Measurement of odor intensity by an electronic nose

Machine Learning Driven Atom-Thin Materials for Fragrance Sensing

Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

Absorption processes in reducing the odor nuisance of wastewater

Deep social awareness, especially in highly developed countries, imposes pressure on entrepreneurs and service providers, forcing them to undertake effective actions to minimize the effects of their activities also in terms of the emission of malodorous substances. The article presents information on the absorption processes harnessed in the deodorization of gases from wastewater management and the characteristics of these gases. Avoiding emissions is not always possible, hence there is a need to conduct an inventory of such gases and use deodorization methods. The specificity of gases from wastewater management and their prevalence urge the search for cheap and easy-to-use deodorization methods. It is obvious that the selection of deodorization technology is driven by many factors and should be preceded by a thorough analysis of the possibilities and limitations of various solutions. The aim of this article is, therefore, to present the characteristics of gases from wastewater management and to discuss various technologies based on absorption processes as a technology for deodorizing such gases in order to help potential investors choose an emission-reducing method suitable for particular conditions.•Malodorous substances in wastewater management.•Deodorization using water and chemical absorption.•Deodorization using biological purification.

Field application of cost-effective sensors for the monitoring of NH3, H2S, and TVOC in environmental treatment facilities and the estimation of odor intensity

Odor is usually a complex mixture of various compounds. In many countries, odor complaints have been addressed using the air dilution olfactory method (ADOM) to reduce their malodor complaint. In this study, continuous monitoring of ammonia, hydrogen sulfide, and total volatile organic compounds (TVOC) using sensors was conducted in facilities for municipal and livestock wastewater treatment (LWT), and for food waste composting (FWC). Odor intensity was modeled by multivariate linear regression using sensor monitoring data with air dilution measured by the ADOM. In testing the performance of sensors in the lab, all three sensors showed acceptable values for linearity, accuracy, repeatability, lowest detection limit, and response time, so the sensors were acceptable for application in the field. In on-site real-time monitoring, the three sensors functioned well in the three environmental facilities during the testing period. Average ammonia and hydrogen sulfide concentrations were high in the LWT facility, while TVOC showed the highest concentration in the FWC facility. A longer sampling time is necessary for ammonia monitoring. Odor intensity from individual sensor data correlated well to complex odor measured by the ADOM. Finally, we suggest a protocol for field application of sensor monitoring and odor data reproduction.Implications: We suggest a protocol for the field application of sensor monitoring and odor data estimation in this study. This study can be useful to a policy maker and field operator to reduce odor emission through the determination of a more effective treatment technology and removal pathway for individual odorants.

Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system

Conventionally, odours emitted by different sources present in wastewater treatment plants (WWTPs) are measured by dynamic olfactometry, where a human panel sniffs and analyzes air bags collected from the plant. Although the method is considered the gold standard, the process is costly, slow, and infrequent, which does not allow operators to quickly identify and respond to problems. To better monitor and map WWTP odour emissions, here we propose a small rotary-wing drone equipped with a lightweight (1.3-kg) electronic nose. The "sniffing drone" sucks in air via a ten-meter (33-foot) tube and delivers it to a sensor chamber where it is analyzed in real-time by an array of 21 gas sensors. From the sensor signals, machine learning (ML) algorithms predict the odour concentration that a human panel using the EN13725 methodology would report. To calibrate and validate the predictive models, the drone also carries a remotely controlled sampling device (compliant with EN13725:2022) to collect sample air in bags for post-flight dynamic olfactometry. The feasibility of the proposed system is assessed in a WWTP in Spain through several measurement campaigns covering diverse operating regimes of the plant and meteorological conditions. We demonstrate that training the ML algorithms with dynamic (transient) sensor signals measured in flight conditions leads to better performance than the traditional approach of using steady-state signals measured in the lab via controlled exposures to odour bags. The comparison of the electronic nose predictions with dynamic olfactometry measurements indicates a negligible bias between the two measurement techniques and 95 % limits of agreement within a factor of four. This apparently large disagreement, partly caused by the high uncertainty of olfactometric measurements (typically a factor of two), is more than offset by the immediacy of the predictions and the practical advantages of using a drone-based system.

Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity

Odors are one of the most diverse and complicated gaseous mixtures so that their discrimination is challenging yet attractive because of the rich information about their origin. The more similar the properties of odors are, the more difficult the discrimination becomes. The practical applications, however, often demand such discrimination, especially with a compact sensing platform. In this paper, we show that a nanomaterial designed for a specific type of odors can clearly discriminate them even with a single nanomechanical sensing channel. Fuel oils and their mixture are used as a model target that has similar chemical properties but different compositions mainly consisting of paraffinic, olefinic, naphthenic, and aromatic hydrocarbons. We demonstrate using octadecyl functionalized silica-titania nanoparticles that the difference in the compositions is successfully picked up based on their high affinity for the aliphatic hydrocarbons and alkyl chain length dependent nonlinear viscoelastic behavior. Such a properly designed material is proved to derive sufficient information from a series of analytes to discriminate them even with a single sensing element. This approach provides a guideline to prepare various sensors whose response properties are distinct and optimized depending on applications.

