Gráficos y parámetros de posición, dispersión y forma de estadística descriptiva Aspectos prácticos

Prats Montalbán, JM. (2015). Gráficos y parámetros de posición, dispersión y forma de estadística descriptiva Aspectos prácticos. http://hdl.handle.net/10251/5306

Statistical Process Control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection

The monitoring, fault detection and visualization of defects are a strategic issue for product quality. This paper presents a novel methodology based on the integration of textural Multivariate image analysis (MIA) and multivariate statistical process control (MSPC) for process monitoring. The proposed approach combines MIA and p-control charts, as well as T2 and RSS images for defect location and visualization. Simulated images of steel plates are used to illustrate the monitoring performance of it. Both approaches are also applied on real clover images.The authors want to thank Ole Mathis Kruse and Prof. Cecilia Futsaether, from the Norwegian University of Life Sciences (Dept. of Mathematic Sciences and Technology), for providing the real image data set. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI 2011-28112-C04-02.Prats Montalbán, JM.; Ferrer Riquelme, AJ. (2014). Statistical Process Control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection. Computers and Chemical Engineering. 71:501-511. https://doi.org/10.1016/j.compchemeng.2014.09.014S5015117

Gráficos y parámetros de posición, dispersión y forma de estadística descriptiva. Ejemplos de aplicación

Prats Montalbán, JM. (2015). Gráficos y parámetros de posición, dispersión y forma de estadística descriptiva. Ejemplos de aplicación. http://hdl.handle.net/10251/5304

MIA and NIR Chemical Imaging for pharmaceutical product characterization

[EN] This paper presents a three step methodology based on the use of chemical oriented models (MCR and CLS) for extracting out the chemical distribution maps (CDMs) from hyperspectral images, afterwards performing multivariate image analysis (MIA) on the CDMs, and !nally extracting 'channel' and textural features from the score images related to quality characteristics These features show complementary properties to those directly obtained from the CDMs, since they take advantage of their internal correlation structure. The approach has been successfully applied to the evaluation of homogeneity and cluster presence of API in a novel formulation developed to improve the dissolution of poorly soluble drugs. © 2012 Elsevier B.V. All rights reserved.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02, and also by NSF-Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS, EEC-0540855) and the program NSF-Major Research Instrumentation grant 0821113.Prats-Montalbán, JM.; Jerez-Rozo, J.; Romanach, R.; Ferrer Riquelme, AJ. (2012). MIA and NIR Chemical Imaging for pharmaceutical product characterization. Chemometrics and Intelligent Laboratory Systems. 117(117):240-249. https://doi.org/10.1016/j.chemolab.2012.04.002S24024911711

