Sunil K. Jha

A quick responding quartz crystal microbalance sensor array based on molecular imprinted polyacrylic acids coating for selective identification of aldehydes in body odor

In present work, a novel quartz crystal microbalance (QCM) sensor array has been developed for prompt identification of primary aldehydes in human body odor. Molecularly imprinted polymers (MIP) are prepared using the polyacrylic acid (PAA) polymer matrix and three organic acids (propenoic acid, hexanoic acid and octanoic acid) as template molecules, and utilized as QCM surface coating layer. The performance of MIP films is characterized by 4-element QCM sensor array (three coated with MIP layers and one with pure PAA for reference) dynamic and static responses to target aldehydes: hexanal, heptanal, and nonanal in single, binary, and tertiary mixtures at distinct concentrations. The target aldehydes were selected subsequent to characterization of body odor samples with solid phase-micro extraction gas chromatography mass spectrometer (SPME-GC-MS). The hexanoic acid and octanoic acid imprinted PAA exhibit fast response, and better sensitivity, selectivity and reproducibility than the propenoic acid, and non-imprinted PAA in array. The response time and recovery time for hexanoic acid imprinted PAA are obtained as 5 s and 12 s respectively to typical concentrations of binary and tertiary mixtures of aldehydes using the static response. Dynamic sensor array response matrix has been processed with principal component analysis (PCA) for visual, and support vector machine (SVM) classifier for quantitative identification of target odors. Aldehyde odors were identified successfully in principal component (PC) space. SVM classifier results maximum recognition rate 79% for three classes of binary odors and 83% including single, binary, and tertiary odor classes in 3-fold cross validation.

Data fusion approach for human body odor discrimination using GC-MS spectra

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 individuals body odor. The opted peaks result satisfactory differentiation of individuals 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.

Olfaction-Inspired Sensing Using a Sensor System with Molecular Recognition and Optimal Classification Ability for Comprehensive Detection of Gases

In this study, we examined the comprehensive detection of numerous volatile molecules based on the olfactory information constructed by using olfaction-inspired sensor technology. The sensor system can simultaneously detect multiple odors by the separation and condensation ability of molecularly imprinted filtering adsorbents (MIFAs), where a MIP filter with a molecular sieve was deposited on a polydimethylsiloxane (PDMS) substrate. The adsorption properties of MIFAs were evaluated using the solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS). The results demonstrated that the system embedded with MIFAs possesses high sensitivity and specific selectivity. The digitization and comprehensive classification of odors were accomplished by using artificial odor maps constructed through this system.

Optimized KPCA method for chemical vapor class recognition by SAW sensor array response analysis

This paper confirms the suitability of kernel principal component analysis (KPCA) as a robust feature extraction and denoising method in sensor array based vapor detection system (E-nose). Particularly the study focuses on response analysis of surface acoustic wave (SAW) sensor array in chemical class recognition of volatile organic compounds (VOCs). Usually KPCA results deprived performance compare to linear feature extraction methods. However its performance is affected by the proper selection of kernel function and optimization of allied parameters. We have presented the comparative performance analysis of feature vectors extracted by KPCA method using five types of kernel functions in combination with support vector machine (SVM) classifier. Study outcomes are based on analysis of 12 data sets (enclosing different intensity of additive noise and outliers) generated with SAW sensor model simulator. We find that in research of kernel function selection; polynomial kernel achieves persistently maximum class recognition rate of VOCs (average 82 %) even in presence of high level of additive Gaussian noise and outlier and anova kernel results minimum class recognition rate (average 70 %). The class recognition efficiency of feature vectors extracted by rest of the three kernel functions lies in between these two.