Books

• Reis, M.S., Gao F. (editors), Advanced Process Monitoring for Industry 4.0.
Basel (Switzerland): MDPI, 2021. ISBN: 978-3-0365-2073-5.

• Reis, M.S., Estatística para a Melhoria de Processos – A Perspectiva Seis Sigma.
Coimbra: Imprensa da Universidade de Coimbra, 2016. (In Portuguese).

• Saraiva, P.M., J. d’Orey, P. Sampaio, M.S. Reis, C. Cardoso, J. Pinheiro, L. Tomé, O Futuro da Qualidade em Portugal.
Lisboa: APQ, 2010. ISBN: 978-972-9388-04-0. (In Portuguese).

Book Chapters

• Reis, M.S., Saraiva P.M., Data-Centric Process Systems Engineering for the Chemical Industry 4.0. Ed. by Kenett, R.; Swars, R.S.; Zonnenshain, A. Systems Engineering in the Fourth Industrial Revolution – Big Data, Novel Technologies, and Modern Systems Engineering, Chichester: Wiley, 2008, p. 337-370. ISBN: 978-1-119-51389-6. In Press.

• Reis, M.S., Multivariate image analysis. Ed. by Granato, D., Ares, G. Mathematical and Statistical Methods in Food Science and Technology. Chichester: Wiley-Blackwell, 2014, p. 201-218. ISBN: 978-1-118-43368-3.

• Reis, M.S., Bakshi B.R., Saraiva P.M., Denoising and Signal to Noise Enhancement: Wavelet Transform and Fourier Transform. Editado por Brown, S.; Tauler, R.; Walczak, R.. Comprehensive Chemometrics: Chemical and Biochemical Data Analysis. Oxford: Elsevier, 2009, Vol. 2, p. 25-55.

• Reis, M.S., Saraiva P.M., Multivariate and Multiscale Data Analysis. Editado por Coleman, S.; Greenfield, T.; Stewardson, D.; Montegomery, D.C.. Statistical Practice in Business and Industry, Statistics in Practice Series, Chichester: Wiley, 2008, p. 337-370.

Articles in peer reviewed journals and book series

/ 2023

• Espírito Santo, J., A. Ladeirinha, A. Alarcão, E. Strelet, M.S. Reis, R. Santos, L. Carvalho, Preoperative locoregional therapy may relate with stemness and distinct transitions between epithelial and mesenchymal states in hepatocellular carcinoma. Journal of Clinical and Experimental Hepatology. 14(1) (2024), art. 101268. DOI: 10.1016/j.jceh.2023.08.004.

• Strelet, E., Y. Peng, I. Castillo, R. Rendall, Z. Wang, M. Joswiak, B. Braun, L. Chiang, M.S. Reis, Multi-source and Multimodal Data Fusion for Improved Management of a Wastewater Treatment Plant. Journal of Environmental Chemical Engineering. 11(6) (2023), art. 111530. DOI: 10.1016/j.jece.2023.111530.

• Espírito Santo, J., A. Ladeirinha, A. Alarcão, E. Strelet, M.S. Reis, R. Santos, L, Carvalho, Hepatocellular carcinoma: tumor heterogeneity and recurrence after preoperative locoregional therapy. Medical Oncology. 40(12) (2023), art. 340. DOI: 10.1007/s12032-023-02208-1.

• Sansana, J., R. Rendall, M.N. Joswiak, I. Castillo, G. Miller, L.H. Chiang, M.S. Reis, A Functional Data-Driven Approach to Monitor and Analyze Equipment Degradation in Multiproduct Batch Processes. Process Safety and Environmental Protection. 180 (2023), p. 868-882. DOI: 10.1016/j.psep.2023.10.041.

• Rato, T.J., M.S. Reis, Real-time Risk Assessment and Surveillance for Early Prediction of Unplanned Shutdown Events. Chemical Engineering Science. 282 (2023), art. 119364. DOI: 10.1016/j.ces.2023.119364.

• Strelet, E., M.G.B.V. Rasteiro, P.M.G.A.M. Faia, M.S. Reis, A new process analytical technology soft sensor based on electrical tomography for real-time monitoring of multiphase systems. Analytica Chimica Acta. 1276 (2023), art. 341601. DOI: 10.1016/j.aca.2023.341601.

• Reis, M.S., T.J. Rato, Hybrid modelling through Latent Differential-Regression Analysis (LDRA) for predicting long-term equipment degradation in the Chemical Process Industry. Chemical Engineering Science. 278 (2023), art. 118902. DOI: 10.1016/j.ces.2023.118902.

• Branco, S., E. Dogruluk, J.G. Carvalho, M.S. Reis, J. Cabral, Persistence Landscapes - Implementing a Dataset Verification Method in Resource-Scarce Embedded Systems. Computers. 12(6) (2023), art. 110. DOI: 10.3390/computers12060110.

• Coutinho, J.P.L., L.O. Santos, M.S. Reis, Bayesian Optimization for Automatic Tuning of Digital Multi-loop PID Controllers. Computers & Chemical Engineering. 173 (2023), art. 108211. DOI: 10.1016/j.compchemeng.2023.108211

• Dias, P.A.N., R.J. Rodrigues, M.S. Reis, Fast Characterization of In-Plane Fiber Orientation at the Surface of Paper Sheets through Image Analysis. Chemometrics and Intelligent Laboratory Systems. 234 (2023), art. 104761. DOI: 10.1016/j.chemolab.2023.104761.

• Coutinho, J.P.L. M.S. Reis, D.F.M.G. Neves, F.P. Bernardo, Robust Optimization and Data-Driven Modeling of Tissue Paper Packing considering Cargo Deformation. Computers & Industrial Engineering. 175 (2023), art. 108898. DOI: 10.1016/j.cie.2022.108898.

• Paredes, R., M.S. Reis, Causal Network Inference and Functional Decomposition for Decentralized Statistical Process Monitoring: Detection and Diagnosis. Chemical Engineering Science. 267 (2023), art. 118338. DOI: 10.1016/j.ces.2022.118338.

• Duarte, B.P.M., A. Atkinson, S.P. Singh, M.S. Reis, Optimal Design of Experiments for Hypothesis Testing on Ordered Treatments via Intersection-Union Tests. Statistical Papers. 64 (2023), p. 587-615. DOI: 10.1007/s00362-022-01334-8.

