title: Robust calibrations on reduced sample sets for API content prediction in tablets: definition of a cost-effective NIR model development strategy.
abstract: Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34-40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.
authors: Department of Analytical Chemistry and Pharmaceutical Technology, Center for Pharmaceutical Research, Vrije Universiteit Brussel - VUB, Laarbeeklaan 103, B-1090 Brussels, Belgium.
authors: Sigrid
authors: S
authors: Pieters
authors: Wouter
authors: W
authors: Saeys
authors: Tom
authors: T
authors: Van den Kerkhof
authors: Mohammad
authors: M
authors: Goodarzi
authors: Mario
authors: M
authors: Hellings
authors: Thomas
authors: T
authors: De Beer
authors: Yvan Vander
authors: YV
authors: Heyden
mesh_terms: D002138:Calibration; D008956:Models, Chemical; D004364:Pharmaceutical Preparations / Q000032:analysis*; D019265:Spectroscopy, Near-Infrared / Q000191:economics / Q000379:methods*; D013607:Tablets / Q000737:chemistry*; D014867:Water / Q000737:chemistry
chemical_list: D004364:Pharmaceutical Preparations; D013607:Tablets; D014867:Water