James A. Covington
@warwick.ac.uk
School of Engineering
University of Warwick
RESEARCH INTERESTS
Artificial olfaction and electronic noses.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Hamed Karami, Mohammed Kamruzzaman, James A. Covington, M.élynda Hassouna, Yousef Darvishi, Maiken Ueland, Sigfredo Fuentes, and Marek Gancarz
Elsevier BV
Malgorzata Wesoly, Emma Daulton, Sascha Jenkins, Sarah van Amsterdam, John Clarkson, and James A. Covington
American Chemical Society (ACS)
Chuhong Wang and James A. Covington
MDPI AG
Olfactory displays are digital devices designed to provide the controlled release of odours to users. In this paper, we report on the design and development of a simple vortex-based olfactory display for a single user. By employing a vortex approach, we are able to minimize the amount of required odour, whilst still producing a good user experience. The olfactory display designed here is based on a steel tube with 3D-printed apertures and solenoid valve operation. A number of different design parameters (such as aperture size) were investigated, and the best combination was combined into a functional olfactory display. User testing was undertaken with four volunteers who were presented with four different odours, at two concentrations. It was found that the time to identify an odour was not strongly related to concentration. However, the intensity of the odour was correlated. We also found that there was a wide variance in human panel results when considering the length of time for a subject to identify an odour to its perceived intensity. This is likely linked to the subject group receiving no odour training before the experiments. However, we were able to produce a working olfactory display, based on a scent project method, which could be applicable to a range of application scenarios.
Yasser M. Qureshi, Vitaly Voloshin, Luca Facchinelli, Philip J. McCall, Olga Chervova, Cathy E. Towers, James A. Covington, and David P. Towers
MDPI AG
Mosquito-borne diseases account for around one million deaths annually. There is a constant need for novel intervention mechanisms to mitigate transmission, especially as current insecticidal methods become less effective with the rise of insecticide resistance among mosquito populations. Previously, we used a near infra-red tracking system to describe the behaviour of mosquitoes at a human-occupied bed net, work that eventually led to an entirely novel bed net design. Advancing that approach, here we report on the use of trajectory analysis of a mosquito flight, using machine learning methods. This largely unexplored application has significant potential for providing useful insights into the behaviour of mosquitoes and other insects. In this work, a novel methodology applies anomaly detection to distinguish male mosquito tracks from females and couples. The proposed pipeline uses new feature engineering techniques and splits each track into segments such that detailed flight behaviour differences influence the classifier rather than the experimental constraints such as the field of view of the tracking system. Each segment is individually classified and the outcomes are combined to classify whole tracks. By interpreting the model using SHAP values, the features of flight that contribute to the differences between sexes are found and are explained by expert opinion. This methodology was tested using 3D tracks generated from mosquito mating swarms in the field and obtained a balanced accuracy of 64.5% and an ROC AUC score of 68.4%. Such a system can be used in a wide variety of trajectory domains to detect and analyse the behaviours of different classes, e.g., sex, strain, and species. The results of this study can support genetic mosquito control interventions for which mating represents a key event for their success.
Yiyun Zhu, Chris Blackman, Pengfei Zhou, Sai Kiran Ayyala, James A. Covington, Yanbai Shen, Jinsheng Liang, Xiangxi Zhong, Caroline Knapp, and Ye Zhou
Elsevier BV
Nina M. Frerichs, Sofia el Manouni el Hassani, Nancy Deianova, Mirjam M. van Weissenbruch, Anton H. van Kaam, Daniel C. Vijlbrief, Johannes B. van Goudoever, Christian V. Hulzebos, Boris. W. Kramer, Esther J. d’Haens,et al.
MDPI AG
Early detection of late-onset sepsis (LOS) in preterm infants is crucial since timely treatment initiation is a key prognostic factor. We hypothesized that fecal volatile organic compounds (VOCs), reflecting microbiota composition and function, could serve as a non-invasive biomarker for preclinical pathogen-specific LOS detection. Fecal samples and clinical data of all preterm infants (≤30 weeks’ gestation) admitted at nine neonatal intensive care units in the Netherlands and Belgium were collected daily. Samples from one to three days before LOS onset were analyzed by gas chromatography—ion mobility spectrometry (GC-IMS), a technique based on pattern recognition, and gas chromatography—time of flight—mass spectrometry (GC-TOF-MS), to identify unique metabolites. Fecal VOC profiles and metabolites from infants with LOS were compared with matched controls. Samples from 121 LOS infants and 121 matched controls were analyzed using GC-IMS, and from 34 LOS infants and 34 matched controls using GC-TOF-MS. Differences in fecal VOCs were most profound one and two days preceding Escherichia coli LOS (Area Under Curve; p-value: 0.73; p = 0.02, 0.83; p < 0.002, respectively) and two and three days before gram-negative LOS (0.81; p < 0.001, 0.85; p < 0.001, respectively). GC-TOF-MS identified pathogen-specific discriminative metabolites for LOS. This study underlines the potential for VOCs as a non-invasive preclinical diagnostic LOS biomarker.
