Multilevel simultaneous component analysis for fault detection in multicampaign process monitoring: application to on-line high performance liquid chromatography of a continuous process.

A continuous process is monitored by on-line HPLC in four separate campaigns, ranging in duration from 10 to 104 h. Methods are reported that allow the study of variation over all four campaigns using Multilevel Simultaneous Components Analysis, which separates out the within- and between-campaign variation. In order to obtain control charts, Q- and D-statistics are combined with a within-campaign submodel (Simultaneous Components Analysis) to obtain a single model that is based only on within-campaign variation over all four campaigns.

[1]  Age K. Smilde,et al.  Multilevel component analysis of time-resolved metabolic fingerprinting data , 2005 .

[2]  J. J. Jansen,et al.  ASCA: analysis of multivariate data obtained from an experimental design , 2005 .

[3]  A. J. Morris,et al.  Confidence limits for contribution plots , 2000 .

[4]  Henk A. L. Kiers,et al.  Alternating least squares algorithms for simultaneous components analysis with equal component weight matrices in two or more populations , 1989 .

[5]  W. Krzanowski Between-Groups Comparison of Principal Components , 1979 .

[6]  S. Wold Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[7]  Alberto Ferrer,et al.  Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process , 2007 .

[8]  R. Bro,et al.  PARAFAC2—Part I. A direct fitting algorithm for the PARAFAC2 model , 1999 .

[9]  Marieke E. Timmerman,et al.  Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences , 2003 .

[10]  R. Brereton,et al.  Dynamic analysis of on-line high-performance liquid chromatography for multivariate statistical process control. , 2008, Journal of chromatography. A.

[11]  P. Eilers A perfect smoother. , 2003, Analytical chemistry.

[12]  H. Hotelling Analysis of a complex of statistical variables into principal components. , 1933 .

[13]  Uwe Kruger,et al.  Synthesis of T2 and Q statistics for process monitoring , 2004 .

[14]  S. de Jong,et al.  A framework for sequential multiblock component methods , 2003 .

[15]  José Manuel Andrade,et al.  Procrustes rotation in analytical chemistry, a tutorial , 2004 .

[16]  R. J. Adcock A Problem in Least Squares , 1878 .

[17]  B. Flury,et al.  Two generalizations of the common principal component model , 1987 .

[18]  Henk A. L. Kiers,et al.  Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure , 1994 .

[19]  Onno E. de Noord,et al.  Multilevel component analysis and multilevel PLS of chemical process data , 2005 .

[20]  S. J. Wierda Multivariate statistical process control—recent results and directions for future research , 1994 .

[21]  B. W. Wright,et al.  High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis. , 2003, Journal of chromatography. A.

[22]  Wojtek J. Krzanowski,et al.  Principal Component Analysis in the Presence of Group Structure , 1984 .

[23]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[24]  Roger E. Millsap,et al.  Component analysis in cross-sectional and longitudinal data , 1988 .

[25]  Hein Putter,et al.  The bootstrap: a tutorial , 2000 .

[26]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[27]  Marieke E Timmerman,et al.  Multilevel component analysis. , 2006, The British journal of mathematical and statistical psychology.

[28]  Rasmus Bro,et al.  PARAFASCA: ASCA combined with PARAFAC for the analysis of metabolic fingerprinting data , 2008 .

[29]  Age K. Smilde,et al.  Generalized contribution plots in multivariate statistical process monitoring , 2000 .

[30]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[31]  Richard G Brereton,et al.  On-line HPLC combined with multivariate statistical process control for the monitoring of reactions. , 2007, Analytica chimica acta.

[32]  Rasmus Bro,et al.  Automated alignment of chromatographic data , 2006 .

[33]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[34]  J. Gower Generalized procrustes analysis , 1975 .

[35]  B. Flury Common Principal Components in k Groups , 1984 .