PAT and compensating for non-linear effects in process spectroscopic data for improved process monitoring and control ((Research Highlight))

Julian Morris (1) , Zeng-Ping Chen (2)

1 School of Chemical Engineering and Advanced Materials
2 Chemical Engineering & Advanced Materials

Robust fit-for-purpose multivariate calibration models are of critical importance to on-line/in-line quantitative monitoring of speciality chemicals, 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 external process variables (e.g. temperature) and samples' physical properties (e.g. particle size, sample compactness), which invalidate the assumption of a linear relationship between the spectroscopic measurements and the concentrations of the target chemical components. This paper discussed the effects of these variations on spectroscopic measurements, and presents an overview of recent work on modelling and correcting of the detrimental effects of variations in external process variables (e.g. temperature) and samples' physical properties. A number of application studies to complex (messy) data sets and an industrial application demonstrate the methodologies and algorithms discussed.

Mots clés: Life Sciences

Julian Morris, Zeng-Ping Chen. PAT and compensating for non-linear effects in process spectroscopic data for improved process monitoring and control ((Research Highlight)). Biotechnology Journal, 2009, 4 (5), pp.610-n/a. ⟨10.1002/biot.200800314⟩. ⟨hal-00484125⟩