Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis

Process analytical chemistry was recognized by Callis et al. (Analytical Chemistry, 59 (1987): 624A–635A) (1) as a field that extends well beyond real time measurements of process parameters. Process Analytical Technology is taking central stage with the 2004 guidance from the Food and Drug Administration, with a mandate much wider than real time measurements. The pharmaceutical industry is entering a new era. Chemometrics has played an integral part for the real time development of process analytical measurements (multivariate calibration) and it is ready to face the challenge of Process Analytical Technology in this wider definition. The scope of this paper is to demonstrate that multivariate, data based statistical methods, can play a critical role in process understanding, multivariate statistical process control, abnormal situation detection, fault diagnosis, process control and process scale-up, as linked to process analytical technology.

[1]  Theodora Kourti,et al.  Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start‐ups and grade transitions , 2003 .

[2]  Theodora Kourti,et al.  Process analysis and abnormal situation detection: from theory to practice , 2002 .

[3]  Chonghun Han,et al.  A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns , 2000 .

[4]  J. Macgregor,et al.  Control of batch product quality by trajectory manipulation using latent variable models , 2004 .

[5]  Kim H. Esbensen,et al.  New developments in acoustic chemometric prediction of particle size distribution—‘the problem is the solution’ , 2000 .

[6]  Alisa Rudnitskaya,et al.  Electronic tongues and their analytical application , 2002, Analytical and bioanalytical chemistry.

[7]  Theodora Kourti,et al.  Application of latent variable methods to process control and multivariate statistical process control in industry , 2005 .

[8]  J. Westerhuis,et al.  Multivariate modelling of the pharmaceutical two‐step process of wet granulation and tableting with multiblock partial least squares , 1997 .

[9]  Theodora Kourti Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications , 2003, Annu. Rev. Control..

[10]  Jerry Workman,et al.  Process analytical chemistry. , 2005, Analytical chemistry.

[11]  J. Macgregor,et al.  Mixture designs and models for the simultaneous selection of ingredients and their ratios , 2007 .

[12]  Johan A. Westerhuis,et al.  Multivariate modelling of the tablet manufacturing process with wet granulation for tablet optimization and in-process control , 1997 .

[13]  Garcia Salvador Munoz,et al.  BATCH PROCESS IMPROVEMENT USING LATENT VARIABLE METHODS , 2004 .

[14]  Theodora Kourti,et al.  Multivariate SPC for startups and grade transitions , 2002 .

[15]  A. Ferrer,et al.  Dealing with missing data in MSPC: several methods, different interpretations, some examples , 2002 .

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

[17]  Bruce R. Kowalski,et al.  Prediction of Product Quality from Spectral Data Using the Partial Least-Squares Method , 1984 .

[18]  C. E. Schlags,et al.  Multivariate statistical analysis of an emulsion batch process , 1998 .

[19]  P. A. Taylor,et al.  Synchronization of batch trajectories using dynamic time warping , 1998 .

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

[21]  P. A. Taylor,et al.  The impact of missing measurements on PCA and PLS prediction and monitoring applications , 2006 .

[22]  Theodora Kourti,et al.  Analysis, Monitoring and Fault Diagnosis of Industrial Processes Using Multivariate Statistical Projection Methods , 1996 .

[23]  S Albert,et al.  Multivariate statistical monitoring of batch processes: an industrial case study of fermentation supervision. , 2001, Trends in biotechnology.

[24]  Prashant Mhaskar,et al.  Predictive control of crystal size distribution in protein crystallization , 2005, Nanotechnology.

[25]  Alison J. Burnham,et al.  Frameworks for latent variable multivariate regression , 1996 .

[26]  I. Jolliffe A Note on the Use of Principal Components in Regression , 1982 .

[27]  T. Fearn,et al.  Near infrared spectroscopy in food analysis , 1986 .

[28]  Kim H. Esbensen,et al.  ACOUSTIC CHEMOMETRICS-FROM NOISE TO INFORMATION , 1998 .

[29]  Dmitry Kirsanov,et al.  Fermentation monitoring using multisensor systems: feasibility study of the electronic tongue , 2004, Analytical and bioanalytical chemistry.

[30]  John F. MacGregor,et al.  Establishing Multivariate Specification Regions for Incoming Materials , 2004 .

[31]  J. Macgregor,et al.  Experiences with industrial applications of projection methods for multivariate statistical process control , 1996 .

