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dc.contributor.authorArthit Jityenen_US
dc.contributor.authorRawat Jaisuthien_US
dc.contributor.authorTanakorn Osotchanen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherThammasat Universityen_US
dc.identifier.citationChiang Mai Journal of Science. Vol.47, No.4 Special Issue 2 (2020), 776-785en_US
dc.description.abstract© 2020, Chiang Mai University. All rights reserved. Various metal oxide (MO) powders have been explored for electrochemical sensor as liquid or gas phase sensor. These materials can show ideal or near-ideal Nernstian responses due to their high conductivity, good chemical stability, and most importantly their superior biocompatibility. In this work, various MOs were used as non-specific sensor array for electronic tongue system. To fabricate the sensor, carbon nanotube (CNT) was mixed with several MO powders consisting of zinc oxide, cobalt oxide, copper oxide, iron oxide and copper iron oxide powders with the ratio of 1:1 by weight. These sensor arrays were used as array of working electrode for cyclic voltammetry (CV) measurement in electronic tongue system. To investigate the classification ability, the five different species of orange juice including Shogun, Tangerine, Valencia, Sainumpeung and Mandarin were used to perform the measurement. The 0.1M of NaOH, as electrolyzed solution, was added to enhance electrical conductivity with the ratio of 1:1 by volume. The CV signals were performed the preprocessing and extracted main feature by using wavelet analysis. In order to distinguish different types of orange juice, the extracted signals were classified by using principal component analysis. The result showed that these CNT electrodes modifying with MOs can be utilized to the electronic tongue application for orange juice classification with high stability.en_US
dc.rightsMahidol Universityen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectMaterials Scienceen_US
dc.titleElectronic tongue system based on non-specific metal oxides/carbon nanotubes electronical sensors for orange juice classificationen_US
Appears in Collections:Scopus 2020

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