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Improved formulation of the latent variable model inversion¿based optimization problem for quality by design applications

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Improved formulation of the latent variable model inversion¿based optimization problem for quality by design applications

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Palací-López, D.; Villalba-Torán, PM.; Facco, P.; Barolo, M.; Ferrer, A. (2020). Improved formulation of the latent variable model inversion¿based optimization problem for quality by design applications. Journal of Chemometrics. 34(6):1-18. https://doi.org/10.1002/CEM.3230

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/169648

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Title: Improved formulation of the latent variable model inversion¿based optimization problem for quality by design applications
Author: Palací-López, Daniel Villalba-Torán, Pedro Miguel Facco, Pierantonio Barolo, Massimiliano Ferrer, Alberto
UPV Unit: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Abstract:
[EN] Latent variable regression model (LVRM) inversion is a relevant tool for finding, if they exist, different combinations of manufacturing conditions that yield the desired process outputs. Finding the best manufacturing ...[+]
Subjects: Latent variable modelling , Latent variable model inversion , Optimization in the latent space , Partial least-squares (PLS) , Quality by design (QbD)
Copyrigths: Reserva de todos los derechos
Source:
Journal of Chemometrics. (issn: 0886-9383 )
DOI: 10.1002/CEM.3230
Publisher:
John Wiley & Sons
Publisher version: https://doi.org/10.1002/CEM.3230
Project ID:
info:eu-repo/grantAgreement/UPV//2014.93231/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-82896-C2-1-R/ES/DISEÑO, CARACTERIZACION Y AJUSTE OPTIMO DE BIOCIRCUITOS SINTETICOS PARA BIOPRODUCCION CON CONTROL DE CARGA METABOLICA/
Thanks:
Agencia Estatal de Investigacion, Grant/Award Number: DPI2017-82896-C2-1-R; European Regional Development Fund; Ministerio de Economia, Industria y Competitividad, Gobierno de Espana; Universitat Politecnica de Valencia, ...[+]
Type: Artículo

References

FDA.Pharmaceutical CGMPs for the 21s Century—A Risk‐Based Approach; 2004.

Liu, J. J., & MacGregor, J. F. (2005). Modeling and Optimization of Product Appearance:  Application to Injection-Molded Plastic Panels. Industrial & Engineering Chemistry Research, 44(13), 4687-4696. doi:10.1021/ie0492101

Bonvin, D., Georgakis, C., Pantelides, C. C., Barolo, M., Grover, M. A., Rodrigues, D., … Dochain, D. (2016). Linking Models and Experiments. Industrial & Engineering Chemistry Research, 55(25), 6891-6903. doi:10.1021/acs.iecr.5b04801 [+]
FDA.Pharmaceutical CGMPs for the 21s Century—A Risk‐Based Approach; 2004.

Liu, J. J., & MacGregor, J. F. (2005). Modeling and Optimization of Product Appearance:  Application to Injection-Molded Plastic Panels. Industrial & Engineering Chemistry Research, 44(13), 4687-4696. doi:10.1021/ie0492101

Bonvin, D., Georgakis, C., Pantelides, C. C., Barolo, M., Grover, M. A., Rodrigues, D., … Dochain, D. (2016). Linking Models and Experiments. Industrial & Engineering Chemistry Research, 55(25), 6891-6903. doi:10.1021/acs.iecr.5b04801

MontgomeryDC.Applied Statistics and Probability for Engineers Third Edition; 2003; Vol. 37.

MacGregorJF.Empirical Models for Analyzing “Big” Data‐What´s the Difference. InSpring AIChE Conference; Orlando Florida USA 2018.

