Comprehensive control charting applied to chromatography

TY - JOUR

T1 - Comprehensive control charting applied to chromatography

AU - Laursen, Kristoffer

AU - Rasmussen, Morten Arendt

AU - Bro, Rasmus

PY - 2011/5

Y1 - 2011/5

N2 - Multivariate statistical process control (MSPC) based for example on principal component analysis (PCA) can make use of the information contained in multiple measured signals simultaneously. This can be much more powerful in detecting variations due to special causes than conventional single variable statistical process control (SPC). Furthermore, the PCA based SPC simplifies monitoring as it limits the number of control charts to typically two charts rather than one for each signal. However, the derived MSPC statistics may suffer from lack of sensitivity if only one or a few variables deviate in a given situation. In this paper we develop a new comprehensive control (COCO) chart procedure that considers both univariate statistics and multivariate statistics derived from PCA in a single plot that allows easy visualization of the combined data from a univariate and multivariate point of view. The method is exemplified using twenty analytical chromatographic peak areas obtained for purity analysis of a biopharmaceutical drug substance. The new control chart procedure detected two different types of faulty events in this study.

AB - Multivariate statistical process control (MSPC) based for example on principal component analysis (PCA) can make use of the information contained in multiple measured signals simultaneously. This can be much more powerful in detecting variations due to special causes than conventional single variable statistical process control (SPC). Furthermore, the PCA based SPC simplifies monitoring as it limits the number of control charts to typically two charts rather than one for each signal. However, the derived MSPC statistics may suffer from lack of sensitivity if only one or a few variables deviate in a given situation. In this paper we develop a new comprehensive control (COCO) chart procedure that considers both univariate statistics and multivariate statistics derived from PCA in a single plot that allows easy visualization of the combined data from a univariate and multivariate point of view. The method is exemplified using twenty analytical chromatographic peak areas obtained for purity analysis of a biopharmaceutical drug substance. The new control chart procedure detected two different types of faulty events in this study.

U2 - 10.1016/j.chemolab.2011.04.002

DO - 10.1016/j.chemolab.2011.04.002

M3 - Journal article

SN - 0169-7439

VL - 107

SP - 215

EP - 225

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

IS - 1

ER -