Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/31840
Title: A hybrid sensing approach for pure and adulterated honey classification
Authors: Norazian, Subari
Junita, Mohamad Saleh, Dr.
Ali Yeon, Md Shakaff, Prof. Dr.
Ammar, Zakaria
aziansubari@ump.edu.my
jms@eng.usm.my
aliyeon@unimap.edu.my
ammarzakaria@unimap.edu.my
Keywords: Data fusion
Electronic nose
FTIR
Honey classification
Pure honey
Issue Date: Oct-2012
Publisher: MDPI AG, Basel, Switzerland
Citation: Sensors (Switzerland), vol. 12(10), 2012, pages 14022-14040
Abstract: This 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.
Description: Link to publisher’s homepage at http://www.mdpi.com
URI: http://www.mdpi.com/1424-8220/12/10/14022
http://dspace.unimap.edu.my:80/dspace/handle/123456789/31840
ISSN: 1424-8220
Appears in Collections:Ammar Zakaria, Associate Professor Dr.
Centre of Excellence for Advanced Sensor Technology (CEASTech) (Articles)
Ali Yeon Md Shakaff, Dato' Prof. Dr.

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