Process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control

Abstract

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.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

“…26 Particularly in cultures of microbes, filamentous and mammalian cells, which are multi-phase systems with solution and solid phases, uncontrolled changes in the physical properties of the samples can result in variations in the optical-path length, causing multiplicative light-scattering effects. 26 Pre-processing techniques are routinely applied to spectral data aiming at reducing those changes, thus keeping the linear model valid. Such methods include multiplicative scatter correction (MSC), standard normal variate (SNV), and spectral derivatives.…”

“…25 Unlike in off-line assays, on-line spectroscopic measurements in real-time applications are almost inevitably subject to variations in measurement conditions (e.g., temperature and other properties, such as cell density, particle size, and sample compactness), which can invalidate the linear relationship assumption between the spectroscopic measurements and the concentration of the target components. 26 Particularly in cultures of microbes, filamentous and mammalian cells, which are multi-phase systems with solution and solid phases, uncontrolled changes in the physical properties of the samples can result in variations in the opticalpath length, causing multiplicative light-scattering effects. 26 Pre-processing techniques are routinely applied to spectral data aiming at reducing those changes, thus keeping the linear model valid.…”