Segmentation techniques in image analysis: A comparative study

[EN] Nowadays, the detection, localization, and quantification of different kinds of features in an RGB image (segmentation) is extremely helpful for, e.g., process monitoring or customer product acceptance. In this article, some of the most commonly used RGB image segmentation approaches are compared in an orange quality control case study. Analysis of variance and correspondence analysis are combined for determining their most relevant differences and highlighting their pros and cons.Spanish Ministry of Economy and Competitiveness, Grant/Award Number: DPI2014-55276-C5-1R; Spanish National Institute for Agricultural and Food Research and Technology (INIA), Grant/Award Number: RTA2012-00062-C04-01; European Regional Development Fund (FEDER); Shell Global Solutions International B.V.Vitale, R.; Prats-Montalbán, JM.; López García, F.; Blasco Ivars, J.; Ferrer, A. (2016). Segmentation techniques in image analysis: A comparative study. Journal of Chemometrics. 30(12):749-758. https://doi.org/10.1002/cem.2854S7497583012Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Bevilacqua, M., Bucci, R., Magrì, A. D., Magrì, A. L., Nescatelli, R., & Marini, F. (2013). Classification and Class-Modelling. Chemometrics in Food Chemistry, 171-233. doi:10.1016/b978-0-444-59528-7.00005-3Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. doi:10.1017/cbo9780511809071MacQueen J Some methods for classification and analysis of multivariate observations Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability Berkeley, CA University of California Press 1967 281 297Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5), 786-804. doi:10.1109/proc.1979.11328Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2), 167-181. doi:10.1023/b:visi.0000022288.19776.77Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166-173. doi:10.1002/cem.785Postma, G. J., Krooshof, P. W. T., & Buydens, L. M. C. (2011). Opening the kernel of kernel partial least squares and support vector machines. Analytica Chimica Acta, 705(1-2), 123-134. doi:10.1016/j.aca.2011.04.025Vitale, R., de Noord, O. E., & Ferrer, A. (2014). A kernel-based approach for fault diagnosis in batch processes. Journal of Chemometrics, 28(8), S697-S707. doi:10.1002/cem.2629Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026Prats-Montalbán J Control estadístico de procesos mediante análisis multivariante de imágenes Ph.D. Thesis 2005López, F., Prats, J. M., Ferrer, A., & Valiente, J. M. (2006). Defect Detection in Random Colour Textures Using the MIA T2 Defect Maps. Image Analysis and Recognition, 752-763. doi:10.1007/11867661_68Ho, P.-G. (Ed.). (2011). Image Segmentation. doi:10.5772/628Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277-1294. doi:10.1016/0031-3203(93)90135-jMATLAB R2012b (8.0.0.783), Natick, USA: The Mathworks IncWold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1-3), 37-52. doi:10.1016/0169-7439(87)80084-9Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9Cao, D.-S., Liang, Y.-Z., Xu, Q.-S., Hu, Q.-N., Zhang, L.-X., & Fu, G.-H. (2011). Exploring nonlinear relationships in chemical data using kernel-based methods. Chemometrics and Intelligent Laboratory Systems, 107(1), 106-115. doi:10.1016/j.chemolab.2011.02.004Vitale, R., de Noord, O. E., & Ferrer, A. (2015). Pseudo-sample based contribution plots: innovative tools for fault diagnosis in kernel-based batch process monitoring. Chemometrics and Intelligent Laboratory Systems, 149, 40-52. doi:10.1016/j.chemolab.2015.09.013Hirschfeld, H. O. (1935). A Connection between Correlation and Contingency. Mathematical Proceedings of the Cambridge Philosophical Society, 31(4), 520-524. doi:10.1017/s030500410001351

On-line product quality and process failure monitoring in freeze-drying of pharmaceutical products

