In recent years, due to advances in sensor technology, multi-modal measurement of process and products properties has become easier. However, multi-modal measurements are only of use if the data from adding new sensors is worthwhile, especially in the case of industrial applications where financial justification is needed for new sensor purchase and integration, and if the multi-modal data generated can be properly utilised. Several multi-block methods have been developed to do this; however, their use is largely limited to chemometricians, and non-experts have little experience with such methods. To deal with this, we present the first version of a MATLAB-based graphical user interface (GUI) for multi-block data analysis (MBA), capable of performing data visualisation, regression, classification and variable selection for up to 4 different sensors. The MBA-GUI can also be used to implement a recent technique called sequential pre-processing through orthogonalization (SPORT). Data sets are supplied to demonstrate how to use the MBA-GUI. In summary, the developed GUI makes the implementation of multi-block data analysis easier, so that it could be used also by practitioners with no programming skills or unfamiliar with the MATLAB environment. The fully functional GUI can be downloaded from (https://github.com/puneetmishra2/Multi-block.git) and can be either installed to run in the MATLAB environment or as a standalone executable program. The GUI can also be used for analysis of a single block of data (standard chemometrics).

MBA-GUI: A chemometric graphical user interface for multi-block data visualisation, regression, classification, variable selection and automated pre-processing / Mishra, P.; Roger, J. M.; Rutledge, D. N.; Biancolillo, A.; Marini, F.; Nordon, A.; Jouan-Rimbaud-Bouveresse, D.. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 205:(2020), pp. 1-12. [10.1016/j.chemolab.2020.104139]

MBA-GUI: A chemometric graphical user interface for multi-block data visualisation, regression, classification, variable selection and automated pre-processing

Marini F.;
2020

Abstract

In recent years, due to advances in sensor technology, multi-modal measurement of process and products properties has become easier. However, multi-modal measurements are only of use if the data from adding new sensors is worthwhile, especially in the case of industrial applications where financial justification is needed for new sensor purchase and integration, and if the multi-modal data generated can be properly utilised. Several multi-block methods have been developed to do this; however, their use is largely limited to chemometricians, and non-experts have little experience with such methods. To deal with this, we present the first version of a MATLAB-based graphical user interface (GUI) for multi-block data analysis (MBA), capable of performing data visualisation, regression, classification and variable selection for up to 4 different sensors. The MBA-GUI can also be used to implement a recent technique called sequential pre-processing through orthogonalization (SPORT). Data sets are supplied to demonstrate how to use the MBA-GUI. In summary, the developed GUI makes the implementation of multi-block data analysis easier, so that it could be used also by practitioners with no programming skills or unfamiliar with the MATLAB environment. The fully functional GUI can be downloaded from (https://github.com/puneetmishra2/Multi-block.git) and can be either installed to run in the MATLAB environment or as a standalone executable program. The GUI can also be used for analysis of a single block of data (standard chemometrics).
2020
chemometrics; data fusion; graphical user interface; multi-sensor
01 Pubblicazione su rivista::01a Articolo in rivista
MBA-GUI: A chemometric graphical user interface for multi-block data visualisation, regression, classification, variable selection and automated pre-processing / Mishra, P.; Roger, J. M.; Rutledge, D. N.; Biancolillo, A.; Marini, F.; Nordon, A.; Jouan-Rimbaud-Bouveresse, D.. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 205:(2020), pp. 1-12. [10.1016/j.chemolab.2020.104139]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1499973
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