/ 2022

• Reis, M.S., R. Rendall, T.J. Rato, C. Martins, P. Delgado, The truncated Q statistic for Statistical Process Monitoring of High-Dimensional Systems. Computer-Aided Chemical Engineering. (2022). Accepted.

• Paredes, R., T.J. Rato, L.O. Santos, M.S. Reis, Hierarchical Statistical Process Monitoring based on a Functional Decomposition of the Causal Network. Computer-Aided Chemical Engineering. (2022). Accepted.

• Barbosa, C., E. Ramalhosa, I. Vasconcelos, M.S. Reis, A. Mendes-Ferreira, Machine Learning Techniques Disclosure the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality. Microorganisms. 10(1), 107 (2022), p. 1-20. DOI: 10.3390/microorganisms10010107.

• Reis, M.S., E. Strelet, J. Sansana, M.J. Quina, L.M. Gando-Ferreira, T.J. Rato, A Federated Classification Approach of Waste Lubricant Oils in Geographically Distributed Laboratories. Industrial & Engineering Chemistry Research. 61(48) (2022), p. 17544-17556. DOI: 10.1021/acs.iecr.2c02293.

• Fernandes, N.C.P., T.J. Rato, M.S. Reis, Modeling in the Observable or Latent Space? A Comparison of Dynamic Latent Variable based Monitoring Methods for Sensor Fault Detection. Chemometrics and Intelligent Laboratory Systems. 231 (2022), art. 104684. DOI: 10.1016/j.chemolab.2022.104684.

• Yang, W.-T., M.S. Reis, V. Borodin, M. Juge, A. Roussy, An Interpretable Unsupervised Bayesian Network Model for Fault Detection and Diagnosis. Control Engineering Practice. 127 (2022), art. 105304. DOI: 10.1016/j.conengprac.2022.105304.

• Lordelo, R., J.R.S. Botelho, P.V. Morais, H.C. de Souza, R. Branco, A.M.A. Dias, M.S. Reis, Evaluation of the microbiological effectiveness of three accessible mask decontamination methods and their impact on filtration, air permeability and physicochemical properties. International Journal of Environmental Research and Public Health. 19(11) (2022), Article 6567. DOI: 10.3390/ijerph19116567. PMID: 35682153; PMCID: PMC9180249.

• Tomé, L.I.N., M.S. Reis, H.C. de Sousa, M.E.M. Braga, Chitosan-Xanthan Gum PEC-based Aerogels: a Chemically Stable PEC in scCO2 from the Screening of Natural Polyelectrolytes. Materials Chemistry and Physics. 287 (2022), Article 126294. DOI: 10.1016/j.matchemphys.2022.126294.

• Dias, T., R. Oliveira, P. Saraiva, M.S. Reis, Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling. Sensors. 22 (2022), Article 3374. DOI: 10.3390/s22103734.

• Branco, S., J.G. Carvalho, M.S. Reis, Nuno V. Lopes, Jorge Cabral, 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems. Sensors. 22 (2022), Article 3657. DOI: 10.3390/s22103657.

• Reis, M.S., B. Jiang, Predicting the Lifetime of Lithium-Ion Batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP). Chemical Engineering Science. 252 (2022), Article 117510. DOI: 10.1016/j.ces.2022.117510.

• Reis, M.S., P.M. Saraiva, Data-Driven Process Systems Engineering: Contributions to its consolidation following the path laid down by George Stephanopoulos. Computers & Chemical Engineering. 159 (2022), Article 107675. DOI: 10.1016/j.compchemeng.2022.107675.

• Barbosa, C., E. Ramalhosa, I. Vasconcelos, M.S. Reis, A. Mendes-Ferreira, Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality. Microorganisms. 10(1), 107 (2022), p. 1-20. DOI: 10.3390/microorganisms10010107.

/ 2021

• Strelet, E., Z. Wang, Y. Peng, I. Castillo, R. Rendall, B. Braun, M. Joswiak, L.H. Chiang, M.S. Reis, Multi-source Heterogeneous Data Fusion for Toxin Level Quantification. IFAC-PapersOnLine (2021), 54(3), p. 67-72. DOI: 10.1016/j.ifacol.2021.08.220.

• Sancho, A., J. C. Ribeiro, M.S. Reis, F.G. Martins, Cluster Analysis of Crude Oils with k-means based on their Physicochemical Properties. Computers & Chemical Engineering. 157 (2021), Article 107633. DOI: 10.1016/j.compchemeng.2021.107633.

• Gomes, V., R. Rendall, M.S. Reis, A. Mendes-Ferreira, P. Melo-Pinto, Determination of sugar, pH and anthocyanin contents in Port wine grape berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-linear Predictive Methods. Applied Sciences. 11(21), 10319 (2021), p. 1-25. DOI: 10.3390/app112110319.

• Reis, M.S., P.M. Saraiva, Data-Centric Process Systems Engineering: a Push Towards PSE 4.0. Computers & Chemical Engineering. 155 (2021), Article 107529. DOI: 10.1016/j.compchemeng.2021.107529.

• Gomes, V., M.S. Reis, F. Rovira-Más, A. Mendes-Ferreira, P. Melo-Pinto, Prediction of sugar content in Port wine vintage grapes using machine learning and hyperspectral imaging. Processes. 9(7), 1241 (2021), p. 1-16. DOI: 10.3390/pr9071241.

• Dias, T., R. Oliveira, P.M. Saraiva, M.S. Reis, Forecasting the Research Octane Number in a Continuous Catalyst Regeneration (CCR) Reformer. Quality and Reliability Engineering International. (2021), p. 1-19. DOI: 10.1002/qre.2968.

• De Souza, D.C.M., L. Cabrita, C.F. Galinha, M.S. Reis, PAT soft sensors for wide range prediction of key properties of diesel fuels and blending components for the oil industry. Computers & Chemical Engineering. 153 (2021), Article 107449. DOI: 10.1016/j.compchemeng.2021.107449.

• Reis, M.S., R. Rendall, T. J. Rato, C. Martins, P. Delgado, Improving the Sensitivity of Statistical Process Monitoring of Manifolds Embedded in High-Dimensional Spaces: the truncated-Q Statistic. Chemometrics and Intelligent Laboratory Systems. 215 (2021), art. 104369. DOI: 10.1016/j.chemolab.2021.104369.

