TY - JOUR
T1 - Molecular imprinted polyacrylic acids based QCM sensor array for recognition of organic acids in body odor
AU - Jha, Sunil K.
AU - Liu, Chuanjun
AU - Hayashi, Kenshi
N1 - Funding Information:
This research work was supported by Grant-in-Aid for JSPS fellows 24.02367 and partly supported by JSPS KAKENHI Grant Number 25420409 . The author S.K.J. gratefully acknowledges lab colleagues and Mrs. Anju Sunil Jha for their contribution and support during the study.
Funding Information:
Sunil K. Jha received his B.Sc. and M.Sc. in Physics from Udai Pratap Autonomous College, Varanasi affiliated to V.B.S. Purvanchal University, Jaunpur, India, in 2003 and 2005 and Ph.D. in Physics from Banaras Hindu University, Varanasi, India in 2012. He is currently postdoctoral research fellow sponsored by the Japan Society for the Promotion of Science (JSPS) for pursuing research at the Organic Electronic Device Lab, Department of Electronics, Kyushu University (Japan). His present research interests include sensor array signal processing, multivariate data analysis, data fusion and human body odor analysis.
PY - 2014/12/1
Y1 - 2014/12/1
N2 - In present research, a novel volatile acids imprinted 3-element quartz crystal microbalance (QCM) sensor array has been designed for the selective recognition of organic acids odor. The target organic acids have been previously assigned as significant biomarker in human body odor and human waste odor. This is also confirmed with solid phase micro-extraction gas chromatography-mass spectrometer (SPME-GC-MS) characterization of body odor samples in present study. Polyacrylic acid (PAA) polymer has been used to prepare three selective molecular imprinted polymers (MIPs). MIP films were coated on the surface of QCM electrodes. Performance of MIP based QCM sensor array has been studied by exposing different concentrations of propenoic acid, hexanoic acid, and octanoic acid in singly and their binary mixtures. The response time and recovery time were 5 s and 14 s, respectively for one of the MIP coated QCM sensor to a typical binary mixture of acids odor. Frequency shift of QCM due to odor molecules adsorption in thin layer of MIP was measured as sensor signal. Sensor signal response matrix has been analyzed with principal component analysis (PCA) and support vector machine (SVM) methods for the odor recognition. The acid odors were identified effectively in principal component (PC) space. SVM classifier results 70-76% recognition rate for three classes of binary odor. While excluding the binary mixture of propenoic acid and octanoic acid the average recognition rate approaches to 95% in 3-fold cross validation of SVM classifier.
AB - In present research, a novel volatile acids imprinted 3-element quartz crystal microbalance (QCM) sensor array has been designed for the selective recognition of organic acids odor. The target organic acids have been previously assigned as significant biomarker in human body odor and human waste odor. This is also confirmed with solid phase micro-extraction gas chromatography-mass spectrometer (SPME-GC-MS) characterization of body odor samples in present study. Polyacrylic acid (PAA) polymer has been used to prepare three selective molecular imprinted polymers (MIPs). MIP films were coated on the surface of QCM electrodes. Performance of MIP based QCM sensor array has been studied by exposing different concentrations of propenoic acid, hexanoic acid, and octanoic acid in singly and their binary mixtures. The response time and recovery time were 5 s and 14 s, respectively for one of the MIP coated QCM sensor to a typical binary mixture of acids odor. Frequency shift of QCM due to odor molecules adsorption in thin layer of MIP was measured as sensor signal. Sensor signal response matrix has been analyzed with principal component analysis (PCA) and support vector machine (SVM) methods for the odor recognition. The acid odors were identified effectively in principal component (PC) space. SVM classifier results 70-76% recognition rate for three classes of binary odor. While excluding the binary mixture of propenoic acid and octanoic acid the average recognition rate approaches to 95% in 3-fold cross validation of SVM classifier.
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U2 - 10.1016/j.snb.2014.07.098
DO - 10.1016/j.snb.2014.07.098
M3 - Article
AN - SCOPUS:84906311363
VL - 204
SP - 74
EP - 87
JO - Sensors and Actuators B: Chemical
JF - Sensors and Actuators B: Chemical
SN - 0925-4005
ER -