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

• The study proposes a novel prediction method with infrared spectroscopy.
• The method enables to predict concentration with at least one calibration sample.
• The method will predict concentration well in both powder and liquid mixtures.
• The method provided accurate prediction in binary liquid mixtures.

Process 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.