Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR--颗粒学报PARTICUOLOGY

Ma, C. Y., & Wang, X. Z. (2011). Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR. Particuology, 9(6), 589–597. https://doi.org/10.1016/j.partic.2011.03.010

Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR

Chao Y. Ma, Xue Z. Wang^*

Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds LS2 9JT, UK

10.1016/j.partic.2011.03.010

Volume 9, Issue 6, December 2011, Pages 589-597

Received 18 October 2010, Revised 19 March 2011, Accepted 23 March 2011, Available online 20 August 2011.

E-mail: x.z.wang@leeds.ac.uk

Highlights

Abstract

Near infrared spectroscopy (NIR) is now probably the most popular process analytical technology (PAT) for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm (GA) and support vector machine (SVM). GA is used for wavelength selection and SVM for mode building. The new GA–SVM approach is shown to outperform other methods including GA–PLS (partial least squares) and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of l-glutamic acid.

Graphical abstract

Keywords

Process analytical technology; Near infrared spectroscopy; Support vector machine; Genetic algorithm; Wavelength selection; Cooling crystallization

文章导读