Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1180379 | Chemometrics and Intelligent Laboratory Systems | 2015 | 9 Pages |
•Our goal is to improve predictive ability of iterative optimization technology (IOT).•IOT can predict the composition of a mixture without calibration.•Latent variable model approach is used for dimensionality reduction of spectra.•Genetic algorithm is applied to wavelength-region selection.•The performance is confirmed with a simulated dataset and two industrial datasets.
Process analytical technology plays an important role in the pharmaceutical industry. Calibration-free/minimum approach, iterative optimization technology (IOT), was previously proposed to predict the composition of a mixture while maintaining a similar prediction ability to calibration models such as a partial least squares. However, for the mixture case which includes similar structured materials, it would be essentially difficult to provide good prediction on mixture component ratio. This study presents a method which can improve the prediction ability of IOT through reducing dimensionality of spectra with optimal selection of wavelength. It involves using a latent variable model approach for dimensionality reduction of spectra in IOT and genetic algorithm-based wavelength selection for optimal wavelength-region selection. Through the analyses of numerical simulation data and real industrial data, it was confirmed that the proposed method achieved higher predictive accuracy compared to the traditional IOT.