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

[33]  J. Macgregor,et al.  Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes , 2003 .

[34]  Barry Lennox,et al.  Real-time monitoring of an industrial batch process , 2006, Comput. Chem. Eng..

[35]  Charles E. Miller,et al.  Chemometrics for on‐line spectroscopy applications—theory and practice , 2000 .

[36]  Tadashi Makino,et al.  Application of an electronic nose system for evaluation of unpleasant odor in coated tablets. , 2005, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[37]  John F. MacGregor,et al.  Rapid Development of New Polymer Blends: The Optimal Selection of Materials and Blend Ratios , 2006 .

[38]  H. Siesler,et al.  Near-infrared spectroscopy:principles,instruments,applications , 2002 .

[39]  Steven D. Brown,et al.  Transfer of multivariate calibration models: a review , 2002 .

[40]  Eyal Dassau,et al.  New product design via analysis of historical databases , 2000 .

[41]  J. Macgregor,et al.  MULTIVARIATE IDENTIFICATION: A STUDY OF SEVERAL METHODS , 1992 .

[42]  Claus A. Andersson,et al.  Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data , 2004 .

[43]  J. Macgregor,et al.  Analysis of multiblock and hierarchical PCA and PLS models , 1998 .

[44]  R. Weber Optimization and Control , 2007 .

[45]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[46]  J. Macgregor,et al.  Development of inferential process models using PLS , 1994 .

[47]  Michael S. Dudzic,et al.  An industrial perspective on implementing on-line applications of multivariate statistics , 2004 .

[48]  B Lennox,et al.  Process monitoring of an industrial fed-batch fermentation. , 2001, Biotechnology and bioengineering.

[49]  Katherine A. Bakeev Process analytical technology , 2005 .

[50]  John F. MacGregor,et al.  Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops , 2006, Machine Vision and Applications.

[51]  Honglu Yu,et al.  Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods , 2003 .

[52]  Yale Zhang,et al.  Integrated monitoring solution to start-up and run-time operations for continuous casting , 2003, Annu. Rev. Control..

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

[54]  Theodora Kourti,et al.  Abnormal situation detection and projection methods—industrial applications. October 28–29, 2003. Hamilton, Ontario, Canada , 2005 .

[55]  Theodora Kourti,et al.  Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .

[56]  Theodora Kourti,et al.  Troubleshooting of an Industrial Batch Process Using Multivariate Methods , 2003 .

[57]  John F. MacGregor,et al.  Multivariate monitoring of batch processes using batch‐to‐batch information , 2004 .

[58]  Manish H. Bharati,et al.  Softwood Lumber Grading through On-line Multivariate Image Analysis Techniques , 2003 .

[59]  Theodora Kourti,et al.  Product transfer between sites using Joint-Y PLS , 2005 .

[60]  Theodora Kourti,et al.  Model Predictive Monitoring for Batch Processes , 2004 .

[61]  John F. MacGregor,et al.  Multivariate analysis and optimization of process variable trajectories for batch processes , 2000 .

[62]  Y. Moteki,et al.  Operation Planning and Quality Design of a Polymer Process , 1986 .

[63]  Theodora Kourti,et al.  Optimization of Batch Operating Policies. Part I. Handling Multiple Solutions , 2006 .

[64]  John F. MacGregor,et al.  Product design through multivariate statistical analysis of process data , 1998 .

[65]  Paul Nomikos,et al.  Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis World Batch Forum, Toronto, May 1996 , 1996 .

[66]  Margaret A. Nemeth Design and Analysis in Chemical Research , 2002, Technometrics.

[67]  John H. Kalivas,et al.  Which principal components to utilize for principal component regression , 1992 .

[68]  Manish H. Bharati,et al.  Automatic masking in multivariate image analysis using support vector machines , 2005 .

[69]  Nina F. Thornhill,et al.  Principal component analysis of spectra with application to acoustic emissions from mechanical equipment , 2002 .

[70]  P. A. Taylor,et al.  Missing data methods in PCA and PLS: Score calculations with incomplete observations , 1996 .

[71]  Jon Brumbaugh,et al.  DEPARTMENT OF HEALTH AND HUMAN SERVICES FOOD AND DRUG ADMINISTRATION , 2000 .

[72]  Barry Lennox,et al.  Integrated condition monitoring and control of fed-batch fermentation processes , 2004 .