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Title: An Electronic Nose system for aromatic rice classification
Authors: Abu Hassan, Abdullah
Abdul Hamid, Adom, Prof. Madya Dr.
Ali Yeon, Md. Shakaff, Prof. Dr.
Mansur N, Ahmad
Ammar, Zakaria
Nazifah, Ahmad Fikri
Othman, Omar
???metadata.dc.contributor.url???: abdhamid@unimap.edu.my
Keywords: Artificial Neural Network (ANN);Aromatic rice classification;Electronic nose;Embedded system;Hierarchical Cluster Analysis (HCA);Principal Component Analysis (PCA)
Issue Date: Apr-2011
Publisher: American Scientific Publishers
Citation: Sensor Letters, vol. 9 (2), 2011, pages 850-855
Abstract: Aromatic rice is a variety of rice with good cooking qualities such as nice aroma and flavour. It is pricier because it is only suitable to be cultivated in regions with specific climatic and soil conditions. Presently, the aromatic rice quality classification uses either Isotope Ratio Mass Spectrometry (IRMS), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Near Infrared (NIR) or Deoxyribonucleic Acid (DNA). The rice aroma can also be classified using Gas Chromatography Mass Spectrometry (GC-MS), human panels or Electronic Nose (e-nose). The training for the human pan-els is lengthy, but the results are comparable to those using the said instrument analysis. However, the use of human panels has significant drawbacks such as fatigue, inconsistent and time consuming. This paper presents the development of a new cost-effective, portable, e-nose prototype with embedded data processing capabilities for aromatic rice classification. This system is intended to be used to assist the human panels. The e-nose utilises Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) for data analysis. An Artificial Neural Network (ANN) was used to classify the unknown samples. The results show that the e-nose is able to successfully classify the aromatic rice with high accuracy.
Description: Link to publisher's homepage at http://www.aspbs.com/
URI: http://www.ingentaconnect.com/content/asp/senlet/2011/00000009/00000002/art00082?token=00531897fb646b0e6720297d76347070237b60246c6a432c6b6d3f6a4b4b6e6e42576b6427388e7fc67
ISSN: 1546-198X
Appears in Collections:Abu Hassan Abdullah, Dr.
Ammar Zakaria, Dr.
School of Mechatronic Engineering (Articles)
Ali Yeon, Md Shakaff, Prof. Dr.
Abdul Hamid Adom, Prof. Dr.

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