• Sansana, J., M.N. Joswiak, I. Castillo, Z. Wang, R. Rendall, L.H. Chiang, M.S. Reis, Recent trends on hybrid modeling for Industry 4.0. Computers & Chemical Engineering. 151 (2021), Article 107365. DOI: 10.1016/j.compchemeng.2021.107365.

• Soccio, A., J.P. Barbosa, M.S. Reis, A Scalable Approach for the Efficient Segmentation of Hyperspectral Images. Chemometrics and Intelligent Laboratory Systems. 213 (2021), art. 104314. DOI: 10.1016/j.chemolab.2021.104314.

• De Souza, D.C.M., L. Cabrita, C.F. Galinha, T.J. Rato, M.S. Reis, A Spectral AutoML Approach for Industrial Soft Sensor Development: Validation in an Oil Refinery Plant. Computers & Chemical Engineering. 150 (2021), Article 107324. DOI: 10.1016/j.compchemeng.2021.107324.

• Cubo, C., P.M. Saraiva, P. Sampaio, M.S. Reis, 2017 World State of Quality: first worldwide results. Total Quality Management & Business Excellence. 32(3-4) (2021). DOI: 10.1080/14783363.2019.1575722.

• Reis, M.S., Discussion: Process Data Streams Aggregation vs Product Samples Aggregation. Journal of Quality Technology. 53(1) (2021), p. 33-37. DOI: 10.1080/00224065.2019.1611357.

/ 2020

• Rato, T.J., P. Delgado, C. Martins, M.S. Reis, First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes. Processes. 8:11 (2020), p. 1520. DOI: 10.3390/pr8111520.

• Rato, T.J., M.S. Reis, An Integrated Multiresolution Framework for Quality Prediction and Process Monitoring in Batch Processes. Journal of Manufacturing Systems. 57 (2020), p. 198-216. DOI: 10.1016/j.jmsy.2020.09.007.

• Martins, M.F., A. Honório-Ferreira, M.S. Reis, C. Cortez-Vaz, C.A. Gonçalves, Sialic acids expression in newborn rat lungs: implications for pulmonary developmental biology. Acta Histochemica. 122:8 (2020), Artilce 151626. DOI: 10.1016/j.acthis.2020.151626.

• Correia, A.A.S, L. Lopes, M.S. Reis, Advanced Predictive Modelling applied to the Chemical Stabilization of Soft Soils. Geotechnical Engineering. (2020), p. 1-11. DOI: 10.1680/jgeen.19.00295.

• Del Castillo, E., M.S. Reis, Bayesian Predictive Optimization of Profiles and Multi-Response Systems in the Process Industry: a Review and Extensions. Chemometrics and Intelligent Laboratory Systems. 206 (2020), art. 104121. DOI: 10.1016/j.chemolab.2020.104121.

• Rato, T.J., D. Neves, A. Antunes, M.S. Reis, A Systematic PAT Soft Sensor Screening and Development Methodology applied to the Prediction of Free Fatty Acids in Industrial Biodiesel Production. Fuel. 282 (2020), Article 118800. DOI: 10.1016/j.fuel.2020.118800.

• Dias, T., R. Oliveira, P.M. Saraiva, M.S. Reis, Predictive Analytics in the Petrochemical Industry: Research Octane Number (RON) forecasting and analysis in an Industrial Catalytic Reforming Unit. Computers & Chemical Engineering. 139 (2020), Article 106912. DOI: 10.1016/j.compchemeng.2020.106912.

• Martins, M.F., M.S. Reis, A. Honório-Ferreira, C.A. Gonçalves, Presence of N-acetylneuraminic acid in the lung during postnatal development. European Journal of Histochemistry. 64:3124 (2020), p. 148-155. DOI: 10.4081/ejh.2020.3124.

• Yang, W.-T., J. Blue, A. Roussy, Pinaton, J.; M.S. Reis, A Physics-Informed Run-to-Run Control for Semiconductor Manufacturing. Expert Systems with Applications. 155 (2020), p. 113424. DOI: 10.1016/j.eswa.2020.113424.

• Lourenço, A., M.S. Reis, J. Arnold, M.G. Rasteiro, Data-driven modelling of the complex interaction between flocculant properties and floc size and structure. Processes. 8(3), (2020), art. 349. DOI: 10.3390/pr8030349.

• Vieira, A.C., A.C. Pereira, J.C. Marques, M.S. Reis, Multi-target optimization of solid phase microextraction to analyse key flavour compounds in wort and beer. Food Chemistry. 317, (2020), art. 126466. DOI: 10.1016/j.foodchem.2020.126466.

• Campos, M.P., M.S. Reis, Data Preprocessing for Multiblock Modelling – A Systematization with New Methods. Chemometrics and Intelligent Laboratory Systems. 199 (2020), art. 103959. DOI: 10.1016/j.chemolab.2020.103959.

• Sansana, J., R. Rendall, Z. Wang, L.H. Chiang, M.S. Reis, Sensor Fusion with Irregular Sampling and Varying Measurement Delays. Industrial & Engineering Chemistry Research. 59(6) (2020), p. 2328-2340. DOI: 10.1021/acs.iecr.9b05105.

• Reis, M.S., Discussion of: “Industrial Statistics and Manifold Data”. Quality Engineering. 32(2) (2020), p. 168-172. DOI: 10.1080/08982112.2019.1683196.

• Grangeia, H.B., C. Silva, S.P. Simões, M.S. Reis, Quality by Design in Pharmaceutical Manufacturing: A Systematic Review of Current Status, Challenges and Future Perspectives. European Journal of Pharmaceutics and Biopharmaceutics. 147 (2019), p. 19-37. DOI: 10.1016/j.ejpb.2019.12.007.

• Saraiva, P.M., P. Sampaio, C. Cubo, M.S. Reis, Macroquality Measurement: World State of Quality and European Quality Scoreboard Approaches and Results. Total Quality Management & Business Excellence. 31(9-10) (2020). DOI: 10.1080/14783363.2018.1461012.

• Reis, M.S., R. Rendall, B. Palumbo, A. Lepore, C. Capezza, Predicting Ships’ CO2 Emissions using Feature-Oriented Methods. Applied Stochastic Models in Business and Industry. 36(1) (2020), p. 110-123. DOI: 10.1002/asmb.2477.