This is an Author's Accepted Manuscript of Domenico Colucci, José M. Prats-Montalbán, Alberto Ferrer & Davide Fissore (2021) On-line product quality and process failure monitoring in freeze-drying of pharmaceutical products, Drying Technology, 39:2, 134-147, DOI: 10.1080/07373937.2019.1614949 [copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/07373937.2019.1614949[EN] In this work the information provided by a noninvasive imaging sensor was used to develop two algorithms for real time fault detection and product quality monitoring during the Vacuum Freeze-Drying of single dose pharmaceuticals. Two algorithms based on multivariate statistical techniques, namely Principal Component Analysis and Partial Least Square Regression, were developed and compared. Five batches obtained under Normal Operating Conditions were used to train a reference model of the process; the classification abilities of these algorithms were tested on five more batches simulating different kind of faults. Good classification performances have been obtained with both algorithms. Coupling the information obtained from an infrared camera with that of other variables obtained from the PLC of the equipment, and from the textural analysis performed on the RGB images of the product, strongly improves the performances of the algorithms. The proposed algorithms can account for the heterogeneity of the batch and aim to reduce the off-specification products.This research work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the project DPI2017-82896-C2-1-R.Colucci, D.; Prats-Montalbán, JM.; Ferrer, A.; Fissore, D. (2021). On-line product quality and process failure monitoring in freeze-drying of pharmaceutical products. Drying Technology. 39(2):134-147. https://doi.org/10.1080/07373937.2019.1614949S134147392Jennings, T. A. (1999). Lyophilization. doi:10.1201/b14424PIKAL, M., SHAH, S., ROY, M., & PUTMAN, R. (1990). The secondary drying stage of freeze drying: drying kinetics as a function of temperature and chamber pressure☆. International Journal of Pharmaceutics, 60(3), 203-207. doi:10.1016/0378-5173(90)90074-eU. S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Veterinary, Medicine (CVM), Office of Regulatory Affairs (ORA), Pharmaceutical CGMPs. September 2004. Guidance for Industry, PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance, 2004. https://www.fda.gov/downloads/drugs/guidances/ucm070305.pdf (accessed Jan 2019).Barresi, A. A., Pisano, R., Fissore, D., Rasetto, V., Velardi, S. A., Vallan, A., … Galan, M. (2009). Monitoring of the primary drying of a lyophilization process in vials. Chemical Engineering and Processing: Process Intensification, 48(1), 408-423. doi:10.1016/j.cep.2008.05.004Patel, S. M., & Pikal, M. (2009). Process Analytical Technologies (PAT) in freeze-drying of parenteral products. Pharmaceutical Development and Technology, 14(6), 567-587. doi:10.3109/10837450903295116Fissore, D., Pisano, R., & Barresi, A. A. (2018). Process analytical technology for monitoring pharmaceuticals freeze-drying – A comprehensive review. Drying Technology, 36(15), 1839-1865. doi:10.1080/07373937.2018.1440590Barresi, A. A., Pisano, R., Rasetto, V., Fissore, D., & Marchisio, D. L. (2010). Model-Based Monitoring and Control of Industrial Freeze-Drying Processes: Effect of Batch Nonuniformity. Drying Technology, 28(5), 577-590. doi:10.1080/07373931003787934Pisano, R., Fissore, D., & Barresi, A. A. (2014). Intensification of Freeze-Drying for the Pharmaceutical and Food Industries. Modern Drying Technology, 131-161. doi:10.1002/9783527631704.ch05Fissore, D.; Pisano, R.; Barresi, A. On the Use of Temperature Measurement to Monitor a Freeze-Drying Process for Pharmaceuticals. Proceedings of IEEE International Instrumentation and Measurement Technology Conference “I2MTC 2017”, Torino, Italy, May 22–25, 2017; pp. 1276–1281.Bosca, S., Corbellini, S., Barresi, A. A., & Fissore, D. (2013). Freeze-Drying Monitoring Using a New Process Analytical Technology: Toward a «Zero Defect» Process. Drying Technology, 31(15), 1744-1755. doi:10.1080/07373937.2013.807431Grassini, S., Parvis, M., & Barresi, A. A. (2013). Inert Thermocouple With Nanometric Thickness for Lyophilization Monitoring. IEEE Transactions on Instrumentation and Measurement, 62(5), 1276-1283. doi:10.1109/tim.2012.2223312Emteborg, H., Zeleny, R., Charoud-Got, J., Martos, G., Lüddeke, J., Schellin, H., & Teipel, K. (2014). Infrared Thermography for Monitoring of Freeze-Drying Processes: Instrumental Developments and Preliminary Results. Journal of Pharmaceutical Sciences, 103(7), 2088-2097. doi:10.1002/jps.24017Van Bockstal, P.-J., Corver, J., De Meyer, L., Vervaet, C., & De Beer, T. (2018). Thermal Imaging as a Noncontact Inline Process Analytical Tool for Product Temperature Monitoring during Continuous Freeze-Drying of Unit Doses. Analytical Chemistry, 90(22), 13591-13599. doi:10.1021/acs.analchem.8b03788Lietta, E., Colucci, D., Distefano, G., & Fissore, D. (2019). On the Use of Infrared Thermography for Monitoring a Vial Freeze-Drying Process. Journal of Pharmaceutical Sciences, 108(1), 391-398. doi:10.1016/j.xphs.2018.07.025Velardi, S. A., & Barresi, A. A. (2008). Development of simplified models for the freeze-drying process and investigation of the optimal operating conditions. Chemical Engineering Research and Design, 86(1), 9-22. doi:10.1016/j.cherd.2007.10.007Pearson, K. (1901). LIII. 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Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957Van Sprang, E. N. ., Ramaker, H.-J., Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2002). Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science, 57(18), 3979-3991. doi:10.1016/s0009-2509(02)00338-xRato, T. J., Rendall, R., Gomes, V., Chin, S.-T., Chiang, L. H., Saraiva, P. M., & Reis, M. S. (2016). A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength. Industrial & Engineering Chemistry Research, 55(18), 5342-5358. doi:10.1021/acs.iecr.5b04851Rato, T. J., Rendall, R., Gomes, V., Saraiva, P. M., & Reis, M. S. (2018). A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part II—Assessing Detection Speed. Industrial & Engineering Chemistry Research, 57(15), 5338-5350. doi:10.1021/acs.iecr.7b04911Bharati, M. H., & MacGregor, J. F. (1998). Multivariate Image Analysis for Real-Time Process Monitoring and Control. Industrial & Engineering Chemistry Research, 37(12), 4715-4724. doi:10.1021/ie980334lPrats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Colucci, D., Prats-Montalbán, J. M., Fissore, D., & Ferrer, A. (2019). Application of multivariate image analysis for on-line monitoring of a freeze-drying process for pharmaceutical products in vials. Chemometrics and Intelligent Laboratory Systems, 187, 19-27. doi:10.1016/j.chemolab.2019.02.004Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Kourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778Camacho, J., Picó, J., & Ferrer, A. (2009). The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter? Analytica Chimica Acta, 642(1-2), 59-68. doi:10.1016/j.aca.2009.02.001Kourti, T., Nomikos, P., & MacGregor, J. F. (1995). Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. 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Application of multivariate image analysis for on-line monitoring of a freeze-drying process for pharmaceutical products in vials

