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Sensors 2012, 12(10), 14022-14040; doi:10.3390/s121014022

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

1
School of Electrical & Electronic Engineering, Universiti Sains Malaysia (USM), Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia
2
Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), 0100 Kangar, Perlis, Malaysia
*
Author to whom correspondence should be addressed.
Received: 17 August 2012 / Revised: 19 September 2012 / Accepted: 29 September 2012 / Published: 17 October 2012
(This article belongs to the Section Chemical Sensors)
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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. View Full-Text
Keywords: electronic nose; FTIR; honey classification; data fusion; pure honey electronic nose; FTIR; honey classification; data fusion; pure honey
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Subari, N.; Mohamad Saleh, J.; Md Shakaff, A.Y.; Zakaria, A. A Hybrid Sensing Approach for Pure and Adulterated Honey Classification. Sensors 2012, 12, 14022-14040.

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