Electronic nose implementation for biomedical applications

The growing rate of diabetes and undiagnosed diabetes related diseases is becoming a worldwide major health concern. The motivation of this thesis was to make use of a technology called the ‘electronic nose’ (eNose) for diagnosing diseases. It presents a comprehensive study on metabolic and gastro-intestinal disorders, choosing diabetes as a target disease. Using eNose technology with urinary volatile organic compounds (VOCs) is attractive as it allows non-invasive monitoring of various molecular constituents in urine. Trace gases in urine are linked to metabolic reactions and diseases. Therefore, urinary volatile compounds were used for diagnosis purposes in this thesis. The literature on existing eNose technologies, their pros and cons and applications in biomedical field was thoroughly reviewed, especially in detecting headspace of urine. Since the thesis investigates urinary VOCs, it is important to discover the stability of urine samples and their VOCs in time. It was discovered that urine samples lose their stability and VOCs emission after 9 months. A comprehensive study with 137 diabetic and healthy control urine samples was done to access the capability of commercially available eNose instruments for discrimination between these two groups. Metal oxide gas sensor based commercial eNose (Fox 4000, AlphaMOS Ltd) and field asymmetric ion mobility spectrometer (Lonestar, Owlstone Ltd) were used to analyse volatiles in urinary headspace. Both technologies were able to distinguish both groups with sensitivity and specificity of more than 90%. Then the project moved onto developing a Non-dispersive infrared (NDIR) sensor system that is non-invasive, low-cost, precise, rapid, simple and patient friendly, and can be used at both hospitals and homes. NDIR gas sensing is one of the most widely used optical gas detection techniques. NDIR system was used for diagnosing diabetes and gastro related diseases from patient’s wastes. To the best of the authors’ knowledge, this is the first and only developed tuneable NDIR eNose system. The developed optical eNose is able to scan the whole infrared range between 3.1μm and 10.5 μm with step size of 20 nm. To simulate the effect of background humidity and temperature on the sensor response, a gas test rig system that includes gas mixture, VOC generator, humidity generator and gas analyser was designed to enable the user to have control of gas flow, humidity and temperature. This also helps to find out system’s sensitivity and selectivity. Finally, after evaluating the sensitivity and selectivity of optical eNose, it was tested on simple and complex odours. The results were promising in discriminating the odours. Due to insufficient sample batches received from the hospital, synthetic urine samples were purchased, and diabetic samples were artificially made. The optical eNose was able to successfully separate artificial diabetic samples from non-diabetic ones

Low cost optical electronic nose for biomedical applications

Here we report on the development of a Non-Dispersive Infrared Sensor (NDIR) optical electronic nose, which we intend to target towards healthcare applications. Our innovative electronic nose uses an array of four different tuneable infra-red detectors to analyse the gas/volatile content of a sample under test. The instrument has the facility to scan a range of wavelengths from 3.1 μm and 10.5 μm with a step size of 20 nm. The use of a tuneable filter, instead of expensive lasers, reduces the overall cost of the system. We have tested our instrument to a range of gases and vapours and our electronic nose is able to detect, for example, methane down to single figure ppm at two different wavelengths. It is also able to discriminate between complex odours, here we present the results from 6 different chemicals. In this case, fixed frequency measurements were used as “virtual sensors” and their output then analysed by (PCA), which for all but one case, showed good separation

Design and development of a low-cost, portable monitoring device for indoor environment quality

This article describes the design and development of a low-cost, portable monitoring system for indoor environment quality (IEQ). IEQ is a holistic concept that encompasses elements of indoor air quality (IAQ), indoor lighting quality (ILQ), acoustic comfort, and thermal comfort (temperature and relative humidity). The unit is intended for the monitoring of temperature, humidity, PM2.5, PM10, total VOCs (×3), CO2, CO, illuminance, and sound levels. Experiments were conducted in various environments, including a typical indoor working environment and outdoor pollution, to evaluate the unit’s potential to monitor IEQ parameters. The developed system was successfully able to monitor parameter variations, based on specific events. A custom IEQ index was devised to rate the parameter readings with a simple scoring system to calculate an overall IEQ percentage. The advantages of the proposed system, with respect to commercial units, is associated with better customisation and flexibility to implement a variety of low-cost sensors. Moreover, low-cost sensor modules reduce the overall cost to provide a comprehensive, portable, and real-time monitoring solution. This development facilities researchers and interested enthusiasts to become engaged and proactive in participating in the study, management, and improvement of IEQ

