Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds
In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extra...
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| Format: | Conference or Workshop Item |
| Language: | English English English |
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American Institute of Physics
2017
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| Online Access: | http://irep.iium.edu.my/57495/ http://irep.iium.edu.my/57495/ http://irep.iium.edu.my/57495/ http://irep.iium.edu.my/57495/1/57495_Feature%20extraction%20techniques_AIP%20complete.pdf http://irep.iium.edu.my/57495/2/57495_Feature%20extraction%20techniques_SCOPUS.pdf http://irep.iium.edu.my/57495/13/57495%20Feature%20extraction%20techniques%20using%20multivariate%20analysis%20WOS.pdf |
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iium-574952019-08-17T15:44:39Z http://irep.iium.edu.my/57495/ Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds Thriumani,, Reena Zakaria, Ammar Hashim, Yumi Zuhanis Has-Yun Helmy, Khaled Mohamed Omar, Mohammad Iqbal Jeffree, Amanina Iymia Adom, Abdul Hamid Md Shakaff, Ali Yeon Kamarudin, Latifah Munirah QD Chemistry TD Environmental technology. Sanitary engineering In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extracted by taking into consideration of both steady state and the transient information. The extracted data are then being processed by multivariate analysis, Linear Discriminant Analysis (LDA) to provide visualization of the clustering vector information in multi-sensor space. The Probabilistic Neural Network (PNN) classifier was used to test the performance of the E-Nose on determining the volatile organic compounds (VOCs) of lung cancer cell line. The LDA data projection was able to differentiate between the lung cancer cell samples and other samples (breast cancerrmal cell and blank medium) effectively. The features extracted from the steady state response reached 100% of classification rate while the transient response with the aid of LDA dimension reduction methods produced 100% classification performance using PNN classifier with a spread value of 0.1. The results also show that E-Nose application is a promising technique to be applied to real patients in further work and the aid of Multivariate Analysis; it is able to be the alternative to the current lung cancer diagnostic methods. American Institute of Physics 2017-03-13 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/57495/1/57495_Feature%20extraction%20techniques_AIP%20complete.pdf application/pdf en http://irep.iium.edu.my/57495/2/57495_Feature%20extraction%20techniques_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/57495/13/57495%20Feature%20extraction%20techniques%20using%20multivariate%20analysis%20WOS.pdf Thriumani,, Reena and Zakaria, Ammar and Hashim, Yumi Zuhanis Has-Yun and Helmy, Khaled Mohamed and Omar, Mohammad Iqbal and Jeffree, Amanina Iymia and Adom, Abdul Hamid and Md Shakaff, Ali Yeon and Kamarudin, Latifah Munirah (2017) Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds. In: "11th Asian Conference on Chemical Sensors, ACCS 2015", 16- 18 November 2015, Rasa Sayang Resort- Shangri- La PenangPenang; Malaysia. http://aip.scitation.org/doi/abs/10.1063/1.4975287# 10.1063/1.4975287 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English English |
| topic |
QD Chemistry TD Environmental technology. Sanitary engineering |
| spellingShingle |
QD Chemistry TD Environmental technology. Sanitary engineering Thriumani,, Reena Zakaria, Ammar Hashim, Yumi Zuhanis Has-Yun Helmy, Khaled Mohamed Omar, Mohammad Iqbal Jeffree, Amanina Iymia Adom, Abdul Hamid Md Shakaff, Ali Yeon Kamarudin, Latifah Munirah Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds |
| description |
In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extracted by taking into consideration of both steady state and the transient information. The extracted data are then being processed by multivariate analysis, Linear Discriminant Analysis (LDA) to provide visualization of the clustering vector information in multi-sensor space. The Probabilistic Neural Network (PNN) classifier was used to test the performance of the E-Nose on determining the volatile organic compounds (VOCs) of lung cancer cell line. The LDA data projection was able to differentiate between the lung cancer cell samples and other samples (breast cancerrmal cell and blank medium) effectively. The features extracted from the steady state response reached 100% of classification rate while the transient response with the aid of LDA dimension reduction methods produced 100% classification performance using PNN classifier with a spread value of 0.1. The results also show that E-Nose application is a promising technique to be applied to real patients in further work and the aid of Multivariate Analysis; it is able to be the alternative to the current lung cancer diagnostic methods. |
| format |
Conference or Workshop Item |
| author |
Thriumani,, Reena Zakaria, Ammar Hashim, Yumi Zuhanis Has-Yun Helmy, Khaled Mohamed Omar, Mohammad Iqbal Jeffree, Amanina Iymia Adom, Abdul Hamid Md Shakaff, Ali Yeon Kamarudin, Latifah Munirah |
| author_facet |
Thriumani,, Reena Zakaria, Ammar Hashim, Yumi Zuhanis Has-Yun Helmy, Khaled Mohamed Omar, Mohammad Iqbal Jeffree, Amanina Iymia Adom, Abdul Hamid Md Shakaff, Ali Yeon Kamarudin, Latifah Munirah |
| author_sort |
Thriumani,, Reena |
| title |
Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds |
| title_short |
Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds |
| title_full |
Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds |
| title_fullStr |
Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds |
| title_full_unstemmed |
Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds |
| title_sort |
feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds |
| publisher |
American Institute of Physics |
| publishDate |
2017 |
| url |
http://irep.iium.edu.my/57495/ http://irep.iium.edu.my/57495/ http://irep.iium.edu.my/57495/ http://irep.iium.edu.my/57495/1/57495_Feature%20extraction%20techniques_AIP%20complete.pdf http://irep.iium.edu.my/57495/2/57495_Feature%20extraction%20techniques_SCOPUS.pdf http://irep.iium.edu.my/57495/13/57495%20Feature%20extraction%20techniques%20using%20multivariate%20analysis%20WOS.pdf |
| first_indexed |
2023-09-18T21:21:17Z |
| last_indexed |
2023-09-18T21:21:17Z |
| _version_ |
1777411898253246464 |