• Yang, W.-T., J. Blue, A. Roussy, Pinaton, J.; M.S. Reis, A Structure Data-Driven Framework for Virtual Metrology Modeling. IEEE Transactions on Automation Science and Engineering. 17(3) (2020), p. 1297-1306. DOI: 10.1109/TASE.2019.2941047.

• Sansana, J., R. Rendall, Z. Wang, L.H. Chiang, M.S. Reis, Multirate fusion of data sources with different quality. IFAC-PapersOnLine (2020), 53(2), p. 194-199. DOI: 10.1016/j.ifacol.2020.12.120

• Reis, M.S., T.J., Platforms for Automatic PAT Soft Sensor Development and Analysis. IFAC-PapersOnLine (2020), 53(2), p. 11332-11337. DOI: 10.1016/j.ifacol.2020.12.541.

• Sancho, A., J.C. Ribeiro, M.S. Reis, F.G. Martins, Cluster Analysis of Crude Oils Based on Physicochemical Properties. Computer-Aided Chemical Engineering. 48 (2020), p. 541-546. DOI: 10.1016/B978-0-12-823377-1.50091-4

/ 2019

• Fernandes, N.C.P., A. Romanenko, M.S. Reis, Mechanistic Modeling and Simulation for Process Data Generation. Industrial & Engineering Chemistry Research. 58(38) (2019), p. 17871-17884. DOI: 10.1021/acs.iecr.9b01752.

• Rato, T.J., M.S. Reis, Optimal Fusion of Industrial Data Streams with Different Granularities. Computers & Chemical Engineering. 130 (2019), p. 106564. DOI: 10.1016/j.compchemeng.2019.106564.

• Rato, T.J., M.S. Reis, SS-DAC: A Systematic Framework for Selecting the Best Modelling Approach and Pre-processing for Spectroscopic Data. Computers & Chemical Engineering. 128 (2019), p. 437-449. DOI: 10.1016/j.compchemeng.2019.05.036.

• Rato, T.J., M.S. Reis, Multiresolution Interval Partial Least Squares: A Framework for Waveband Selection and Resolution Optimization. Chemometrics and Intelligent Laboratory Systems. 186 (2019), p. 41-54. DOI: 10.1016/j.chemolab.2019.02.002.

• Cubo, C., P.M. Saraiva, P. Sampaio, M.S. Reis, 2017 World State of Quality: first worldwide results. Total Quality Management & Business Excellence. (2019). DOI: 10.1080/14783363.2019.1575722.

• Reis, M.S., Multiscale and Multi-granularity Process Analytics: A Review. Processes. 61, 7(2), (2019), p. 1-21. DOI: 10.3390/pr7020061.

• Rendall, R., L.H. Chiang, M.S. Reis, Data-driven Methods for Batch Data Analysis – A Critical Overview and Mapping on the Complexity Scale. Computers & Chemical Engineering. 124 (2019), p. 1-13. DOI: 10.1016/j.compchemeng.2019.01.014.

• Reis, M.S., A. C. Pereira, J.M. Leça, P.M. Rodrigues, J.C. Marques, Multi-Response and Multi-Objective Latent Variable Optimization of Modern Analytical Instrumentation for the Quantification of Chemically Related Families of Compounds: Case study - Solid Phase Microextraction (SPME) applied to the Quantification of Analytes with Impact on Wine Aroma. Journal of Chemometrics. 33(3) (2019), p. e3103. DOI: 10.1002/cem.3103.

• Rendall, R., I. Castillo, A. Schmidt, S.-T. Chin, L.H. Chiang, M.S. Reis, Wide Spectrum Feature Selection (WiSe) for Regression Model Building. Computers & Chemical Engineering. 121 (2019), p. 99-110. DOI: 10.1016/j.compchemeng.2018.10.005.

• Reis, M.S., G. Gins, T.J. Rato, Incorporation of Process-Specific Structure in Statistical Process Monitoring: a Review. Journal of Quality Technology. (2019). Accepted. 10.1080/00224065.2019.1569954.

• Reis, M.S., An Advanced Data-Centric Multi-Granularity Platform for Industrial Data Analysis. Computer-Aided Chemical Engineering. (2019). Accepted.

• Yang, W.-T., J. Blue, A. Roussy, M.S. Reis, J. Pinaton, Virtual Metrology Modeling Based on Gaussian Bayesian Network. Proceedings – Winter Simulation Conference. Vol. 2018-December (2019), p. 3574-3582. DOI: 10.1109/WSC.2018.8632485.

/ 2018

• Santos, C.P., T.J. Rato, M.S. Reis, Design of Experiments: A Comparison Study from the Non-Expert User’s Perspective. Journal of Chemometrics. (2018), p. e3087. DOI: 10.1002/cem.3087.

• Rendall, R., M.S. Reis, Which Regression Method to Use? Making Informed Decisions in “Data-Rich/Knowledge Poor” Scenarios – the Predictive Analytics Comparison framework (PAC). Chemometrics and Intelligent Laboratory Systems. 181 (2018), p. 52-63. DOI: 10.1016/j.chemolab.2018.08.004.

• Rato, T.J., M.S. Reis, Optimal Selection of Time Resolution for Batch Data Analysis. Part I: Predictive Modelling. AIChE Journal. 64 (2018), p. 3923-3933. DOI: 10.1002/aic.16361.

• Rendall, R., I. Castillo, B. Lu, B. Colegrove, M. Broadway, L. Chiang, M.S. Reis, Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks. Chemometrics and Intelligent Laboratory Systems. 180 (2018), p. 26-35. DOI: 10.1016/j.chemolab.2018.07.001.

• Pereira, A.C., M.S. Reis, J.M. Leça, P.M. Rodrigues, J.M. Marques, Definitive Screening Designs and Latent Variable Modelling for the Optimization of Solid Phase Microextraction (SPME): Case study - Quantification of Volatile Fatty Acids in Wines. Chemometrics and Intelligent Laboratory Systems. 179 (2018), p. 73-81. DOI: 10.1016/j.chemolab.2018.06.010.

• Reis, M.S., R.S. Kenett, Assessing the Value of Information of Data-Centric Activities in the Chemical Processing Industry 4.0. AIChE Journal. 64 (2018), p. 3868-3881. DOI: 10.1002/aic.16203.

