Process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control.
Robust fit-for-purpose multivariate calibration models are of critical importance to on-line/in-line quantitative monitoring of bio-chemicals and pharmaceuticals using spectroscopic instruments. Unlike in off-line assays, the spectroscopic measurements in on-line/in-line real-time applications are almost inevitably subjected to variations in measurement conditions (e.g. temperature) and samples' physical properties (e.g. cell density, particle size, sample compactness), which can invalidate the assumption of a linear relationship between the spectroscopic measurements and the concentrations of the target chemical components. This Biotech Highlight discusses the effects of such variations on spectroscopic measurements, and presents an overview of recent work on modelling and correcting of the detrimental effects of variations in measurement conditions and samples' physical properties.A number of application studies to complex datasets and an industrial plant demonstrate the methodologies and algorithms discussed.
Chen
Zengping
Z
Centre for Process Analytics and Control Technology, Newcastle University, Newcastle upon Tyne, UK.
Morris
Julian
J
D004345:Drug Industry / Q000379:methods*; D008027:Light; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D055095:Optics and Photonics; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D012542:Scattering, Radiation; D019265:Spectroscopy, Near-Infrared; D013057:Spectrum Analysis / Q000379:methods*; D013678:Technology, Pharmaceutical / Q000379:methods*; D013696:Temperature
D004364:Pharmaceutical Preparations