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A hybrid sensing approach for pure and adulterated honey classification

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Main Authors: Norazian, Subari, Junita, Mohamad Saleh, Dr., Ali Yeon, Md Shakaff, Prof. Dr., Ammar, Zakaria
Other Authors: aziansubari@ump.edu.my
Format: Article
Language:English
Published: MDPI AG, Basel, Switzerland 2014
Subjects:
Data fusion
Electronic nose
FTIR
Honey classification
Pure honey
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/31840
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spelling my.unimap-318402014-02-10T08:13:45Z A hybrid sensing approach for pure and adulterated honey classification 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 Data fusion Electronic nose FTIR Honey classification Pure honey Link to publisher’s homepage at http://www.mdpi.com 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. 2014-02-10T08:13:45Z 2014-02-10T08:13:45Z 2012-10 Article Sensors (Switzerland), vol. 12(10), 2012, pages 14022-14040 1424-8220 http://www.mdpi.com/1424-8220/12/10/14022 http://dspace.unimap.edu.my:80/dspace/handle/123456789/31840 en MDPI AG, Basel, Switzerland
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Data fusion
Electronic nose
FTIR
Honey classification
Pure honey
spellingShingle Data fusion
Electronic nose
FTIR
Honey classification
Pure honey
Norazian, Subari
Junita, Mohamad Saleh, Dr.
Ali Yeon, Md Shakaff, Prof. Dr.
Ammar, Zakaria
A hybrid sensing approach for pure and adulterated honey classification
description Link to publisher’s homepage at http://www.mdpi.com
author2 aziansubari@ump.edu.my
author_facet aziansubari@ump.edu.my
Norazian, Subari
Junita, Mohamad Saleh, Dr.
Ali Yeon, Md Shakaff, Prof. Dr.
Ammar, Zakaria
format Article
author Norazian, Subari
Junita, Mohamad Saleh, Dr.
Ali Yeon, Md Shakaff, Prof. Dr.
Ammar, Zakaria
author_sort Norazian, Subari
title A hybrid sensing approach for pure and adulterated honey classification
title_short A hybrid sensing approach for pure and adulterated honey classification
title_full A hybrid sensing approach for pure and adulterated honey classification
title_fullStr A hybrid sensing approach for pure and adulterated honey classification
title_full_unstemmed A hybrid sensing approach for pure and adulterated honey classification
title_sort hybrid sensing approach for pure and adulterated honey classification
publisher MDPI AG, Basel, Switzerland
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/31840
_version_ 1643796699681914880
score 13.214268

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