A preliminary study on in-vitro lung cancer detection using e-nose technology

A preliminary study on in-vitro lung cancer detection using e-nose technology

The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify dif...

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Main Authors: Thriumani , Reena, Zakaria, Ammar, Jeffree, Amanina Iymia, Hishamuddin, NA, Omar, Mohammad Iqbl, Adom, Abdul Hamid, M. Shakaff, Ali Yeon, Kamarudin, Latifah Munirah, Yusuf, Nurlisa, Hashim, Yumi Zuhanis Has-Yun, Mohamed Helmy, Khaled
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
Q Science (General)
Online Access:http://irep.iium.edu.my/47326/
http://irep.iium.edu.my/47326/
http://irep.iium.edu.my/47326/4/47326-cover.pdf
id iium-47326
recordtype eprints
spelling iium-473262018-05-24T04:56:48Z http://irep.iium.edu.my/47326/ A preliminary study on in-vitro lung cancer detection using e-nose technology Thriumani , Reena Zakaria, Ammar Jeffree, Amanina Iymia Hishamuddin, NA Omar, Mohammad Iqbl Adom, Abdul Hamid M. Shakaff, Ali Yeon Kamarudin, Latifah Munirah Yusuf, Nurlisa Hashim, Yumi Zuhanis Has-Yun Mohamed Helmy, Khaled Q Science (General) The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify different volatile components leading to the diagnosis of lung cancer at an early stage. In this preliminary study, a commercialized E-nose consists of an array of 32 conducting polymer sensors (Cyranose 320) was used to detect and discriminate the VOCs emitted from cancer cells which is A549 (lung cancer cell line) between MCF7 (breast cancer cell line). Blank medium was used to obtain controlled value. The VOC profiles of each sample were characterized using a classification algorithm called k-Nearest Neighbors (KNN) to test and benchmark the performance of Enose in identifying VOCs of lung cancer from different cancer cell lines. The E-nose with KNN classifier was able to classify the VOCs of lung cancer cell with over 90% successful accuracy in 30 seconds. This study can conclude that e-nose is capable to rapidly discriminate volatile organic compounds of cancerous cells which generated during cell growth. 2014-11-28 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/47326/4/47326-cover.pdf Thriumani , Reena and Zakaria, Ammar and Jeffree, Amanina Iymia and Hishamuddin, NA and Omar, Mohammad Iqbl and Adom, Abdul Hamid and M. Shakaff, Ali Yeon and Kamarudin, Latifah Munirah and Yusuf, Nurlisa and Hashim, Yumi Zuhanis Has-Yun and Mohamed Helmy, Khaled (2014) A preliminary study on in-vitro lung cancer detection using e-nose technology. In: 4th IEEE International Conference on Control Systems, Computing and Engineering (ICCSE 2014), 28th-30th November 2014, Batu Ferringhi, Penang Malaysia. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7072789
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic Q Science (General)
spellingShingle Q Science (General)
Thriumani , Reena
Zakaria, Ammar
Jeffree, Amanina Iymia
Hishamuddin, NA
Omar, Mohammad Iqbl
Adom, Abdul Hamid
M. Shakaff, Ali Yeon
Kamarudin, Latifah Munirah
Yusuf, Nurlisa
Hashim, Yumi Zuhanis Has-Yun
Mohamed Helmy, Khaled
A preliminary study on in-vitro lung cancer detection using e-nose technology
description The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify different volatile components leading to the diagnosis of lung cancer at an early stage. In this preliminary study, a commercialized E-nose consists of an array of 32 conducting polymer sensors (Cyranose 320) was used to detect and discriminate the VOCs emitted from cancer cells which is A549 (lung cancer cell line) between MCF7 (breast cancer cell line). Blank medium was used to obtain controlled value. The VOC profiles of each sample were characterized using a classification algorithm called k-Nearest Neighbors (KNN) to test and benchmark the performance of Enose in identifying VOCs of lung cancer from different cancer cell lines. The E-nose with KNN classifier was able to classify the VOCs of lung cancer cell with over 90% successful accuracy in 30 seconds. This study can conclude that e-nose is capable to rapidly discriminate volatile organic compounds of cancerous cells which generated during cell growth.
format Conference or Workshop Item
author Thriumani , Reena
Zakaria, Ammar
Jeffree, Amanina Iymia
Hishamuddin, NA
Omar, Mohammad Iqbl
Adom, Abdul Hamid
M. Shakaff, Ali Yeon
Kamarudin, Latifah Munirah
Yusuf, Nurlisa
Hashim, Yumi Zuhanis Has-Yun
Mohamed Helmy, Khaled
author_facet Thriumani , Reena
Zakaria, Ammar
Jeffree, Amanina Iymia
Hishamuddin, NA
Omar, Mohammad Iqbl
Adom, Abdul Hamid
M. Shakaff, Ali Yeon
Kamarudin, Latifah Munirah
Yusuf, Nurlisa
Hashim, Yumi Zuhanis Has-Yun
Mohamed Helmy, Khaled
author_sort Thriumani , Reena
title A preliminary study on in-vitro lung cancer detection using e-nose technology
title_short A preliminary study on in-vitro lung cancer detection using e-nose technology
title_full A preliminary study on in-vitro lung cancer detection using e-nose technology
title_fullStr A preliminary study on in-vitro lung cancer detection using e-nose technology
title_full_unstemmed A preliminary study on in-vitro lung cancer detection using e-nose technology
title_sort preliminary study on in-vitro lung cancer detection using e-nose technology
publishDate 2014
url http://irep.iium.edu.my/47326/
http://irep.iium.edu.my/47326/
http://irep.iium.edu.my/47326/4/47326-cover.pdf
first_indexed 2023-09-18T21:07:21Z
last_indexed 2023-09-18T21:07:21Z
_version_ 1777411022171144192

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