• Lopes. A.L.C.V., A.F.G. Ribeiro, M.S. Reis, D.C.M. Silva, I. Portugal, C.M.S.G. Baptista. Distribution models for nitrophenols in a liquid-liquid system. Chemical Engineering Science. 189 (2018), p. 266-276. DOI: 10.1016/j.ces.2018.04.056.

• Rato, T.J., M.S. Reis, Building Optimal Multiresolution Soft Sensors for Continuous Processes. Industrial & Engineering Chemistry Research. 57(30) (2018), p. 9750-9765. DOI: 10.1021/acs.iecr.7b04623.

• Rato, T.J., R. Rendall, V. Gomes, P.M. Saraiva, M.S. Reis, A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part II – Assessing Detection Speed. Industrial & Engineering Chemistry Research. 57(15) (2018), p. 5338-5350. DOI: 10.1021/acs.iecr.7b04911.

• Campos, M.P., R. Sousa, M.S. Reis, Establishing the Optimal Blocks’ Order in SO-PLS: Stepwise SO-PLS and Alternative Formulations. Journal of Chemometrics. 32(8) (2018), e3032. DOI: 10.1002/cem.3032.

• Saraiva, P.M., C. Cubo, P. Sampaio, M.S. Reis, From another quality dimension. Quality Progress. December. 51(12) (2018), p. 50-57.

• Saraiva, P.M., P. Sampaio, C. Cubo, M.S. Reis, Macroquality Measurement: World State of Quality and European Quality Scoreboard Approaches and Results. Total Quality Management & Business Excellence. (2018). DOI: 10.1080/14783363.2018.1461012.

• Geert, G., J. Van Impe, M.S. Reis, Finding the optimal time resolution for batch-end quality prediction: MRQP – a framework for Multi-Resolution Quality Prediction. Chemometrics and Intelligent Laboratory Systems. 172 (2018), p. 150-158. DOI: 10.1016/j.chemolab.2017.12.006.

• Reis, M.S., Incorporating Systems Structure in Data-Driven High-Dimensional Predictive Modeling. Computer-Aided Chemical Engineering. 43 (2018), p. 1039-1044. DOI: 10.1016/B978-0-444-64235-6.50182-0.

• Reis, M.S., A Systematic Framework for Assessing the Quality of Information in Data-Driven Applications for the Industry 4.0. IFAC PapersOnLine (2018), 51(18), p. 43-48. DOI: 10.1016/j.ifacol.2018.09.244.

• Reis, M.S., T.J. Rato, Multiresolution Analytics for Large Scale Industrial Processes. IFAC PapersOnLine (2018), 51(18), p. 464-469. DOI: 10.1016/j.ifacol.2018.09.381.

• Yang, W.-T., J. Blue, A. Roussy, M.S. Reis, Advanced Run-to-Run Controller in Semiconductor Manufacturing with Real-time Equipment Condition. Proceedings of the 29th Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2018. (2018), p. 346-352. DOI: 10.1109/ASMC.2018.8373161.

/ 2017

• Rendall, R., B. Lu, I. Castillo, S.-T. Chin, L. H. Chiang, M.S. Reis, A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes. Industrial & Engineering Chemistry Research. 56 (30) (2017), p. 8590-8605. DOI: 10.1021/acs.iecr.6b04553.

• Reis, M.S., G. Gins, Industrial Process Monitoring in the Big Data/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis. Processes. 5(3), 35, (2017), p.1-16. DOI: 10.3390/pr5030035.

• Silva, B.M.A., S.Vicente, S. Cunha, J.F.J. Coelho, C. Silva, M.S. Reis, S. Simões, Retrospective Quality by Design (rQbD) applied to the Optimization of Orodispersible Films. International Journal of Pharmaceutics. 528 (1-2) (2017), p. 655-663. DOI: 10.1016/j.ijpharm.2017.06.054.

• Rendall, R., B. Lu, I. Castillo, S.-T. Chin, L. Chiang, M.S. Reis, Profile-driven Features for Offline Quality Prediction in Batch Processes. Computer-Aided Chemical Engineering. 40 (2017). p. 1501-1506. DOI: 10.1016/B978-0-444-63965-3.50252-X.

• Rato, T.J., M.S. Reis, Improved Fault Diagnosis in Online Process Monitoring of Complex Networked Processes: a Data-Driven Approach. Computer-Aided Chemical Engineering. 40 (2017), p. 1681-1686. DOI: 10.1016/B9780-444-63965-3.50282-8.

• Campos, M., R. Sousa, A.C. Pereira, M.S. Reis, Advanced predictive methods for wine age prediction: Part II - A comparison study of multiblock regression approaches. Talanta. 171 (2017), p. 132-142. DOI: 10.1016/j.talanta.2017.04.064.

• Rendall, R., A.C. Pereira, M.S. Reis, Advanced predictive methods for wine age prediction: Part I - a comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods. Talanta. 171 (2017). DOI: 10.1016/j.talanta.2016.10.062.

• Rato, T.J., J. Blue, J. Pinaton, M.S. Reis, Translation Invariant Multiscale Energy-based PCA (TIME-PCA) for Monitoring Batch Processes in Semiconductor Manufacturing. IEEE – Transactions on Automation Science and Engineering. 14(2) (2017), p. 894-904. DOI: 10.1109/TASE.2016.2545744

• Lopes, A., A. Ribeiro, M.S. Reis, D.C.M. Silva, I. Portugal, C.M.S.G. Baptista, Modelling the Distribution of Nitrophenols in a Liquid-Liquid System Representative of an Industrial Nitration Process. Chemical Engineering Transactions. 57 (2017), p. 1033-1038.

• Lepore, A., B. Palumbo, C. Capezza, R. Rendall, M.S. Reis, A comparison of advanced regression techniques for predicting CO2 emissions in the ship industry. Quality and Reliability Engineering International. Accepted. (2017). DOI: 10.1002/qre.2171.

• Pinheiro, C.T., V. Ascensão, M.S. Reis, M.J. Quina, L. M. Gando-Ferreira, A data-driven approach for the study of coagulation phenomena in waste lubricant oils and its relevance in alkaline regeneration treatments. Science of the Total Environment. (2017). DOI: 10.1016/j.scitotenv.2017.05.124.

