Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by...

Near Infrared Chemical Imaging (NIR-CI) is an attractive technique in pharmaceutical development and manufacturing, where new and more robust methods for assessment of the quality of the final dosage products are continuously demanded. The pharmaceutical manufacturing process of tablets is usually composed by several unit operations such as blending, granulation, compression, etc. Having reliable, robust and timely information about the development of the process is mandatory to assure the quality of the final product. One of the main advantages of NIR-CI is the capability of recording a great amount of spectral information in short time. To extract the relevant information from NIR-CI images, several Chemometric methods, like Partial Least Squares Regression, have been demonstrated to be powerful tools. Nevertheless, these methods require a calibration phase. Developing new methods that do not need any prior calibration would be a welcome development. In this context, we study the potential usefulness of Classical Least Squares and Multivariate Curve Resolution models to provide quantitative and spatial information of all the ingredients in a complex tablet matrix composed of five components without the development of any previous calibration model. The distribution of the analytes in the surfaces, as well as the quantitative determination of the five components is studied and tested.