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multivariate statistical process control in chromatography

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 1997年第1期38卷 51-62页

作者： Nijhuis, A deJong, S Vandeginste, BGM STATE UNIV GHENT DEPT ORGAN CHEM B-9000 GHENT BELGIUM

The need for multivariate statistical process control (MSPC) to check the performance of processes is becoming more important as the increasing number of variables that can be measured increases. In this paper Hotelling's T-2 statistic based on PCA is used for the development of multivariate control charts. The significant principal components (PCs) are used to develop the T-2-chart and the remaining PCs contribute to the congruent to SPE-chart. Results of applying multivariate control charts in chromatography are presented. The application concerns a capillary gas chromatography analysis of the fatty acid composition in BCR162 (soya-maize oil). (C) 1997 Elsevier Science B.V.

关键词： multivariate statistical process control Hotelling's T-2 statistic principal component analysis chromatography

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multivariate statistical process control in annual pharmaceutical product review

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JOURNAL OF process control 2018年 69卷 97-102页

作者： Torres, Adamastor R. de Oliveira, Antonio Diogenes P. Grangeiro Junior, Severino Fragoso, Wallace D. Univ Fed Paraiba Dept Chem Joao Pessoa Paraiba Brazil Governor Miguel Arraes LAFEPE Pharmaceut Lab State Pernambuco Recife PE Brazil

Annual Product Review recommending by the Food and Drug Administration and the International Conference on Harmonization is a periodic assessment of products quality to verifying the consistency of the production process and the quality of the finished product. In this work it was performed an annual product review using multivariate statistical process control. multivariate control charts was used for the periodic evaluation of the Hydrochlorothiazide 25 mg tablet, produced between 2009 and 2013 in the Pharmaceutical Laboratory of Pernambuco (Brazil). The procedure has allowed one to monitor and diagnose deviations in the production process. A corrective action was taken in the process by the Quality System in 2011 and the methodology applied was used to evaluate this action's effectiveness. Through the multivariate statistical process control it was possible to make an annual product review in accordance with the pharmaceutical industry regulatory requirements. (C) 2018 Published by Elsevier Ltd.

关键词： Annual product review multivariate statistical process control Principal component analysis Reconstruction-based contributions Hydrochlorothiazide

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multivariate statistical process control charts: An overview

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2007年第5期23卷 517-543页

作者： Bersimis, S. Psarakis, S. Panaretos, J. Athens Univ Econ & Business Dept Stat GR-10434 Athens Greece Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Univ Piraeus Dept Stat & Insurance Sci GR-18534 Piraeus Greece

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal. Copyright (C)2006 John Wiley & Sons, Ltd.

关键词： quality control process control multivariate statistical process control Hotelling's T-2 CUSUM EWMA PCA PLS

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multivariate statistical process control based on multiway locality preserving projections

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JOURNAL OF process control 2008年第7-8期18卷 797-807页

作者： Hu, Kunlun Yuan, Jingqi Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data seta It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling's T-2 (D) and the squared prediction error (SPE or Q) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： locality preserving projections multivariate statistical process control batch process monitoring fault detection and diagnosis moving window technique robustness

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multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process

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INTERNATIONAL JOURNAL OF PHARMACEUTICS 2017年第1-2期528卷 242-252页

作者： Silva, A. F. Sarraguca, M. C. Fonteyne, M. Vercruysse, J. De Leersnyder, F. Vanhoorne, V. Bostijn, N. Verstraeten, M. Vervaet, C. Remon, J. P. De Beer, T. Lopes, J. A. Univ Ghent Lab Pharmaceut Proc Analyt Technol Ghent Belgium Univ Porto Fac Farm Dept Ciencias Quim LAQV REQUIMTE Rua Jorge Viterbo Ferreira 228 P-4050313 Oporto Portugal Univ Ghent Lab Pharmaceut Technol Ghent Belgium Univ Lisbon Fac Farm Res Inst Med iMed Lisboa Av Prof Gama Pinto P-1649003 Lisbon Portugal

A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma (TM)-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T-2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Continuous manufacturing multivariate statistical process control Principal component analysis control charts Contribution plots

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multivariate statistical process control applied to sulfate determination by sequential injection analysis

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ANALYST 1997年第8期122卷 737-741页

作者： Rius, A Callao, MP Rius, FX Departament de Quimica Universitat Rovira i Virgili

A methodology was developed for determining sulfates in water at levels up to 500 mg l(-1) using a sequential injection analysis system. The multivariate calibration model avoids the need for the separation of interferents and sample pre-treatment. The trueness of the multivariate calibration model was assessed by comparing the predictions of the model with reference concentrations determined by a reference method using the joint interval test for the slope and intercept of the regression line with errors on both axes, The accuracy, evaluated by the root mean square error of prediction, reached 6.9%, multivariate statistical process control techniques were used to check the system's stability before developing the model and the validity of the model when it is used to predict the concentrations of unknown samples.

