title: A criterion for assessing homogeneity distribution in hyperspectral images. Part 2: application of homogeneity indices to solid pharmaceutical dosage forms.
abstract: This article is the second of a series of two articles detailing the application of mixing index to assess homogeneity distribution in oral pharmaceutical solid dosage forms by image analysis. Chemical imaging (CI) is an emerging technique integrating conventional imaging and spectroscopic techniques with a view to obtaining spatial and spectral information from a sample. Near infrared chemical imaging (NIR-CI) has proved an excellent analytical tool for extracting high-quality information from sample surfaces. The primary objective of this second part was to demonstrate that the approach developed in the first part could be successfully applied to near infrared hyperspectral images of oral pharmaceutical solid dosage forms such as coated, uncoated and effervescent tablets, as well as to powder blends. To this end, we assessed a new criterion for establishing mixing homogeneity by using four different methods based on a three-dimensional (M×N×λ) data array of hyperspectral images (spectral standard deviations and correlation coefficients) or a two-dimensional (M×N) data array (concentration maps and binary images). The four methods were used applying macropixel analysis to the Poole (M(P)) and homogeneity (H%(Poole)) indices. Both indices proved useful for assessing the degree of homogeneity of pharmaceutical samples. The results testify that the proposed approach can be effectively used in the pharmaceutical industry, in the finished products (e.g., tablets) and in mixing unit operations for example, as a process analytical technology tool for the blending monitoring (see part 1).
authors: Applied Chemometrics Group, Departamento de Química, Edificio Cn, Facultad de Ciencias, Universitat Autònoma de Barcelona, 01893 Bellaterra, Barcelona, Spain.
authors: Juan G
authors: JG
authors: Rosas
authors: Marcelo
authors: M
authors: Blanco
mesh_terms: D000465:Algorithms; D001241:Aspirin / Q000032:analysis*; D002626:Chemistry, Pharmaceutical; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D005079:Excipients / Q000032:analysis; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D015999:Multivariate Analysis; D011208:Powders; D012371:Robotics; D019265:Spectroscopy, Near-Infrared; D013607:Tablets; D013678:Technology, Pharmaceutical / Q000379:methods*
chemical_list: D005079:Excipients; D011208:Powders; D013607:Tablets; D001241:Aspirin