Development of a tuneable NDIR optical electronic nose

Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1−10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO2 and CH4), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO2 and CH4. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications

An improved machine learning pipeline for urinary volatiles disease detection:Diagnosing diabetes

Motivation The measurement of disease biomarkers in easily–obtained bodily fluids has opened the door to a new type of non–invasive medical diagnostics. New technologies are being developed and fine–tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine. These VOCs are known to contain a range of information on the relevant person’s metabolism and can in principle be used for disease diagnostic purposes. Key to the effective use of such data are well–developed data processing pipelines, which are necessary to extract the most useful data from the complex underlying biological structure. Results In this study, we present a new data analysis pipeline for FAIMS data, and demonstrate a number of improvements over previously used methods. We evaluate the effect of a series of candidate operational steps during data processing, such as the use of wavelet transforms, principal component analysis (PCA), and classifier ensembles. We also demonstrate the use of FAIMS data in our pipeline to diagnose diabetes on the basis of a simple urine sample using machine learning classifiers. We present results for data generated from a case-control study of 115 urine samples, collected from 72 type II diabetic patients, with 43 healthy volunteers as negative controls. The resulting pipeline combines the steps that resulted in the best classification model performance. These include the use of a two–dimensional discrete wavelet transform, and the Wilcoxon rank–sum test for feature selection. We are able to achieve a best ROC curve AUC of 0.825 (0.747–0.9, 95% CI) for classification of diabetes vs control. We also note that this result is robust to changes in the data pipeline and different analysis runs, with AUC > 0.80 achieved in a range of cases. This is a substantial improvement in performance over previously used data processing methods in this area. Our ability to make strong statements about FAIMS ability to diagnose diabetes is sadly limited, as we found confounding effects from the demographics when including these data in the pipeline. The demographics alone produced a best AUC of 0.87 (0.795–0.94, 95% CI). While the combination of the demographics and FAIMS data resulted in an improvement on the AUC (0.907; 0.848–0.97, 95% CI), it did not prove to be a significant difference. Nevertheless, the pipeline itself shows a significant improvement in performance over more basic methods which have been used with FAIMS data in the past

Variation in gas and volatile compound emissions from human urine as it ages, measured by an electronic nose

The medical profession is becoming ever more interested in the use of gas-phase biomarkers for disease identification and monitoring. This is due in part to its rapid analysis time and low test cost, which makes it attractive for many different clinical arenas. One technology that is showing promise for analyzing these gas-phase biomarkers is the electronic nose-an instrument designed to replicate the biological olfactory system. Of the possible biological media available to "sniff", urine is becoming ever more important as it is easy to collect and to store for batch testing. However, this raises the question of sample storage shelf-life, even at -80 °C. Here we investigated the effect of storage time (years) on stability and reproducibility of total gas/vapour emissions from urine samples. Urine samples from 87 patients with Type 2 Diabetes Mellitus were collected over a four-year period and stored at -80 °C. These samples were then analyzed using FAIMS (field-asymmetric ion mobility spectrometry-a type of electronic nose). It was discovered that gas emissions (concentration and diversity) reduced over time. However, there was less variation in the initial nine months of storage with greater uniformity and stability of concentrations together with tighter clustering of the total number of chemicals released. This suggests that nine months could be considered a general guide to a sample shelf-life

Development of a Tuneable NDIR Optical Electronic Nose

Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO2 and CH4), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO2 and CH4. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications

Experimental molecular communications in obstacle rich fluids

A key potential advantage of molecular communications is the ability of molecules to propagate in complex propagation channels. Here, we experimentally test the information rate in both relatively laminar and turbulent conditions by tracking the information molecules using particle image velocimetry (PIV). A number of obstacle types are placed in the channel and we observe that they do not generally lower the information rate, but may actually improve it in some cases. This is explained by the formation of self-sustaining coherent vortex signal structures with a higher signal-to-noise ratio (SNR), which are caused by obstacles. The initial results demonstrate experimentally that the variety of obstacles tested do not impact data rate and may in some cases enhance it

Non-Invasive Diagnosis of Diabetes by Volatile Organic Compounds in Urine Using FAIMS and Fox4000 Electronic Nose

The electronic nose (eNose) is an instrument designed to mimic the human olfactory system. Usage of eNose in medical applications is more popular than ever, due to its low costs and non-invasive nature. The eNose sniffs the gases and vapours that emanate from human waste (urine, breath, and stool) for the diagnosis of variety of diseases. Diabetes mellitus type 2 (DM2) affects 8.3% of adults in the world, with 43% being underdiagnosed, resulting in 4.9 million deaths per year. In this study, we investigated the potential of urinary volatile organic compounds (VOCs) as novel non-invasive diagnostic biomarker for diabetes. In addition, we investigated the influence of sample age on the diagnostic accuracy of urinary VOCs. We analysed 140 urine samples (73 DM2, 67 healthy) with Field-Asymmetric Ion Mobility Spectrometry (FAIMS); a type of eNose; and FOX 4000 (AlphaM.O.S, Toulouse, France). Urine samples were collected at UHCW NHS Trust clinics over 4 years and stored at −80 °C within two hours of collection. Four different classifiers were used for classification, specifically Sparse Logistic Regression, Random Forest, Gaussian Process, and Support Vector on both FAIMS and FOX4000. Both eNoses showed their capability of diagnosing DM2 from controls and the effect of sample age on the discrimination. FAIMS samples were analysed for all samples aged 0⁻4 years (AUC: 88%, sensitivity: 87%, specificity: 82%) and then sub group samples aged less than a year (AUC (Area Under the Curve): 94%, Sensitivity: 92%, specificity: 100%). FOX4000 samples were analysed for all samples aged 0⁻4 years (AUC: 85%, sensitivity: 77%, specificity: 85%) and a sub group samples aged less than 18 months: (AUC: 94%, sensitivity: 90%, specificity: 89%). We demonstrated that FAIMS and FOX 4000 eNoses can discriminate DM2 from controls using urinary VOCs. In addition, we showed that urine sample age affects discriminative accuracy

Thermally modulated CMOS compatible particle sensor for air quality monitoring

Combating the health effects of particulate matter pollution requires affordable and reliable real-time air quality monitoring. The potential for large-scale manufacturing of acoustic wave-based sensors makes them an interesting option for low-cost, low-power particle sensing applications. This paper demonstrates a solidly mounted resonator particulate matter sensor with improved sensitivity through thermal modulation of the device. A novel, CMOS compatible solidly mounted resonator with an integrated microheater was designed, manufactured, and tested. In simulations, it was found that particle deposition increases both the heat loss and the thermal time constant of the solidly mounted resonator. The effect of this on the resonant frequency shift of the device caused by particle deposition is investigated closely in this work. The sensitivity of the devices to particle deposition was tested experimentally with and without temperature modulation by placing the device in a test chamber and allowing the randomised settling of aerosolised particles on its surface. The unmodulated sensor demonstrated a particle mass sensitivity of ~ 40 Hz/ng whilst the mass sensitivity of the temperature-modulated device was shown to improve by a factor of nearly × 5 to 190 Hz/ng. Temperature modulation also improved the detection limit from 100 ng to 50 ng. Further experiments were conducted by adding an impactor mechanism to have a more controlled measurement set up. To this effect a thermophoretic particle deposition mechanism was added to the device to enhance its performance. It was demonstrated that the repeatability of measurements was significantly improved, making the device a promising low-cost technology for air quality monitoring