• Rato, T.J., M.S. Reis, Multiresolution Soft Sensors (MR-SS): A New Class of Model Structures for Handling Multiresolution Data. Industrial & Engineering Chemistry Research. 56(13) (2017), p. 3640-3654.

• Rato, T.J., M.S. Reis, Markovian and Non-Markovian Sensitivity Enhancing Transformations for Process Monitoring. Chemical Engineering Science. 163 (2017), p. 223-233.

• Pinheiro, C.T., R. Rendall, M.J. Quina, M.S. Reis, L. M. Gando-Ferreira, Assessment and Prediction of Lubricant Oil Properties Using Infrared Spectroscopy and Advanced Predictive Analytics. Energy & Fuels. 31(1) (2017), p. 179-187. DOI: 10.1021/acs.energyfuels.6b01958.

• Soares, M.A.R., M.J. Quina, M.S. Reis, R.M. Quinta-Ferreira, Assessment of co-composting process with high load of an inorganic industrial waste. Waste Management. 59 (2017), p. 90-89. DOI: 10.1016/j.wasman.2016.09.044.

/ 2016

• Fernández-Ramos, C., R. Rodríguez-Gómez, M.S. Reis, O. Ballesteros, A. Navalón, J.L. Vílchez, Sorption, degradation and transport phenomena of alcohol ethoxysulfates in agricultural soils. Laboratory studies. Chemosphere. 171 (2016), p.661-670. DOI: 10.1016/j.chemosphere.2016.12.091.

• Reis, M.S., R.S. Kenett, A Structured Overview on the Use of Computational Simulators for Teaching Statistical Methods. Quality Engineering. (2016), p. 1-16. DOI: 10.1080/08982112.2016.1272122.

• Reis, M.S., R.D. Braatz, L. Chiang, Big Data – Challenges and Future Research Directions. Chemical Engineering Progress (Special Section dedicated to big data). (March, 2016), p. 46-50.

• Rato, T.J., R. Rendall, V. Gomes, S.-T. Chin, L. Chiang, P.M. Saraiva, M.S. Reis, A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I – Assessing detection strength. Industrial & Engineering Chemistry Research. 55(18) (2016), p. 5342-5358. DOI: 10.1021/acs.iecr.5b04851.

• Manco, G., S. Coleman, R. Goeb, A. Pievatolo, X. Tort-Martorell, M.S. Reis, How can SMEs benefit from Big Data? Challenges and a Path Forward. Quality and Reliability Engineering International. 32(6) (2016), p. 2151-2164.

• Pereira, A.C., M.J. Carvalho, A. Miranda, J.M. Leça, V, Pereira, F. Albuquerque, J.C. Marques, M.S. Reis, Modelling the ageing process: a novel strategy to analyze the wine evolution towards the expected features. Chemometrics and Intelligent Laboratory Systems. 154 (2016), p.176-184.

• Schmitt, E., T.J. Rato, M.S. Reis, B. de Ketelaere, M. Hubert, Parameter selection guidelines for adaptive PCA-based control. Journal of Chemometrics. 30(4) (2016), p. 163-176.

• Rato, T.J., E. Schmitt, B. de Ketelaere, M. Hubert, M.S. Reis, A Systematic Comparison of PCA-based Statistical Process Monitoring Methods for High-dimensional, Time-dependent Processes. AIChE Journal. 62(5) (2016), p. 1478-1493.

• Rendall, R., M.S. Reis, S.-T. Chin, L. Chiang, Managing Uncertainty Information for Improved Data-Driven Modelling. Computer-Aided Chemical Engineering, 38 (2016), p. 1575-1580

• Rendall, R., A. Pereira, M.S. Reis, An extended comparison study of large scale data-driven prediction methods based on variable selection, latent variables, penalized regression and machine learning. Computer-Aided Chemical Engineering, 38 (2016), p. 1629-1634.

/ 2015

• Schmitt, E., T.J. Rato, M.S. Reis, B. de Ketelaere, M. Hubert, Parameter selection guidelines for adaptive PCA-based control. Journal of Chemometrics. (2015).

• Rato, T.J., J. Blue, J. Pinaton, M.S. Reis, Translation Invariant Multiscale Energy-based PCA (TIME-PCA) for Monitoring Batch Processes in Semiconductor Manufacturing. IEEE – Transactions on Automation Science and Engineering. (2015).

• Rato, T.J., E. Schmitt, B. de Ketelaere, M. Hubert, M.S. Reis, A Systematic Comparison of PCA-based Statistical Process Monitoring Methods for High-dimensional, Time-dependent Processes. AIChE Journal. (2015). DOI: 10.1002/aic.15062.

• Reis, M.S., R. Rendall, S.-T. Chin, L. Chiang, Challenges in the Specification and Integration of Measurement Uncertainty in the Development of Data-Driven Models for the Chemical Processing Industry. Industrial & Engineering Chemistry Research. 54 (2015), p. 9159-9177.

• Leça, J.M., A.C. Pereira, A.C. Vieira, M.S. Reis, J.C. Marques, Optimal Design of Experiments Applied to Headspace Solid Phase Microextraction for the Quantification of Vicinal Diketones in Beer through Gas Chromatography-Mass Spectrometric detection. Analytica Chimica Acta. 887 (2015), p. 101-110.

• Oliver-Rodríguez, B., A. Zafra-Gómez, M.S. Reis, C. Verge, J.A. de Ferrer, M. Pérez-Pascual, J.L. Vílchez, Wide-range and Accurate Modeling of Linear Alkylbenzene Sulfonate (LAS) Adsorption/Desorption on Agricultural Soil. Chemosphere. 138 (2015), p. 148-155.

• Oliver-Rodríguez, B., A. Zafra-Gómez, M.S. Reis, B.P.M. Duarte, C. Verge, J.A. de Ferrer, M. Pérez-Pascual, J.L. Vílchez, Evaluation of Linear Alkylbenzene Sulfonate (LAS) Behaviour in Agricultural Soil Through Laboratory Continuous Studies. Chemosphere. 31 (2015), p. 1-8.

• Rato, T.J., M.S. Reis, On-line Process Monitoring using Local Measures of Association. Part I: Detection Performance. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 255-264.

• Rato, T.J., M.S. Reis, On-line Process Monitoring using Local Measures of Association. Part II: Design Issues and Fault Diagnosis. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 265-275.

