Development of a Process Analytical Technology(PAT) method using near-infrared spectroscopy system for evaluating an active coating process for a low-dose drug

Optimization of in-line near-infrared measurement for practical real time monitoring of coating weight gain using design of experiments

This study was conducted to develop an in-line near-infrared (NIR) spectroscopy approach that allows real time quantitative analysis of the coating weight gain on a moving tablet surface during a coating process where talc is used. A holder directly inserting a diffuse reflectance probe into a coating pan was designed, and the optimal measurement conditions were identified using the design of experiments (DoE). The surface of the probe was kept clean of coating droplets at a maximum distance between the probe and the holder of 272.5 mm, leading to the acquisition of accurate spectral data. Under this condition, partial least squares regression (PLSR) was developed using the spectra from 7197 to 6233 cm^-1, which covers the specific peaks for the core tablet and the coating solution. Under the same conditions, least squares regression (LSR) was developed using the univariate predictive analysis of the single absorption spectrum of talc at 7181 cm^-1. In a comparison of the accuracy of the two models, PLSR was found to be more accurate as a result of testing the significance of differences between these distributions in terms of the root mean square errors of prediction (RMSEP) using a randomization t-test. Additionally, it confirmed that the predicted weight gain using NIR spectroscopy was correlated with the coating thickness measured using micro-CT. In conclusion, this study developed an in-line NIR measurement approach for the real-time monitoring of the coating weight gain of tablets and optimized the conditions by evaluating the effect of various factors.

Near infrared system coupled chemometric algorithms for the variable selection and prediction of baicalin in three different processes

Characteristic variables are essential and necessary basis in model construction, and are related to the prediction result closely in near infrared spectroscopy (NIRS) analysis. However, the same compound usually has different characteristic variables for different analysis and it would be lower correlation between variables and structure in many researches. So, the accuracy and reliability are expected to improve by exploring characteristic variables in different spectrum analysis. In this study, competitive adaptive weighted resampling method (CARS) was applied to select characteristic variables related to baicalin from NIRS analysis data, which were applied to analysis of baicalin in three different processes including the herb, extraction process and concentration process of Scutellaria baicalensis. After application of CARS method, 70, 50 and 50 variables were selected respectively from three processes above. The selected variables were firstly analyzed by statistical methods that they were found to be consistent and correlated among three different processes after one-way analysis of variance test and Kendall's W. Partial least-squares (PLS) regression and extreme learning machine (ELM) models were constructed based on optimized data. Models after variable selection were less complicated and had better prediction results than global models. After comparison, CARS-PLS was suitable for the prediction of extraction process, while for the concentration process and herb, CARS-ELM performed better. The Rc value of the herb, extraction and concentration model were 0.9469, 0.9841 and 0.9675, respectively. The RSEP values were 4.54%, 6.96% and 8.37%, respectively. The results help to frame a theoretical basis for characteristic variables of baicalin.