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dc.contributor.authorNorazian, Subari
dc.contributor.authorJunita, Mohamad Saleh, Dr.
dc.contributor.authorAli Yeon, Md Shakaff, Prof. Dr.
dc.contributor.authorAmmar, Zakaria
dc.date.accessioned2014-02-10T08:13:45Z
dc.date.available2014-02-10T08:13:45Z
dc.date.issued2012-10
dc.identifier.citationSensors (Switzerland), vol. 12(10), 2012, pages 14022-14040en_US
dc.identifier.issn1424-8220
dc.identifier.urihttp://www.mdpi.com/1424-8220/12/10/14022
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/31840
dc.descriptionLink to publisher’s homepage at http://www.mdpi.comen_US
dc.description.abstractThis paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.en_US
dc.language.isoenen_US
dc.publisherMDPI AG, Basel, Switzerlanden_US
dc.subjectData fusionen_US
dc.subjectElectronic noseen_US
dc.subjectFTIRen_US
dc.subjectHoney classificationen_US
dc.subjectPure honeyen_US
dc.titleA hybrid sensing approach for pure and adulterated honey classificationen_US
dc.typeArticleen_US
dc.contributor.urlaziansubari@ump.edu.myen_US
dc.contributor.urljms@eng.usm.myen_US
dc.contributor.urlaliyeon@unimap.edu.myen_US
dc.contributor.urlammarzakaria@unimap.edu.myen_US


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