-infrared spectrum-based support vector machine was used for these purposes, but the screening resulted in low accuracy thought to be caused by the intrinsically device-dependent features of the spectra data. Thus, principal component analysis was performed to collect the proper components to discriminate low-quality products from standard products. The principal component score-based support vector machine was able to produce highly accurate results, identifying falsified products with no false positive cases.

Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process

In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting process was controlled by the lower punch fill depth and the minimum distance between the upper and lower punches at compression, which were specifically related to the tablet weight and thickness, respectively. The feed-forward control of the process would be expected to provide some advantages for automated and semi-automated continuous pharmaceutical manufacturing. As a result, our model, using a combination of NIR spectra and the physical attributes of granules to control the distance between punches, resulted in respectable agreement between the predicted process parameters and actual settings to produce tablets of the desired thickness.

Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy

It is significant to analyze the blend homogeneity of cohesive powders during pharmaceutical manufacturing in order to provide the exact content of the active pharmaceutical ingredient (API) for each individual dose unit. In this paper, an online monitoring platform using an MEMS near infrared (NIR) sensor was designed to control the bin blending process of cohesive powders. The state of blend homogeneity was detected by an adaptive algorithm, which was calibration free. The online control procedures and algorithm’s parameters were fine-tuned through six pilot experiments and were successfully transferred to the industrial production. The reliability of homogeneity detection results was validated by 16 commercial scale experiments using 16 kinds of natural product powders (NPPs), respectively. Furthermore, 19 physical quality attributes of all NPPs and the excipient were fully characterized. The blending end time was used to denote the degree of difficulty of blending. The empirical relationships between variability of NPPs and the blending end time were captured by latent variable modeling. The critical material attributes (CMAs) affecting the blending process were identified as the particle shape and flowability descriptors of cohesive powders. Under the framework of quality by design (QbD) and process analytical technology (PAT), the online NIR spectroscopy together with the powder characterization facilitated a deeper understanding of the mixing process.