• Reis, M.S., An Integrated Multiscale and Multivariate Image Analysis Framework for Process Monitoring of Colour Random Textures: MSMIA. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 36-48.

• Rendall, R., M.S. Reis, A.C. Pereira, C. Pestana, V. Pereira, J.C. Marques, Chemometric Analysis of the Volatile Fraction Evolution of Portuguese Beer under Shelf Storage Conditions. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 131-142.

• Rato, T.J., M.S. Reis, Multiscale and Megavariate Monitoring of the Process Networked Structure: M2NET. Journal of Chemometrics. 29(5) (2015), p. 309-322.

/ 2014

• Rato, T.J., M.S. Reis, Non-Causal Data-Driven Monitoring of the Process Correlation Structure: A Comparison Study with New Methods. Computers & Chemical Engineering. 71 (2014), p. 307-322.

• Rendall, R.R., M.S. Reis, A Comparison Study of Single-Scale and Multiscale Approaches for Data-Driven and Model-Based Online Denoising. Quality and Reliability Engineering International. 30(7) (2014), p. 935-950

• Rato, T.J., M.S. Reis, Sensitivity Enhancing Transformations for Large-Scale Process Monitoring. Computer-Aided Chemical Engineering. 34 (2014), p. 643-648.

• Moita, R.D., V.M. Gomes, P.M. Saraiva, M.S. Reis, An Extended Comparative Study of Two- and Three-Way Methodologies for the On-line Monitoring of Batch Processes. Computer-Aided Chemical Engineering. 34 (2014), p. 517-522.

• Rato, T.J., M.S. Reis, Sensitivity Enhancing Transformations for Monitoring the Process Correlation Structure. Journal of Process Control. 24 (2014), p. 905-915.

/ 2013

• G. Nogueira, Silva, D.C.M. Silva, M.S. Reis, C.M.S.G. Baptista, Prediction of the by-products formation in the adiabatic industrial benzene nitration process. Chemical Engineering Transactions. 32 (2013), p. 1249-1254.

• Reis, M.S., Applications of a new empirical modelling framework for balancing model interpretation and prediction accuracy through the incorporation of clusters of functionally related variables. Chemometrics and Intelligent Laboratory Systems. (2013), dx.doi.org/10.1016/j.chemolab.2013.05.007.

• Rato, T.J., M.S. Reis, Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR). Chemometrics and Intelligent Laboratory Systems. 125 (2013), p. 101-108.

• Rato, T.J., M.S. Reis, Defining the structure of DPCA models and its impact on process monitoring and prediction activities. Chemometrics and Intelligent Laboratory Systems. 125 (2013), p. 74-86.

• Reis, M.S., Network-Induced Supervised Learning: Network-Induced Classification (NI-C) and Network-Induced Regression (NI-R). AIChE Journal. 59(5) (2013), p. 1570-1587.

• Pinheiro, I., P.J. Ferreira, F. A. Garcia, M.S. Reis, A.C. Pereira, C. Wandrey, H. Ahmadloo, J.L. Amaral, D. Hunkeler, M.G. Rasteiro, An experimental design methodology to evaluate the importance of different parameters on flocculation by polyelectrolytes. Powder Technology. 238 (2013), p. 2-13.

/ 2012

• Gomes, V.M., A.C. Pereira, P. M. Saraiva, M.S. Reis, Development of Generalized Platforms for the Analysis of Complex Datasets.Quality and Reliability Engineering International. 28 (2012), p. 508-523.

• Reis, M.S., P. M. Saraiva, Prediction of Profiles in the Process Industries.Industrial & Engineering Chemistry Research. 51 (2012), p. 4524-4266.

• Reis, M.S., P. Delgado, A large-scale statistical process control approach for the monitoring of electronic devices assemblage.Computers and Chemical Engineering. 39 (2012), p. 163-169.

/ 2011

• Pereira, A.C., M.S. Reis, P.M. Saraiva, J.C. Marques, Development of a fast and reliable method for long- and short-term wine age prediction. Talanta. 86 (2011), p. 293-304.

• Pereira, A.C., M.S. Reis, P.M. Saraiva, J.C. Marques, Madeira wine ageing prediction based on different analytical techniques: UV–vis, GC-MS, HPLC-DAD. Chemometrics and Intelligent Laboratory Systems. 105 (2011), p. 43-55.

• Cantarero, S., A. Zafra-Gómez, O. Ballesteros, A. Navalón, M.S. Reis, P.M. Saraiva, J.L. Vílchez, Environmental monitoring study of linear alkylbenzene sulfonates and insoluble soap in Spanish sewage sludge samples.Journal of Environmental Science and Health Part A. 46 (2011), p. 617-626.

• Rato, T.J., M.S. Reis, Statistical Process Control of Multivariate Systems with Autocorrelation. In Computer-Aided Chemical Engineering, vol. 29 – Parte A. Ed. by E.N. Pistikopoulos, M.C. Georgiadis and A. Kokossis. Amsterdam: Elsevier (2011). ISBN: 978-0-444-53711-9, p 497-501.

/ 2010

• Rato, T.J. M.S. Reis, Statistical Monitoring of Control Loops Performance: An Improved Historical-data Benchmark Index.Quality and Reliability Engineering International. 26:8 (2010), p. 831-844.

• Reis, M.S., A. Bauer, Image-based classification of paper surface quality using wavelet texture analysis.Computers and Chemical Engineering. 34 (2010), p. 2014-2021.

• Reis, M.S., P.M. Saraiva,Analysis and Classification of the Paper Surface. Industrial & Engineering Chemistry Research. 49:5 (2010), p. 2493–2502.

• Pereira, A.C., M.S. Reis, P.M. Saraiva, J.C. Marques, Analysis and assessment of Madeira wine ageing over an extended time period through GC–MS and chemometric analysis.Analytica Chimica Acta. 659 (2010), p. 93-101.

• Pereira, A.C., M.S. Reis, P.M. Saraiva, J.C. Marques, Aroma ageing trends in GC/MS profiles of liqueur wines.Analytica Chimica Acta. 660 (2010), p. 8-21.

