Stability control for breath analysis using GC-MS

Gas chromatography mass spectrometry (GC-MS) instruments provide researchers and clinicians with a vast amount of information on sample composition, thus these instruments are seen as gold standard in breath analysis research. However, there are many factors that can confound the data measured by GC-MS instruments. These factors will make interpretation of GC-MS data unreliable for breath analysis research. We present in this paper detailed studies of two of these factors: instrument variation over time and chemical degradation of known biomarkers during storage in sorbent tubes. We found that a single quadrupole MS showed larger variability in measurements than a quadrupole time-of-flight MS when the same mixture of chemical standards was analysed for a period of up to 8 weeks. We recommend procedures of normalising the data. Moreover, the stability studies of breath biomarkers like thioethers, previously found indicative of malaria, showed that there is a need to store the samples in sorbent tubes at low temperature, 6 °C, for no more than 20 days to avoid the total decay of the chemicals

Classifying chemical sensor data using GPU-accelerated bio-mimetic neuronal networks based on the insect olfactory system

No description supplie

Drosophila olfactory receptors as classifiers for volatiles from disparate real world applications

Olfactory receptors evolved to provide animals with ecologically and behaviourally relevant information. The resulting extreme sensitivity and discrimination has proven useful to humans, who have therefore co-opted some animals' sense of smell. One aim of machine olfaction research is to replace the use of animal noses and one avenue of such research aims to incorporate olfactory receptors into artificial noses. Here, we investigate how well the olfactory receptors of the fruit fly, Drosophila melanogaster, perform in classifying volatile odourants that they would not normally encounter. We collected a large number of in vivo recordings from individual Drosophila olfactory receptor neurons in response to an ecologically relevant set of 36 chemicals related to wine ('wine set') and an ecologically irrelevant set of 35 chemicals related to chemical hazards ('industrial set'), each chemical at a single concentration. Resampled response sets were used to classify the chemicals against all others within each set, using a standard linear support vector machine classifier and a wrapper approach. Drosophila receptors appear highly capable of distinguishing chemicals that they have not evolved to process. In contrast to previous work with metal oxide sensors, Drosophila receptors achieved the best recognition accuracy if the outputs of all 20 receptor types were used

Diurnal variation in expired breath volatiles in malaria-infected and healthy volunteers

We previously showed that thioether levels in the exhaled breath volatiles of volunteers undergoing controlled human malaria infection (CHMI) with P. falciparum increase as infection progresses. In this study, we show that thioethers have diurnal cyclical increasing patterns and their levels are significantly higher in P. falciparum CHMI volunteers compared to those of healthy volunteers. The synchronized cycle and elevation of thioethers were not present in P. vivax-infection, therefore it is likely that the thioethers are associated with unique factors in the pathology of P. falciparum. Moreover, we found that time-of-day of breath collection is important to accurately predict (98%) P. falciparum-infection. Critically, this was achieved when the disease was asymptomatic and parasitemia was below the level detectable by microscopy. Although these findings are encouraging, they show limitations because of the limited and logistically difficult diagnostic window and its utility to P. falciparum malaria only. We looked for new biomarkers in the breath of P. vivaxCHMI volunteers and found that a set of terpenes increase significantly over the course of the malaria infection. The accuracy of predicting P. vivax using breath terpenes was up to 91%. Moreover, some of the terpenes were also found in the breath of P. falciparum CHMI volunteers (accuracy up to 93.5%). The results suggest that terpenes might represent better biomarkers than thioethers to predict malaria as they were not subject to malaria pathogens diurnal changes

Optimal feature selection for classifying a large set of chemicals using metal oxide sensors

Using linear support vector machines, we investigated the feature selection problem for the application of all-against-all classification of a set of 20 chemicals using two types of sensors, classical doped tin oxide and zeolite-coated chromium titanium oxide sensors. We defined a simple set of possible features, namely the identity of the sensors and the sampling times and tested all possible combinations of such features in a wrapper approach. We confirmed that performance is improved, relative to previous results using this data set, by exhaustive comparison of these feature sets. Using the maximal number of different sensors and all available data points for each sensor does not necessarily yield the best results, even for the large number of classes in this problem. We contrast this analysis, using exhaustive screening of simple feature sets, with a number of more complex feature choices and find that subsampled sets of simple features can perform better. Analysis of potential predictors of classification performance revealed some relevance of clustering properties of the data and of correlations among sensor responses but failed to identify a single measure to predict classification success, reinforcing the relevance of the wrapper approach used. Comparison of the two sensor technologies showed that, in isolation, the doped tin oxide sensors performed better than the zeolite-coated chromium titanium oxide sensors but that mixed arrays, combining both technologies, performed best

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Bio-Benchmarking of Electronic Nose Sensors

BACKGROUND:Electronic noses, E-Noses, are instruments designed to reproduce the performance of animal noses or antennae but generally they cannot match the discriminating power of the biological original and have, therefore, been of limited utility. The manner in which odorant space is sampled is a critical factor in the performance of all noses but so far it has been described in detail only for the fly antenna. METHODOLOGY:Here we describe how a set of metal oxide (MOx) E-Nose sensors, which is the most commonly used type, samples odorant space and compare it with what is known about fly odorant receptors (ORs). PRINCIPAL FINDINGS:Compared with a fly's odorant receptors, MOx sensors from an electronic nose are on average more narrowly tuned but much more highly correlated with each other. A set of insect ORs can therefore sample broader regions of odorant space independently and redundantly than an equivalent number of MOx sensors. The comparison also highlights some important questions about the molecular nature of fly ORs. CONCLUSIONS:The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors. It also potentially deepens our understanding of the performance of the biological system

ACO2 homozygous missense mutation associated with complicated hereditary spastic paraplegia

Objective: To identify the clinical characteristics and genetic etiology of a family affected with hereditar

Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose

Analysis of 34 Sauvignon Blanc wine samples from three different countries and six regions was performed by gas chromatography-mass spectrometry (GC-MS). Linear discriminant analysis (LDA) showed that there were three distinct clusters or classes of wines with different aroma profiles. Wines from the Loire region in France and Australian wines from Tasmania and Western Australia were found to have similar aroma patterns. New Zealand wines from the Marlborough region as well as the Australian ones from Victoria were grouped together based on the volatile composition. Wines from South Australia region formed one discrete class. Seven analytes, most of them esters, were found to be the relevant chemical compounds that characterized the classes. The grouping information obtained by GC-MS, was used to train metal oxide based electronic (MOS-Enose) and mass spectrometry based electronic (MS-Enose) noses. The combined use of solid phase microextraction (SPME) and ethanol removal prior to MOS-Enose analysis, allowed an average error of prediction of the regional origins of Sauvignon Blanc wines of 6.5% compared to 24% when static headspace (SHS) was employed. For MS-Enose, the misclassification rate was higher probably due to the requirement to delimit the m/z range considered. Crow