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dc.contributor.authorAzian Azamimi, Abdullah
dc.contributor.authorNurlisa, Yusuf @ Idris
dc.contributor.authorAmmar, Zakaria, Dr.
dc.contributor.authorMohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.
dc.contributor.authorAli Yeon, Md Shakaff, Prof. Dr.
dc.contributor.authorAbd Hamid, Adom, Prof. Dr.
dc.contributor.authorLatifah Munirah, Kamarudin, Dr.
dc.contributor.authorYeap, Ewe Juan, Dr.
dc.contributor.authorAmizah, Othman, Dr.
dc.contributor.authorMohd Sadek, Yasin
dc.date.accessioned2014-03-06T07:29:08Z
dc.date.available2014-03-06T07:29:08Z
dc.date.issued2013
dc.identifier.citationApplied Mechanics and Materials, vol. 339, 2013, pages 167-172en_US
dc.identifier.isbn978-303785737-3
dc.identifier.issn1660-9336
dc.identifier.urihttp://www.scientific.net/AMM.339.167
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/32392
dc.descriptionLink to publisher's homepage at http://www.ttp.net/en_US
dc.description.abstractArray based gas sensor technology namely Electronic Nose (E-nose) now offers the potential of a rapid and robust analytical approach to odor measurement for medical use. Wounds become infected when a microorganism which is bacteria from the environment or patient's body enters the open wound and multiply. The conventional method consumes more time to detect the bacteria growth. However, by using this E-Nose, the bacteria can be detected and classified according to their volatile organic compound (VOC) in shorter time. Readings were taken from headspace of samples by manually introducing the portable e-nose system into a special container that containing a volume of bacteria in suspension. The data will be processed by using statistical analysis which is Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods. The most common bacteria in diabetic foot are Staphylococcus aureus, Escherchia coli, Pseudomonas aeruginosa, and many more.en_US
dc.language.isoenen_US
dc.publisherTrans Tech Publicationsen_US
dc.subjectBacteria infectionen_US
dc.subjectDiabetic footen_US
dc.subjectElectronic noseen_US
dc.subjectLinear discriminant analysis (LDA)en_US
dc.subjectPrinciple component analysis (PCA)en_US
dc.titleBacteria classification using electronic nose for diabetic wound monitoringen_US
dc.typeArticleen_US
dc.contributor.urlazamimi@unimap.edu.myen_US
dc.contributor.urlammarzakaria@unimap.edu.myen_US
dc.contributor.urliqbalomar@unimap.edu.myen_US
dc.contributor.urlaliyeon@unimap.edu.myen_US
dc.contributor.urlabdhamid@unimap.edu.myen_US


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