[EN] A new Process Analytical Technology (PAT) has been developed and tested for on-line process monitoring of a vacuum freeze-drying process. The sensor uses an infrared camera to obtain thermal images of the ongoing process and multivariate image analysis (MIA) to extract the information. A reference model was built and different kind of anomalous events were simulated to test the capacity of the system to promptly identify them. Two different data structures and two different algorithms for the imputation of the missing information have been tested and compared. Results show that the MIA-based PAT system is able to efficiently detect on-line undesired events occurring during the vacuum freeze-drying process.The authors would like to acknowledge Elena Lietta for her support in the experimental investigation. This research work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the project DPI2017-82896-C2-1-R.Colucci, D.; Prats-Montalbán, JM.; Fisore, D.; Ferrer, A. (2019). Application of multivariate image analysis for on-line monitoring of a freeze-drying process for pharmaceutical products in vials. Chemometrics and Intelligent Laboratory Systems. 187:19-27. https://doi.org/10.1016/j.chemolab.2019.02.004S192718

Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods

[EN] Multivariate Curve Resolution (MCR) has been applied on prostate Diffusion Weighted-Magnetic Resonance Images (DW-MRI). Different physiological-based modeling approaches of the diffusion process have been submitted to validation by sequentially incorporating prior knowledge on the MCR constraints. Results validate the biexponential diffusion modeling approach and show the capability of the MCR models to find, characterize and locate the behaviors related to the presence of an early prostate tumor.The authors want to thank prof. Anna de Juan for her comments and help in using the software for this study. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI 2011-28112-004-02.Aguado Sarrió, E.; Prats-Montalbán, JM.; Sanz Requena, R.; Marti Bonmati, L.; Alberich Bayarri, Á.; Ferrer Riquelme, AJ. (2015). Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods. Chemometrics and Intelligent Laboratory Systems. 140:43-48. https://doi.org/10.1016/j.chemolab.2014.11.002S434814

Sparse N-way partial least squares with R package sNPLS

[EN] We introduce the R package sNPLS that performs N-way partial least squares (N-PLS) regression and Sparse (L1-penalized) N-PLS regression in three-way arrays. N-PLS regression is superior to other methods for three-way data based in unfolding, thanks to a better stabilization of the decomposition. This provides better interpretability and improves predictions. The sparse version also adds variable selection through L1 penalization. The sparse version of N-PLS is able to provide lower prediction errors and to further improve interpretability and usability of the N-PLS results. After a short introduction to both methods, the different functions of the package are presented by displaying their use in simulated and a real dataset.Research in this study was partially supported by the Conselleria de Educacion, Investigacion, Cultura y Deporte de la Generalitat Valenciana under the project PROMETEO/2016/093.Hervás-Marín, D.; Prats-Montalbán, JM.; Lahoz Rodríguez, AG.; Ferrer, A. (2018). Sparse N-way partial least squares with R package sNPLS. Chemometrics and Intelligent Laboratory Systems. 179:54-63. https://doi.org/10.1016/j.chemolab.2018.06.005S546317

MultiBaC: an R package to remove batch effects in multi-omic experiments

Motivation: Batch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases. Results: In this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction. Availability and implementation: MultiBaC package is available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) and GitHub (https://github.com/ConesaLab/MultiBaC.git). The data underlying this article are available in Gene Expression Omnibus repository (accession numbers GSE11521, GSE1002, GSE56622 and GSE43747).This work was funded by the Generalitat Valenciana through PROMETEO grants program for excellence research groups [PROMETEO 2016/093] and by the Spanish MICINN [PID2020-119537RB-I00]. Funding for open access charge: Universitat Politècnica de València