Sammy Hassan, Ryan M. Mushinski, Tilahun Amede, Gary D. Bending, and James A. Covington
MDPI AG
This article outlines the design and implementation of an internet-of-things (IoT) platform for the monitoring of soil carbon dioxide (CO2) concentrations. As atmospheric CO2 continues to rise, accurate accounting of major carbon sources, such as soil, is essential to inform land management and government policy. Thus, a batch of IoT-connected CO2 sensor probes were developed for soil measurement. These sensors were designed to capture spatial distribution of CO2 concentrations across a site and communicate to a central gateway using LoRa. CO2 concentration and other environmental parameters, including temperature, humidity and volatile organic compound concentration, were logged locally and communicated to the user through a mobile (GSM) connection to a hosted website. Following three field deployments in summer and autumn, we observed clear depth and diurnal variation of soil CO2 concentration within woodland systems. We determined that the unit had the capacity to log data continuously for a maximum of 14 days. These low-cost systems have great potential for better accounting of soil CO2 sources over temporal and spatial gradients and possibly flux estimations. Future testing will focus on divergent landscapes and soil conditions.
Steven Laird, Luke Debenham, Danny Chandla, Cathleen Chan, Emma Daulton, Johnathan Taylor, Palashika Bhat, Lisa Berry, Peter Munthali, and James A. Covington
MDPI AG
Throughout the SARS-CoV-2 pandemic, diagnostic technology played a crucial role in managing outbreaks on a national and global level. One diagnostic modality that has shown promise is breath analysis, due to its non-invasive nature and ability to give a rapid result. In this study, a portable FTIR (Fourier Transform Infra-Red) spectrometer was used to detect chemical components in the breath from Covid positive symptomatic and asymptomatic patients versus a control cohort of Covid negative patients. Eighty-five patients who had a nasopharyngeal polymerase chain reaction (PCR) test for the detection of SARS-CoV-2 within the last 5 days were recruited to the study (36 symptomatic PCR positive, 23 asymptomatic PCR positive and 26 asymptomatic PCR negative). Data analysis indicated significant difference between the groups, with SARS-CoV-2 present on PCR versus the negative PCR control group producing an area under the curve (AUC) of 0.87. Similar results were obtained comparing symptomatic versus control and asymptomatic versus control. The asymptomatic results were higher than the symptomatic (0.88 vs. 0.80 AUC). When analysing individual chemicals, we found ethanol, methanol and acetaldehyde were the most important, with higher concentrations in the COVID-19 group, with symptomatic patients being higher than asymptomatic patients. This study has shown that breath analysis can provide significant results that distinguish patients with or without COVID-19 disease/carriage.
Zhiyuan Wu, Fengchun Tian, James A. Covington, Hantao Li, and Siyuan Deng
Institute of Electrical and Electronics Engineers (IEEE)
Sensor drift is often application-dependent and results in a reduction in the overall long-term performance of electronic noses. Even with drift compensation it is remains challenging to transfer these models to other application scenarios. In order to remedy this deficiency, different/generic chemicals are needed to provide a general-purpose calibration approach that can be applied to a wide range of electronic noses. In this article, we investigated a method to identify these chemicals based on four criteria (universality, safety, sensibility, and differentiation). This concept was tested on an in-house electronic nose comprising 37 gas sensors and four environmental sensors combined with an automatic gas acquisition system. The 14 different volatile compounds were tested over four months. The silhouette coefficient was used to evaluate the extent of the sensor drift. Six different chemicals (acetone, alcohol, ethyl acetate, tetrahydrofuran, acetaldehyde, and n-hexane) were finally selected as the most appropriate to calibrate our electronic nose (E-nose). We believe our research may motivate the design of a reasonable chemical selection method for the calibration of general-purpose E-noses.