Liu, Z., Bruwer, M.-J., MacGregor, J. F., Rathore, S. S. S., Reed, D. E., & Champagne, M. J. (2011). Modeling and Optimization of a Tablet Manufacturing Line. Journal of Pharmaceutical Innovation, 6(3), 170-180. doi:10.1007/s12247-011-9112-8

MacGregor, J. F., Bruwer, M. J., Miletic, I., Cardin, M., & Liu, Z. (2015). Latent Variable Models and Big Data in the Process Industries. IFAC-PapersOnLine, 48(8), 520-524. doi:10.1016/j.ifacol.2015.09.020

Jaeckle, C. M., & MacGregor, J. F. (2000). Industrial applications of product design through the inversion of latent variable models. Chemometrics and Intelligent Laboratory Systems, 50(2), 199-210. doi:10.1016/s0169-7439(99)00058-1

García-Muñoz, S., Kourti, T., MacGregor, J. F., Apruzzese, F., & Champagne, M. (2006). Optimization of Batch Operating Policies. Part I. Handling Multiple Solutions#. Industrial & Engineering Chemistry Research, 45(23), 7856-7866. doi:10.1021/ie060314g

Tomba, E., Barolo, M., & García-Muñoz, S. (2012). General Framework for Latent Variable Model Inversion for the Design and Manufacturing of New Products. Industrial & Engineering Chemistry Research, 51(39), 12886-12900. doi:10.1021/ie301214c

Facco, P., Dal Pastro, F., Meneghetti, N., Bezzo, F., & Barolo, M. (2015). Bracketing the Design Space within the Knowledge Space in Pharmaceutical Product Development. Industrial & Engineering Chemistry Research, 54(18), 5128-5138. doi:10.1021/acs.iecr.5b00863

Bano, G., Facco, P., Bezzo, F., & Barolo, M. (2018). Probabilistic Design space determination in pharmaceutical product development: A Bayesian/latent variable approach. AIChE Journal, 64(7), 2438-2449. doi:10.1002/aic.16133

Palací-López, D., Facco, P., Barolo, M., & Ferrer, A. (2019). New tools for the design and manufacturing of new products based on Latent Variable Model Inversion. Chemometrics and Intelligent Laboratory Systems, 194, 103848. doi:10.1016/j.chemolab.2019.103848

MacGregor, J. F., & Bruwer, M.-J. (2008). A Framework for the Development of Design and Control Spaces. Journal of Pharmaceutical Innovation, 3(1), 15-22. doi:10.1007/s12247-008-9023-5

Jaeckle, C., & Macgregor, J. (1996). Product design through multivariate statistical analysis of process data. Computers & Chemical Engineering, 20, S1047-S1052. doi:10.1016/0098-1354(96)00182-2

Lakshminarayanan, S., Fujii, H., Grosman, B., Dassau, E., & Lewin, D. R. (2000). New product design via analysis of historical databases. Computers & Chemical Engineering, 24(2-7), 671-676. doi:10.1016/s0098-1354(00)00406-3

García-Muñoz, S., MacGregor, J. F., Neogi, D., Latshaw, B. E., & Mehta, S. (2008). Optimization of Batch Operating Policies. Part II. Incorporating Process Constraints and Industrial Applications. Industrial & Engineering Chemistry Research, 47(12), 4202-4208. doi:10.1021/ie071437j

Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109-130. doi:10.1016/s0169-7439(01)00155-1

Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304

Feltens, J. (2008). Vector method to compute the Cartesian (X, Y, Z) to geodetic ( $${\phi}$$ , λ, h) transformation on a triaxial ellipsoid. Journal of Geodesy, 83(2), 129-137. doi:10.1007/s00190-008-0246-5

Arteaga, F., & Ferrer, A. (2013). Building covariance matrices with the desired structure. Chemometrics and Intelligent Laboratory Systems, 127, 80-88. doi:10.1016/j.chemolab.2013.06.003

Arteaga, F., & Ferrer, A. (2010). How to simulate normal data sets with the desired correlation structure. Chemometrics and Intelligent Laboratory Systems, 101(1), 38-42. doi:10.1016/j.chemolab.2009.12.003

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