TY - GEN
T1 - Data fusion approach for human body odor discrimination using GC-MS spectra
AU - Jha, Sunil Kr
AU - Imahashi, Masahiro
AU - Hayashi, Kenshi
AU - Takamizawa, Tadashi
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - This study deals with data fusion approach to search discriminating biomarker volatile organic chemicals (VOCs) in body odor for individual differentiation. Particularly we have employed kernel principal component analysis (KPCA) combined with majority voting method to build up novel data fusion strategy. Gas chromatography-mass spectrometry (GC-MS) characterizes human body odor samples to find out the VOCs composition (alcohols, acids, aldehydes, esters, ketones, carbonyl compounds, sulfides and hydrocarbons etc.). Peak number and related area value of VOCs from the GC-MS spectra of body odor extract is used for analysis. GC-MS data from three experiments, based on body odor samples of four persons (different age groups) in dissimilar conditions are collected. Optimal set of peak numbers are selected with fusion approach. Linear PCA is used in validation of elected peak numbers for discrimination of individual's body odor. The opted peaks result satisfactory differentiation of individual's body odor in feature space. Thereafter biomarker VOCs are affirmed by matching corresponding peak number in GC-MS spectra. Analysis outcomes conclude particular set of biomarker VOCs for each experiment.
AB - This study deals with data fusion approach to search discriminating biomarker volatile organic chemicals (VOCs) in body odor for individual differentiation. Particularly we have employed kernel principal component analysis (KPCA) combined with majority voting method to build up novel data fusion strategy. Gas chromatography-mass spectrometry (GC-MS) characterizes human body odor samples to find out the VOCs composition (alcohols, acids, aldehydes, esters, ketones, carbonyl compounds, sulfides and hydrocarbons etc.). Peak number and related area value of VOCs from the GC-MS spectra of body odor extract is used for analysis. GC-MS data from three experiments, based on body odor samples of four persons (different age groups) in dissimilar conditions are collected. Optimal set of peak numbers are selected with fusion approach. Linear PCA is used in validation of elected peak numbers for discrimination of individual's body odor. The opted peaks result satisfactory differentiation of individual's body odor in feature space. Thereafter biomarker VOCs are affirmed by matching corresponding peak number in GC-MS spectra. Analysis outcomes conclude particular set of biomarker VOCs for each experiment.
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U2 - 10.1109/ISSNIP.2014.6827592
DO - 10.1109/ISSNIP.2014.6827592
M3 - Conference contribution
AN - SCOPUS:84903702608
SN - 9781479928439
T3 - IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
BT - IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
PB - IEEE Computer Society
T2 - 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
Y2 - 21 April 2014 through 24 April 2014
ER -