Iterative optimization technology combined with wavelength selection based on excess absorption for a process analytical technology calibration–minimum approach

Abstract

a b s t r a c tProcess analytical technology (PAT) plays an important role in the pharmaceutical industry. Calibration-free/ minimum methods in PAT are expected to aid in a deeper understanding of processes in the early development stage of new drugs. Iterative optimization technology (IOT), an existing calibration-free method, is not able to predict the compositions of nonideal mixtures because the Beer-Lambert law does not hold in some wavelength regions. In this paper, we propose IOT with wavelength selection based on excess absorption (WLSEA), which is available with at least one calibration sample. Excess absorption (EA) is the residual between the measured and ideal spectra of a mixture, and includes noise and spectral change related to molecular interactions. WLSEA determines a threshold of EA that separates noise and spectral change by minimizing prediction errors of IOT. Consequently, WLSEA selects a set of regions where predictive accuracy of IOT is high. WLSEA-IOT can be applied to predict compositions of both ideal and nonideal mixtures that have ideal regions. The performance of the proposed IOT is verified by analyses with three types of mixture spectra. The proposed wavelength selection method will enhance both development of quantitative methods and analyses of molecular interactions with infrared spectroscopy.

Improvement of Prediction Errors Based on Standardized Infrared Spectra for a Calibration-free Approach

“…IOT is not effective when Beer's law does not hold at some wavelengths. The authors have been proposed two types of wavelength selection [Kaneko et al 2015, Shibayama et al 2016. One indicator to determine if Beer's law holds is excess absorbance, which is difference between the observed mixture spectrum and mixture spectrum calculated using eq.…”

“…(1). We have found that root mean square of xmix-xmix,pred (RMSE xmix) is an indicator to estimate prediction error in r [Shibayama et al 2016, Shibayama and. Once virtual mixture spectra are generated with eq.…”

“…However, IOT assumes that a mixture spectrum follows Beer's law at all wavelengths, though it is not possible for real mixtures because of molecular interactions' effects. Thus, wavelength selection is required to eliminate wavelengths where Beer's law does not hold [Kaneko et al 2015, Shibayama 2016]. However, once wavelength selection is conducted with a couple of spectra, the selected wavelengths are only fit to spectra similar to ones in nonideal mixtures that were used for wavelength selection.…”

Supervised Extended Iterative Optimization Technology for Estimation of Powder Compositions in Pharmaceutical Applications: Method and Lifecycle Management

“…Funatsu et al identified this weakness in the method and proposed two ways to mitigate it. One is to limit the region(s) of the spectra to be included into the analysis using of a genetic algorithm for variable selection, and the other is the modification of the inner relationship in BLL adding a nonlinear term. , The latter extension requires the estimation of a nonlinear pure spectrum and an exponent for the molar fractions.…”