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

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

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

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. Journal of Process Control, 5(4), 277-284. doi:10.1016/0959-1524(95)00019-mPatel, S. M., Doen, T., & Pikal, M. J. (2010). Determination of End Point of Primary Drying in Freeze-Drying Process Control. AAPS PharmSciTech, 11(1), 73-84. doi:10.1208/s12249-009-9362-7Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Nomikos, P. Statistical Process Control of Batch Processes. PhD diss., McMaster University, Hamilton, Ontario, 1995.Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Arteaga, F., & Ferrer, A. (2005). Framework for regression-based missing data imputation methods in on-line MSPC. Journal of Chemometrics, 19(8), 439-447. doi:10.1002/cem.946García-Muñoz, S., Kourti, T., & MacGregor, J. F. (2004). Model Predictive Monitoring for Batch Processes. Industrial & Engineering Chemistry Research, 43(18), 5929-5941. doi:10.1021/ie034020wCamacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22(10), 533-547. doi:10.1002/cem.1179Folch-Fortuny, A., Arteaga, F., & Ferrer, A. (2017). PLS model building with missing data: New algorithms and a comparative study. Journal of Chemometrics, 31(7), e2897. doi:10.1002/cem.2897Bharati, M. H.; MacGregor, J. F. Texture Analysis of Images Using Principal Component Analysis. Proceeding of SPIE/Photonics Conference on Process Imaging for Automatic Control, Boston, 2000; pp 27–37.Bellows, R. J., & King, C. J. (1972). Freeze-drying of aqueous solutions: Maximum allowable operating temperature. Cryobiology, 9(6), 559-561. doi:10.1016/0011-2240(72)90179-4Tsourouflis, S., Flink, J. M., & Karel, M. (1976). 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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

VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits

[EN] In this work an N-way partial least squares regression discriminant analysis (NPLS-DA) methodology is developed to detect symptoms of disease caused by Penicillium digitatum in citrus fruits (green mould) using visible/near infrared (VIS/NIR) hyperspectral images. To build the discriminant model a set of oranges and mandarins was infected by the fungus and another set was infiltrated just with water for control purposes. A double cross-validation strategy is used to validate the discriminant models. Finally, permutation testing is used to select a few bands offering the best correct classification rates in the validation set. The discriminant models developed here can be potentially implemented in a fruit packinghouse to detect infected citrus fruits at their arrival from the field with affordable multispectral (3 5 channels) cameras installed in the packinglines.This research was partially funded by the Spanish Ministry of Science and Innovation through grants DPI2011-28112-C04-02 and DPI2014-55276-C05-1R, and by INIA through grant RTA2012-00062-C04-01. In all cases with the support of European FEDER funds. Authors thank Lluis Palou from the Centro de Tecnologia Postcosecha at the IVIA for the help and supervision in the innoculation process of the fruits.Folch Fortuny, A.; Prats-Montalbán, JM.; Cubero-García, S.; Blasco Ivars, J.; Ferrer, A. (2016). VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits. Chemometrics and Intelligent Laboratory Systems. 156:241-248. https://doi.org/10.1016/j.chemolab.2016.05.005S24124815

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

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

Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time

[EN] This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product. (c) 2015 Elsevier B.V. All rights reserved.This work has been partially funded by INIA through research project RTA2012-00062-C04-01 with the support of European FEDER funds.Benalia, S.; Cubero, S.; Prats-Montalbán, JM.; Bernardi, B.; Zimbalatti, G.; Blasco, J. (2016). Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time. Computers and Electronics in Agriculture. 120:17-25. https://doi.org/10.1016/j.compag.2015.11.002S172512

How does the use of digital platforms impact on students marks at high education?

[EN] More and more universities use educational digital platforms to support teaching. Due to the covid-19 crisis, and with the online teaching approval, the usage of digital platforms in universities has gone from being an option to a necessity. Therefore, efforts have been made to promote the use of existing platforms in each university. This has required teachers not only to make an effort to transform and adapt in record time all the presential teaching to virtual teaching, but also has required professors to learn how to use some of the features of existing digital platforms that were not familiar to them. However, did using this type of platforms positively impact on high education students marks? This study aims to determine if such impact existed in engineering schools. More concretely, this paper focus on engineering students from different grades and levels from the Universitat Politècnica de València in Spain, and on the use of the PoliformaT digital platform, which is an adaptation of Sakai platform for the mentioned university. Statistics data provided by PoliformaT about the number of visits, number of events with the platform, and downloaded documents per student for several subjects from the School of Industrial Engineering and the School of Informatics for the course 2019-2020 are analysed. Multiple linear regression models on marks obtained by students and their activity on PoliformaT platform have been built to determine if the usage of digital platforms has any relation with the students¿ marks. A varied casuistry is observed in the results. Relations of the analysed items with marks have been identified in some of the analysed subjects, still these relations being moderate. In this sense, some unconsidered factors might be influencing these relations, being appropriate to analyse them in future research. On the other hand, it is necessary to re-analyse this same scenario in future courses when both, students and professors, have enough usage level of the employed teaching tools. Next course we expect that some of the deficiencies identified in this first study will be reduced after both actors become familiar to these digital tools.This work has been developed within the framework of the projects Coordinación metodológica a través de webs de apoyo en títulos ETSII para diferentes Competencias Trasversales of the call for Educational Innovation and Improvement Projects (PIME) with code PIME/19-20 Ref. 150, Ref. 151 and Ref. 152, in its institutional modality, promoted by the Vice-Rectorate for Studies, Quality and Accreditation and the Institute of Education Sciences of the Universitat Politècnica de València. The second author was supported by the Generalitat Valenciana (Conselleria de Educación, Investigación, Cultura y Deporte) under Grant ACIF/2019/021.Esteso, A.; Rodríguez-Sánchez, MDLÁ.; Alarcón Valero, F.; Prats-Montalbán, JM. (2021). How does the use of digital platforms impact on students marks at high education?. IATED Academy. 9559-9563. https://doi.org/10.21125/inted.2021.19989559956