Global regression model for moisture content determination using near-infrared spectroscopy

    loading  Checking for direct PDF access through Ovid


Graphical abstractHighlightsThe moisture content in three different freeze-dried drug products was determined.Linear and non-linear regression models were evaluated.Support Vector Regression model demonstrated the accuracy of the global NIR model.Near-infrared (NIR) global quantitative models were evaluated for the moisture content (MC) determination of three different freeze-dried drug products. The quantitative models were based on 3822 spectra measured on two identical spectrometers to include variability. The MC, measured with the reference Karl Fischer (KF) method, were ranged from 0.05% to 4.96%. Linear and non-linear regression models using Partial Least Square (PLS), Decision Tree (DT), Bayesian Ridge Regression (Bayes-RR), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were created and evaluated. Among them, the SVR model was retained for a global application. The Standard Error of Calibration (SEC) and the Standard Error of Prediction (SEP) were respectively 0.12% and 0.15%. This model was then evaluated in terms of total error and risk-based assessment, linearity, and accuracy. It was observed that MC can be fastly and simultaneously determined in freeze-dried pharmaceutical products thanks to a global NIR model created with different medicines. This innovative approach allows to speed up the validation time and the in-lab release analyses.

    loading  Loading Related Articles