MIA and NIR Chemical Imaging for pharmaceutical product characterization

[EN] This paper presents a three step methodology based on the use of chemical oriented models (MCR and CLS) for extracting out the chemical distribution maps (CDMs) from hyperspectral images, afterwards performing multivariate image analysis (MIA) on the CDMs, and !nally extracting 'channel' and textural features from the score images related to quality characteristics These features show complementary properties to those directly obtained from the CDMs, since they take advantage of their internal correlation structure. The approach has been successfully applied to the evaluation of homogeneity and cluster presence of API in a novel formulation developed to improve the dissolution of poorly soluble drugs. © 2012 Elsevier B.V. All rights reserved.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02, and also by NSF-Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS, EEC-0540855) and the program NSF-Major Research Instrumentation grant 0821113.Prats-Montalbán, JM.; Jerez-Rozo, J.; Romanach, R.; Ferrer Riquelme, AJ. (2012). MIA and NIR Chemical Imaging for pharmaceutical product characterization. Chemometrics and Intelligent Laboratory Systems. 117(117):240-249. https://doi.org/10.1016/j.chemolab.2012.04.002S24024911711

Multivariate image analysis and near infrared chemical imaging for characterisation of micro-mixing in polymeric thin films

This article introduces a methodology for efficiently analysing hyperspectral images from pharmaceutical formulations. It provides information on the relative concentration and spatial distribution of active pharmamceutical ingredient and excipient in the formulation as well as on the internal correlation structure of the mixture by the use of multivariate image analysis, which provides insight information about the different underlying phenomena in the images (hence about the process).Prats-Montalbán, JM.; Jerez-Rozo, JI.; Romañach, RJ.; Ferrer, A. (2014). Multivariate image analysis and near infrared chemical imaging for characterisation of micro-mixing in polymeric thin films. NIR news. 25(6):4-7. doi:10.1255/nirn.1467S47256Osorio, J. G., Stuessy, G., Kemeny, G. J., & Muzzio, F. J. (2014). Characterization of pharmaceutical powder blends using in situ near-infrared chemical imaging. Chemical Engineering Science, 108, 244-257. doi:10.1016/j.ces.2013.12.027Tauler, R. (1995). Multivariate curve resolution applied to second order data. Chemometrics and Intelligent Laboratory Systems, 30(1), 133-146. doi:10.1016/0169-7439(95)00047-xPrats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Jérez Rozo, J. I., Zarow, A., Zhou, B., Pinal, R., Iqbal, Z., & Romañach, R. J. (2011). Complementary Near‐Infrared and Raman Chemical Imaging of Pharmaceutical Thin Films. Journal of Pharmaceutical Sciences, 100(11), 4888-4895. doi:10.1002/jps.22653Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026De Juan, A., Maeder, M., Hancewicz, T., Duponchel, L., & Tauler, R. (s. f.). Chemometric Tools for Image Analysis. Infrared and Raman Spectroscopic Imaging, 65-109. doi:10.1002/9783527628230.ch