Emma Ronde, Nina M. Frerichs, Shauni Brantenaar, Sofia El Manouni El Hassani, Alfian N. Wicaksono, James A. Covington, Nanne K. H. De Boer, Tim G. De Meij, Thomas Hankemeier, Irwin K. M. Reiss,et al.
Frontiers Media SA
Accurate prediction of preterm birth is currently challenging, resulting in unnecessary maternal hospital admittance and fetal overexposure to antenatal corticosteroids. Novel biomarkers like volatile organic compounds (VOCs) hold potential for predictive, bed-side clinical applicability. In a proof of principle study, we aimed to assess the predictive potential of urinary volatile organic compounds in the identification of pregnant women at risk for preterm birth. Urine samples of women with a high risk for preterm birth (≧24 + 0 until 36 + 6 weeks) were collected prospectively and analyzed for VOCs using gas chromatography coupled with an ion mobility spectrometer (GS-IMS). Urinary VOCs of women delivering preterm were compared with urine samples of women with suspicion of preterm birth collected at the same gestation period but delivering at term. Additionally, the results were also interpreted in combination with patient characteristics, such as physical examination at admission, microbial cultures, and placental pathology. In our cohort, we found that urinary VOCs of women admitted for imminent preterm birth were not significantly different in the overall group of women delivering preterm vs. term. However, urinary VOCs of women admitted for imminent preterm birth and delivering between 28 + 0 until 36 + 6 weeks compared to women with a high risk for preterm birth during the same gestation period and eventually delivering at term (&gt;37 + 0 weeks) differed significantly (area under the curve: 0.70). In addition, based on the same urinary VOCs, we could identify women with a confirmed chorioamnionitis (area under the curve: 0.72) and urinary tract infection (area under the curve: 0.97). In conclusion, urinary VOCs hold potential for non-invasive, bedside prediction of preterm birth and on the spot identification of intra-uterine infection and urinary tract infections. We suggest these observations are further explored in larger populations.
Joshua Nazareth, Daniel Pan, Jee Whang Kim, Jack Leach, James G Brosnan, Adam Ahmed, Emma Brodrick, Paul Bird, Alfian Wicaksono, Emma Daulton,et al.
Oxford University Press (OUP)
Abstract Background Rapid diagnostic and prognostic tests for coronavirus disease (COVID-19) are urgently required. We aimed to evaluate the diagnostic and prognostic ability of breath analysis using gas chromatography–ion mobility spectrometry (GC-IMS) in hospitalized patients with COVID-19. Methods Between February and May 2021, we took 1 breath sample for analysis using GC-IMS from participants who were admitted to the hospital for COVID-19, participants who were admitted to the hospital for other respiratory infections, and symptom-free controls, at the University Hospitals of Leicester NHS Trust, United Kingdom. Demographic, clinical, and radiological data, including requirement for continuous positive airway pressure (CPAP) ventilation as a marker for severe disease in the COVID-19 group, were collected. Results A total of 113 participants were recruited into the study. Seventy-two (64%) were diagnosed with COVID-19, 20 (18%) were diagnosed with another respiratory infection, and 21 (19%) were healthy controls. Differentiation between participants with COVID-19 and those with other respiratory tract infections with GC-IMS was highly accurate (sensitivity/specificity, 0.80/0.88; area under the receiver operating characteristics curve [AUROC], 0.85; 95% CI, 0.74–0.96). GC-IMS was also moderately accurate at identifying those who subsequently required CPAP (sensitivity/specificity, 0.62/0.80; AUROC, 0.70; 95% CI, 0.53–0.87). Conclusions GC-IMS shows promise as both a diagnostic tool and a predictor of prognosis in hospitalized patients with COVID-19 and should be assessed further in larger studies.
Robert W. Brown, David R. Chadwick, Gary D. Bending, Chris D. Collins, Helen L. Whelton, Emma Daulton, James A. Covington, Ian D. Bull, and Davey L. Jones
Elsevier BV
Malgorzata Labanska, Sarah van Amsterdam, Sascha Jenkins, John P. Clarkson, and James A. Covington
MDPI AG
The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.
Ali Khorramifar, Mansour Rasekh, Hamed Karami, James A. Covington, Sayed M. Derakhshani, Jose Ramos, and Marek Gancarz
MDPI AG
Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.
