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

Abstract

Continuous manufacturing (CM) in the pharmaceutical industry has been paid attention to, because it is expected to reduce the costs of manufacturing. One of technical hurdles in CM is establishment and maintenance of predictive models for process monitoring. Conventionally, calibration models with optic spectra such as infrared or Raman spectra have been used as the predictive models for process monitoring. The calibrated models predict product qualities such as active pharmaceutical ingredient’s content, moisture content, particle size, and so on. However, any changes in rots, ratio of ingredients, or operation conditions may affect the relationship between sensor information and the product qualities, which results in deterioration of predictive models. Operators must update calibration models to assure predictive accuracy; however, calibration always requires data acquisition. Thus, the use of calibration models intrinsically increases economical costs. To tackle this problem, the authors have been attempting to propose a calibration-free approach with infrared spectra, which employs an equation in physics. To apply the calibration-free approach to real processes, it is important that a model provides accurate and reliable prediction. In this study, we propose a method to improve predictive accuracy of a calibration-free approach after assessing predictive errors using a rational indicator. We verified that the post-processing method succeeded in non-ideal binary mixtures.