Reference : Multimodal chemometric approach for the analysis of human exhaled breath in lung canc...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Chemistry
http://hdl.handle.net/2268/233485
Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC ×GC-TOFMS
English
Pesesse, Romain mailto [Université de Liège - ULiège > > > Doct. sc. (chimie - Bologne)]
Stefanuto, Pierre-Hugues mailto [Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique, organique et biologique >]
SCHLEICH, FLorence mailto [Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Service de pneumologie - allergologie >]
LOUIS, Renaud mailto [Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Service de pneumologie - allergologie >]
Focant, Jean-François mailto [Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique, organique et biologique >]
Jan-2019
Journal of Chromatography. B, Biomedical Applications
Elsevier
Yes (verified by ORBi)
International
0378-4347
Netherlands
[en] Breath analysis ; Lung Cancer ; GCxGC
[en] Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is
important to enhance our capability to detect it at earlier stages by developing early diagnostic
methods. In that context, the analysis of exhaled breath is an interesting approach because of
the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive
two-dimensional gas chromatography time of flight mass spectrometry (TD-GC×GCTOFMS)
has been used to characterize and compare the volatile content of human breath of
lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced
by the bags membrane and further environmental migration of VOCs during and after the
sampling have also been investigated. Over a realistic period of 6 h, the concentration of
contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples.
On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were
applied and compared in regards to their ability to reduce the data dimensionality and to
extract the significant information. Both approaches allow to efficiently smooth the
background signal and extract significant features (27 for FR and 17 for RF). Principal
component analysis (PCA) was used to evaluate the clustering capacity of the different
models. For both approaches, a separation along PC-1 was obtained with a variance score
around 35%. The combined model provides a partial separation with a PC-1 score of 52%.
This proof-of-concept study further confirms the potential of breath analysis for cancer
detection but also underlines the importance of quality control over the full analytical
procedure, including the processing of the data.
http://hdl.handle.net/2268/233485
10.1016/j.jchromb.2019.01.029

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