title: An accurate quantitative analysis of polymorphs based on artificial neural networks.
abstract: Measurement precision based on homogeneous and accurate standard samples has been reported to result in significant improvement in the sensitivity and accuracy of the quantitative analysis of polymorphic mixtures. The purpose of this study was to further improve the accuracy of the quantitation based on data processing by artificial neural networks (ANNs), using such high quality standard samples. Homogeneous powder mixtures of alpha- and gamma-forms of indomethacin (IMC) at various ratios (0-50% alpha-form content) were subjected to X-ray powder diffractometry. The two diffraction peaks selected as the best combination in multiple linear regression (MLR) were used in the ANN with an extended Kalman filter as a training algorithm. The results obtained by ANN had better predictive accuracy at lower contents (0-5%) compared to those of MLR. ANNs for the diffraction data based on high quality standard samples provide an extremely precise and accurate quantification for polymorphic mixtures.
authors.affiliation: Technology Research and Development Center, Dainippon Sumitomo Pharma Co. Ltd., Kasugade-naka 3-1-98, Osaka 554-8558, Japan. takehiro-okumura@ds-pharma.co.jp
authors.forename: Takehiro
authors.initials: T
authors.lastname: Okumura
authors.forename: Masayuki
authors.initials: M
authors.lastname: Nakazono
authors.forename: Makoto
authors.initials: M
authors.lastname: Otsuka
authors.forename: Kozo
authors.initials: K
authors.lastname: Takayama
mesh_terms: D000465:Algorithms; D007213:Indomethacin / Q000031:analogs & derivatives / Q000737:chemistry*; D016014:Linear Models; D033362:Powder Diffraction; D011208:Powders; D012015:Reference Standards
chemical_list: D011208:Powders; D007213:Indomethacin