On-line HPLC combined with multivariate statistical process control for the monitoring of reactions.

On-line high performance liquid chromatography is used to monitor a steady state reaction over 35.2 h, with 197 chromatograms recorded as the reaction progresses. For each chromatogram, peaks are detected, baseline corrected, aligned and integrated to provide a peak table consisting of the intensities of 19 peaks, two corresponding to the reactants, one to the product and one to the solvent, the remaining being impurities, by-products or intermediates. D-charts and Q-charts from multivariate statistical process control are applied to the data to determine which samples are out of control and also provide diagnostic insight into why these samples are problematic. The D-chart is best at looking at overall performance issues such as problems with mixing or difficulties with instrument operation, whereas the Q-charts are best at detecting impurities during the reaction.

[1]  Richard G Brereton,et al.  Combined kinetics and iterative target transformation factor analysis for spectroscopic monitoring of reactions. , 2006, The Analyst.

[2]  Paul J. Gemperline,et al.  A priori estimates of the elution profiles of the pure components in overlapped liquid chromatography peaks using target factor analysis , 1984, J. Chem. Inf. Comput. Sci..

[3]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[4]  R. Brereton,et al.  Estimation of second order rate constants using chemometric methods with kinetic constraints. , 2002, The Analyst.

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

[6]  Elias S. Manolakos,et al.  Accurate estimation of the signal baseline in DNA chromatograms , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[7]  D. Massart,et al.  Resolution of Complex Liquid Chromatography−Fourier Transform Infrared Spectroscopy Data , 1997 .

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

[9]  Richard G. Brereton,et al.  Multivariate Pattern Recognition in Chemometrics: Illustrated by Case Studies , 1992 .

[10]  R. Brereton,et al.  Influence of noise, peak position and spectral similarities on resolvability of diode-array high-performance liquid chromatography by evolutionary factor analysis , 1994 .

[11]  Richard G. Brereton,et al.  Introduction to multivariate calibration in analytical chemistry , 2000 .

[12]  Yi-Zeng Liang,et al.  Sectional moving window factor analysis for diagnosing elution chromatographic patterns , 2003 .

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

[14]  S. Setarehdan Modified evolving window factor analysis for process monitoring , 2004 .

[15]  T. Passell Use of on-line ion chromatography in controlling water quality in nuclear power plants , 1994 .

[16]  S. Wold,et al.  The multivariate calibration problem in chemistry solved by the PLS method , 1983 .

[17]  R. E. Carlson,et al.  Monotone Piecewise Cubic Interpolation , 1980 .

[18]  Frans van den Berg,et al.  Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data , 2004 .

[19]  Laila Stordrange,et al.  The morphological score and its application to chemical rank determination , 2000 .

[20]  J. Carstensen,et al.  Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping , 1998 .

[21]  Richard G. Brereton,et al.  Chemometrics: Data Analysis for the Laboratory and Chemical Plant , 2003 .

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