关键词： multivariate statistical process control sequential injection analysis multivariate calibration sulfates

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multivariate statistical process control Using LASSO

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JOURNAL OF THE AMERICAN statistical ASSOCIATION 2009年第488期104卷 1586-1596页

作者： Zou, Changliang Qiu, Peihua Nankai Univ Dept Stat Tianjin 300071 Peoples R China Univ Minnesota Sch Stat Minneapolis MN 55455 USA

This article develops a new multivariate statistical process control (SPC) methodology based on adapting the LASSO variable selection method to the SPC problem. The LASSO method has the sparsity property of being able to select exactly the set of nonzero regression coefficients in multivariate regression modeling, which is especially useful in cases where the number of nonzero coefficients is small. In multivariate SPC applications, process mean vectors often shift in a small number of components. Our primary goals are to detect such a shift as soon as it occurs and to identify the shifted mean components. Using this connection between the two problems, we propose a LASSO-based multivariate test statistic, and then integrate this statistic into the multivariate EWMA charting scheme for Phase II multivariate process monitoring. We show that this approach balances protection against various shift levels and shift directions, and thus provides an effective tool for multivariate SPC applications. This article has supplementary material online.

关键词： Hotelling's T(2) statistic Model selection multivariate statistical process control Penalized likelihood Regression-adjusted variable

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multivariate statistical process control in product quality review assessment – A case study

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Annales Pharmaceutiques Francaises 2017年第6期75卷 446-454页

作者： Kharbach, M. Cherrah, Y. Vander Heyden, Y. Bouklouze, A. Pharmaceutical and toxicological analysis research team Laboratory of pharmacology and toxicology Faculty of medicine and pharmacy University Mohammed V. Souissi avenue Med Belarbi El Alaoui BP 6203 Rabat 10000 Morocco Department of analytical chemistry and pharmaceutical technology CePhaR Vrije Universiteit Brussel (VUB) Laarbeeklaan 103 Brussels 1090 Belgium

According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional statistical process control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, multivariate statistical process control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T² 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment. © 2017 Académie Nationale de Pharmacie

关键词： Annual product review Hotelling's T2 multivariate statistical process control Principal component analysis statistical process control

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multivariate statistical process control of Boiler and Turbine-Generator units of a Thermoelectric Power Plant With Multiple Operating Set Points

Multivariate Statistical Process Control of Boiler and Turbi...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Sousa, Bruno M. Lebedev, Andre Ferreira, Joyce Aguiar, Webber E. Braga, Carmela M. P. Braga, Anisio R. Lemos, Andre P. Univ Fed Minas Gerais PPGEE Belo Horizonte MG Brazil GPEMG CEMIG Belo Horizonte MG Brazil Univ Fed Minas Gerais Belo Horizonte MG Brazil

ISBN: (纸本)9781479978007

multivariate statistical process control (MSPC) techniques require process stationarity as a main rule for design and monitoring. However, a critical process such as Boiler and Turbine-Generator units of Thermoelectric Power Plants, which is a multivariable complex system, features different types of non-stationary behavior and operational modes with constant changes of set points of key performance variables. A methodology based on MSPC and Principal Component Analysis (PCA) is presented with an adaptive mean estimator that deals with frequent changes of set points, both for design and just in time monitoring. Experimental results, based on data from a power plant, illustrate the application and use of the methodology.

关键词： multivariate statistical process control Hotelling's T-2 control chart principal component analysis thermoelectric power plant boiler turbine-generator unit

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Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2011年第2期105卷 195-206页

作者： Gonzalez-Martinez, J. M. Ferrer, A. Westerhuis, J. A. Univ Politecn Valencia Dept Estadist & Invest Operativa Aplicadas & Cali Valencia 46022 Spain Univ Amsterdam NL-1090 GE Amsterdam Netherlands

This paper addresses the real-time monitoring of batch processes with multiple different local time trajectories of variables measured during the process run. For Unfold Principal Component Analysis (U-PCA)-or Unfold Partial Least Squares (U-PLS)-based on-line monitoring of batch processes, batch runs need to be synchronized, not only to have the same time length, but also such that key events happen at the same time. An adaptation from Kassidas et al's approach [1] will be introduced to achieve the on-line synchronization of batch trajectories using the Dynamic Time Warping (DTW) algorithm. In the proposed adaptation, a new boundaries definition is presented for accurate on-line synchronization of an ongoing batch, together with a way to adapt mapping boundaries to batch length. A relaxed greedy strategy is introduced to avoid assessing the optimal path each time a new sample is available. The key advantages of the proposed strategy are its computational speed and accuracy for the batch process context. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisae cultivation are used to illustrate the performance of the proposed strategy. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Dynamic Time Warping On-line synchronization Batch processes multivariate statistical process control

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