Sai Kiran Ayyala and James A. Covington
Institute of Electrical and Electronics Engineers (IEEE)
Stefan Kucharski, Pilar Ferrer, Federica Venturini, Georg Held, Alex S. Walton, Conor Byrne, James A. Covington, Sai Kiran Ayyala, Andrew M. Beale, and Chris Blackman
Royal Society of Chemistry (RSC)
NAP-XPS characterisation of SnO2 under operando conditions shows that resistance change, band bending and surface O-vacancy concentration are correlated with ambient O2 concentration, challenging current preconceptions of gas sensor function.
Caroline E. Boulind, Oliver Gould, Ben de Lacy Costello, Joanna Allison, Paul White, Paul Ewings, Alfian N. Wicaksono, Nathan J. Curtis, Anne Pullyblank, David Jayne,et al.
MDPI AG
Colorectal symptoms are common but only infrequently represent serious pathology, including colorectal cancer (CRC). A large number of invasive tests are presently performed for reassurance. We investigated the feasibility of urinary volatile organic compound (VOC) testing as a potential triage tool in patients fast-tracked for assessment for possible CRC. A prospective, multi-center, observational feasibility study was performed across three sites. Patients referred to NHS fast-track pathways for potential CRC provided a urine sample that underwent Gas Chromatography-Mass Spectrometry (GC-MS), Field Asymmetric Ion Mobility Spectrometry (FAIMS), and Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) analysis. Patients underwent colonoscopy and/or CT colonography and were grouped as either CRC, adenomatous polyp(s), or controls to explore the diagnostic accuracy of VOC output data supported by an artificial neural network (ANN) model. 558 patients participated with 23 (4%) CRC diagnosed. 59% of colonoscopies and 86% of CT colonographies showed no abnormalities. Urinary VOC testing was feasible, acceptable to patients, and applicable within the clinical fast track pathway. GC-MS showed the highest clinical utility for CRC and polyp detection vs. controls (sensitivity = 0.878, specificity = 0.882, AUROC = 0.896) but it is labour intensive. Urinary VOC testing and analysis are feasible within NHS fast-track CRC pathways. Clinically meaningful differences between patients with cancer, polyps, or no pathology were identified suggesting VOC analysis may have future utility as a triage tool.
Sofie Bosch, Dion S. J. Wintjens, Alfian Wicaksono, Marieke Pierik, James A. Covington, Tim G. J. de Meij, and Nanne K. H. de Boer
MDPI AG
The early prediction of changes in disease state allows timely treatment of patients with inflammatory bowel disease (IBD) to be performed, which improves disease outcome. The aim of this pilot study is to explore the potential of fecal volatile organic compound (VOC) profiles to predict disease course. In this prospective cohort, IBD patients were asked to collect two fecal samples and fill in a questionnaire at set intervals. Biochemically, active disease was defined by FCP ≥ 250 mg/g and remission was defined by FCP < 100 mg/g. Clinically, active disease was defined by a Harvey Bradshaw Index (HBI) ≥ 5 for Crohn’s disease or by a Simple Clinical Colitis Activity Index (SCCAI) ≥ 3 for ulcerative colitis. Clinical remission was defined by an HBI < 4 or SCCAI ≤ 2. Fecal VOC profiles were measured using gas chromatography-ion mobility spectrometry (GC-IMS). The fecal samples collected first were included for VOC analysis to predict disease state at the following collection. A total of 182 subsequently collected samples met the disease-state criteria. The fecal VOC profiles of samples displaying low FCP levels at the first measurements differed between patients preceding exacerbation versus those who remained in remission (AUC 0.75; p < 0.01). Samples with FCP levels at the first time point displayed different VOC profiles in patients preceding remission compared with those whose disease remained active (AUC 0.86; p < 0.01). Based on disease activity scores, there were no significant differences in any of the comparisons. Alterations in fecal VOC profiles preceding changes in FCP levels may be useful to detect disease-course alterations at an early stage. This could lead to earlier treatment, decreased numbers of complications, surgery and hospital admission.
