Estimation of mass-based composition in powder mixtures using Extended Iterative Optimization Technology (EIOT)

Abstract

The Extended Iterative Optimization Technology (EIOT) method is proposed as an extension to Muteki's [I&ECR 2013;52(35):12258–12268] Iterative Optimization Technology to address deviations from Beer–Lambert's law in powders. The new method estimates the apparent spectrum for the pure species, rather than using the measured spectrum and augments Beer–Lambert's law with a bilinear term to capture the signature and strength of the nonchemical interferences. The proposed method has exhibited acceptable performance in spite of using a lean data set to estimate its parameters. The method provides robust and coherent estimates within the physical boundaries of the system and exhibits robustness to instrument transfer. The lean effort needed to build the EIOT method positions it as an attractive option in early stages of pharmaceutical drug product development. Its robustness to distinguish chemical from nonchemical signals implies a potential to lower the total cost of ownership for an EIOT-based solution in manufacturing. © 2018 American Institute of Chemical Engineers AIChE J, 65: 87–98, 2019.