Development of calibration-minimum regression model with infrared spectra for mole fractions of pure components in mixture

Abstract

In pharmaceutical process, process state is monitored and managed by online and non-destructive spectroscopy testing and this methodology is focused on as Real Time Release Testing. Prediction of mole fractions of pure components in mixtures is an important issue for proper control in blending process. In order to predict mole fractions of pure components in mixtures with high accuracy, statistical models must be built from much amount of training data. However, only little amount of data is available, because taking much amount of data costs much. In this study, we proposed a calibration-minimum method that enables to predict mole fractions of pure components in non-ideal mixtures with high accuracy by expressing molecular interaction effect on a mixture spectrum as a function of mole fractions. The parameters in the proposed model equation can be inferred from little amount of data. The molecular interaction effect obtained from the proposed model is supposed to enhance further understanding of molecular interaction in complex mixtures.