• Reis, M.S., P. Delgado,“Mega”-Variate Statistical Process Control in Electronic Devices Assembling. In Computer-Aided Chemical Engineering, vol. 28. Ed. by S. Pierucci and G. Buzzi Ferraris. Amsterdam: Elsevier (2010). ISBN: 978-0-444-53569-6, p 523-528.

• Pereira, A.C., M.S. Reis, P.M. Saraiva, J.C. Marques, Multivariate Statistical Monitoring of Wine Ageing Processes. In Computer-Aided Chemical Engineering, vol. 28. Ed. by S. Pierucci and G. Buzzi Ferraris. Amsterdam: Elsevier (2010). ISBN: 978-0-444-53569-6, p 247-252.

/ 2009

• Reis, M.S., A. Bauer ⎯ Wavelet texture analysis of on-line acquired images for paper formation assessment and monitoring. Chemometrics and Intelligent Laboratory Systems. 95:2 (2009), p. 129-137.

• Reis, M.S., C.T. Abreu, M.J. Heitor, J. Ataíde, P.M. Saraiva, A new procedure for the routine assessment of paper diagonal curl. Tappi Journal. 8:10 (2009), p. 20-26.

• Reis, M.S., A multiscale empirical modeling framework for system identification. Journal of Process Control. 19:9 (2009), p. 1546-1557.

• Reis, M.S., A. Bauer, Using Wavelet Texture Analysis in Image-Based Classification and Statistical Process Control of Paper Surface Quality. In Computer-Aided Chemical Engineering, vol. 27. Ed. by Rita Maria de Brito Alves, Claudio Augusto Oller do Nascimento, Evaristo Chalbaud Biscaia, Jr.: Elsevier (2009). ISBN-13: 978-0-444-53472-9. p. 1209-1214.

• Paula A.G. Portugal, M.S. Reis, Cristina M.S.G. Baptista, Extending model prediction ability for the formation of nitrophenols in benzene nitration. Chemical Engineering Transactions. 17 (2009), p. 117-122.

• Pereira, A.C., M.S. Reis, and P.M. Saraiva ⎯ Quality control of food products using image analysis and multivariate statistical tools. Industrial & Engineering Chemistry Research. 48:2 (2009), p. 988-998.

• Saraiva, P.M., M.S. Reis, Ouvir e Interpretar Dados no Século XXI. Qualidade. Ano XXXVIII, nº 3, Outono (2009), p.28-38 (in Portuguese).

/ 2008

• Reis, M.S., C.T. Abreu, M. J. Heitor, P.M. Saraiva ⎯ Uma Nova Metodologia para Medição do “Curl” Diagonal do Papel. Pasta e Papel. Verão (2008), p.22-28 (in Portuguese).

• Reis, M.S., B.R. Bakshi, P.M. Saraiva ⎯ Multiscale Statistical Process Control Using Wavelet Packets. AIChE Journal. 54:9 (2008), p. 2366-2378.

/ 2006

• Reis, M.S., P.M. Saraiva, Generalized Multiresolution Decomposition Frameworks for the Analysis of Industrial Data with Uncertainty and Missing Values. Industrial & Engineering Chemistry Research. 45 (2006), p. 6330-6338.

• Reis, M.S., P.M. Saraiva, Multiscale Statistical Process Control with Multiresolution Data. AIChE Journal. 52:6 (2006), p. 2107-2119.

• Reis, M.S., P.M. Saraiva, Heteroscedastic Latent Variable Modelling with Applications to Multivariate Statistical Process Control. Chemometrics and Intelligent Laboratory Systems. 80 (2006), p. 57-66.

• Reis, M.S., P.M. Saraiva, Multiscale Statistical Process Control of Paper Surface. Quality Technology and Quantitative Management. 3:3 (2006), p. 263-282.

• Reis, M.S., P.M. Saraiva, Multiscale Analysis and Monitoring of Paper Surface. In Computer-Aided Chemical Engineering, vol. 21B. Ed. by Marquardt, W., C. Pantelides. Amsterdam: Elsevier (2006). ISBN 0-444-52257-3. p. 1173-1178.

• Reis, M.S., P.M. Saraiva, Multiscale SPC in the Presence of Multiresolution Data. In Computer-Aided Chemical Engineering, vol. 21B. Ed. by Marquardt, W., C. Pantelides. Amsterdam: Elsevier (2006). ISBN 0-444-52257-3. p. 1359-1364.

/ 2005

• Quadros, P.A., M.S. Reis, C. M. S. G. Baptista, Different Modelling Approaches for a Heterogeneous Liquid-Liquid Reaction Process. Industrial & Engineering Chemistry Research. 44 (2005), p. 9414-9421.

• Reis, M.S., P.M. Saraiva, Integration of Data Uncertainty in Linear Regression and Process Optimization. AIChE Journal. 51:11 (2005), p. 3007-3019.

• Costa, R., D. Angélico, M.S. Reis, J. Ataíde, P.M. Saraiva, Paper Superficial Waviness: Conception and Implementation of an Industrial Statistical Measurement System. Analytica Chimica Acta. 544 (2005), p. 135-142.

• Reis, M.S., P.M. Saraiva, Integrating Data Uncertainty in Multiresolution Analysis.In Computer-Aided Chemical Engineering, vol. 20B. Ed. by Puigjaner, L., A. Espuña. Amsterdam: Elsevier (2005). ISBN 0-444-51991-2. p. 1501-1506.

/ 2004

• Reis, M.S., P.M. Saraiva, A Comparative Study of Linear Regression Methods in Noisy Environments. Journal of Chemometrics. 18:12 (2004), p. 526-536.

• Reis, M.S., P.M. Saraiva, Accounting for Measurement Uncertainties in Industrial Data Analysis. In Computer-Aided Chemical Engineering, vol. 18. Ed. by Barbosa-Póvoa, A., H. Matos. Amsterdam: Elsevier (2004). ISBN 0-444-51694-8. p. 751-756.

• Dourado, C., A. Madrigal, M.S. Reis, Prediction of traqueal tube size in children using multiple variables. European Journal of Anaesthesiology. 21 (2004), p. 146-147.

/ 2003

• Reis, M.S., P.M. Saraiva, Multiscale Latent Variable Analysis of Industrial Data. In Computer-Aided Chemical Engineering, vol. 15B. Ed. by B. Chen, A.W. Westerberg. Amsterdam: Elsevier (2003). ISBN 0-444-51404-X. p. 1340-1345.