Michael McFarlane, Ramesh P. Arasaradnam, Beryl Reed, Emma Daulton, Alfian Wicaksono, Heena Tyagi, James A. Covington, and Chuka Nwokolo
MDPI AG
Coeliac disease (CD) patients are distinguishable from healthy individuals via urinary volatile organic compounds (VOCs) analysis. We exposed 20 stable CD patients on gluten-free diet (GFDs) to a 14-day, 3 g/day gluten challenge (GCh), and assessed urinary VOC changes. A control cohort of 20 patients continued on GFD. Urine samples from Days 0, 7, 14, 28 and 56 were analysed using Lonestar FAIMS and Markes Gas Chromatography–Time of Flight–Mass Spectrometer (GC-TOF-MS). VOC signatures on D (day) 7–56 were compared with D0. Statistical analysis was performed using R. In GCh patients, FAIMS revealed significant VOC differences for all time points compared to D0. GC-TOF-MS revealed significant changes at D7 and D14 only. In control samples, FAIMS revealed significant differences at D7 only. GC-TOF-MS detected no significant differences. Chemical analysis via GC-MS-TOF revealed 12 chemicals with significantly altered intensities at D7 vs. D0 for GCh patients. The alterations persisted for six chemicals at D14 and one (N-methyltaurine) remained altered after D14. This low-dose, short-duration challenge was well tolerated. FAIMS and GC-TOF-MS detected VOC signature changes in CD patients when undergoing a minimal GCh. These findings suggest urinary VOCs could have a role in monitoring dietary compliance in CD patients.
Malgorzata Labanska, Sascha Jenkins, Sarah Van Amsterdam, John Clarkson, and James Covington
IEEE
Among emerging novel trend streams in agriculture, techniques for the analysis of the volatile organic compounds emitted from plants are gaining increasing interest. In this work, the electronic nose abilities to detect onions infected with Fusarium oxysporum f. sp. cepae as well as to discriminate between samples with varying proportion of infected onion bulbs were evaluated. It was demonstrated that changes in the samples related to the development of the disease can be observed on the PCA plot. It was also proved that storage of the onions at a low temperature inhibits the development of the infection. Presented findings of the preliminary studies revealed that electronic nose is a promising analytical tool for the detection of the infection of the onion bulbs.
Amber Wang, Sammy S. Hassan, and James A. Covington
IEEE
Here we report on the design, construction, and testing of a multi-scent olfactory display that is intended as promotional tool for smell-related events. Our system used off-the-shelf spray bottles for scent generation, with a control and actuation system constructed around it. The spray bottle is actuated using a solenoid valve, combined with a fan to help deliver the aroma to the user. Scent switching is achieved by sliding the spray bottles on a fixed rail. A step motor is then used to select scents accurately. The final unit can release up to four different scents and testing results show that the unit can be controlled for scent delivery at a range of firing rates.
James A. Covington and Joanne Nock
IEEE
Environmental quality is a factor that affects us every day. It comprises of a range of factors from air pollution to sound. In this paper we report on the second-generation development of PONG, created for personalized environmental quality measurement, which uses an array of sensors connected to a smartphone app to give the user an immediate reading of the environment around them. A fully functional unit was developed and then tested around the University of Warwick and surrounding area.
Anyan Jiang, Fengchun Tian, James Anthony Covington, Maogang Jiang, and Zhiyuan Wu
Institute of Electrical and Electronics Engineers (IEEE)
Gas sensor plays a key role in many applications with sensitivity being a critical performance characteristic. Increasing the surface area of gas sensing material is one approach that can increase sensitivity. Fractal geometries, which have the large specific surface area and special fractal dimension, have previously been successfully used in the design of macrostructure and microstructure of gas sensors to improve their performance. In this article, the influence of geometrical structure of the substrate on the gas sensor performance has been investigated. Two fractal structures (Koch snowflake and Menger sponge) and one traditional structure (Cylinder) were fabricated by 3-D printing and coated in Ag-doped multiwalled carbon nanotube (Ag:MWCNT)-based sensing materials. The fabricated sensors were tested with nitrogen dioxide at different temperatures and humidity. Experimental results show that the sensitivity of gas sensors with fractal structures is increased more than twice that of those with traditional geometrical structures.
Deborah A. van den Brink, Tim de Meij, Daniella Brals, Robert H. J. Bandsma, Johnstone Thitiri, Moses Ngari, Laura Mwalekwa, Nanne K. H. de Boer, Alfian Wicaksono, James A. Covington,et al.
Springer Science and Business Media LLC
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Wangi Pandan Sari, Samuel Agbroko, and James Covington
AIP Publishing