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Compression of electronic tongue data based on voltammetry - A comparative study
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Applied Physics .
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology.
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Applied Physics .
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Applied Physics .
2001 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 76, no 1-3, 455-464 p.Conference paper, Published paper (Other academic)
Abstract [en]

In this paper, three data compression methods are investigated to determine their ability to reduce large data sets obtained by a voltammetric electronic tongue without loss of information, since compressed data sets will save data storage and computational time. The electronic tongue is based on a combination of non-specific sensors and pattern recognition tools, such as principal component analysis (PCA). A series of potential pulses of decreasing amplitude are applied to one working electrode at a time and resulting current transients are collected at each potential step. Voltammograms containing up to 8000 variables are subsequently obtained. The methods investigated are wavelet transformation (WT) and hierarchical principal component analysis (HPCA). Also, a new chemical/physical model based on voltammetric theory is developed in order to extract interesting features of the current transients, revealing different information about species in solutions. Two model experiments are performed, one containing solutions of different electroactive compounds and the other containing complex samples, such as juices from fruits and tomatoes. It is shown that WT and HPCA compress the data sets without loss of information, and the chemical/physical model improves the separations slightly. HPCA is able to compress the two data sets to the largest extent, from 8000 to 16 variables. When data sets are scaled to unit variance, the separation ability improves even further for HPCA and the chemical/physical model. © 2001 Elsevier Science B.V.

Place, publisher, year, edition, pages
2001. Vol. 76, no 1-3, 455-464 p.
Keyword [en]
Chemical/physical model, Data compression, Electroactive compounds, Electronic tongue, Fruits, Hierarchical principal component analysis, Tomatoes and wavelet transformation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-47368DOI: 10.1016/S0925-4005(01)00585-8OAI: oai:DiVA.org:liu-47368DiVA: diva2:268264
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13

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Holmin, SusanneSpångéus, PerKrantz-Rülcher, ChristinaWinquist, Fredrik

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Holmin, SusanneSpångéus, PerKrantz-Rülcher, ChristinaWinquist, Fredrik
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The Institute of TechnologyApplied Physics Department of Physics, Chemistry and Biology
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