A Multi-Sensor System for Sea Water Iodide Monitoring and Seafood Quality Assurance: Proof-of-Concept Study

Iodine is a trace chemical element fundamental for a healthy human organism. Iodine deficiency affects about 2 billion people worldwide causing from mild to severe neurological impairment, especially in children. Nevertheless, an adequate nutritional intake is considered the best approach to prevent such disorders. Iodine is present in seawater and seafood, and its common forms in the diet are iodide and iodate; most iodide in seawater is caused by the biological reduction of the thermodynamically stable iodate species. On this basis, a multisensor instrument which is able to perform a multidimensional assessment, evaluating iodide content in seawater and seafood (via an electrochemical sensor) and discriminating when the seafood is fresh or defrosted quality (via a Quartz Micro balance (QMB)-based volatile and gas sensor), is strategic for seafood quality assurance. Moreover, an electronic interface has been opportunely designed and simulated for a low-power portable release of the device, which should be able to identify seafood over or under an iodide threshold previously selected. The electrochemical sensor has been successfully calibrated in the range 10–640 μg/L, obtaining a root mean square error in cross validation (RMSECV) of only 1.6 μg/L. Fresh and defrosted samples of cod, sea bream and blue whiting fish have been correctly discriminated. This proof-of-concept work has demonstrated the feasibility of the proposed application which must be replicated in a real scenario.

Drift Compensation on Massive Online Electronic-Nose Responses

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

The type of microorganism and substrate determines the odor fingerprint of dried bacteria targeting microbial protein production

The rapidly increasing demand for protein has led to the pursuit of new protein sources, among which microbial protein (MP) is one of the most promising. Although the nutritional properties of MP are important and often well-studied, the sensory properties of the microbial cells will in part determine the commercial success of the product and are much less investigated. Here we assessed the odor fingerprint of dried bacteria originating from pure cultures and enriched mixed microbial communities using an electronic nose (e-nose). The e-nose discriminated between the different MP sources, while the choice of culture and substrate substantially affected their volatile organic compound (VOC) profile. The most dominant odor descriptors (>20% of VOC peak area) were sweet, fruity and fishy, while the mixed cultures presented higher peak areas indicating potentially more intense aromas than the pure cultures. The e-nose can detect the suitability of new MP sources and determine their best end-use.

Health impact of odor from on-situ sewage sludge aerobic composting throughout different seasons and during anaerobic digestion with hydrolysis pretreatment

Aerobic composting and anaerobic digestion with hydrolysis pretreatment are two mainstream methods used to recycle and reclaim sewage sludge. However, during these sludge treatment processes, many odors are emitted that may cause severe emotional disturbance and health risks to those exposed. This study identified odor pollution (i.e. sensory influence, odor contribution, and human risks) from samples collected during sludge aerobic composting throughout different seasons as well as during anaerobic digestion with hydrolysis pretreatment. Odor intensity, odor active values, and permissible concentration-time weighted averages for ammonia and five volatile sulfur compounds were assessed. The results revealed serious odor pollution from all sampling sites during aerobic composting, especially in winter. Excessively strong odors were identified in the composting workshop, with total odor active values between 997 and 8980 which accounted for 78.45%-96.18% of the total sludge aerobic composting plant. Levels of ammonia and dimethyl disulfide in the ambient air were high enough to harm employees' health. During anaerobic digestion, excessively strong odors were identified in dehydration workshop 2, and the total odor active values of six odors reached 32,268, with ammonia and hydrogen sulfide levels significant enough to harm human health.

Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by a FPGA

It is a daunting challenge to measure the concentration of each component in natural gas, because different components in mixed gas have cross-sensitivity for a single sensor. We have developed a mixed gas identification device based on a neural network algorithm, which can be used for the online detection of natural gas. The neural network technology is used to eliminate the cross-sensitivity of mixed gases to each sensor, in order to accurately recognize the concentrations of methane, ethane and propane, respectively. The neural network algorithm is implemented by a Field-Programmable Gate Array (FPGA) in the device, which has the advantages of small size and fast response. FPGAs take advantage of parallel computing and greatly speed up the computational process of neural networks. Within the range of 0–100% of methane, the test error for methane and heavy alkanes such as ethane and propane is less than 0.5%, and the response speed is several seconds.

Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO

The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, which could visually represent the odor changes produced during the yeast culture process, was developed using eleven porphyrins and one pH indicator. Second, odor information of the culture substrate was obtained during the process using the homemade color sensor. Next, color components, which came from different color sensitive spots, were extracted first and then optimized using the ant colony optimization (ACO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized feature color components for quantitative monitoring of cell concentration. Results demonstrated that BPNN models, which were developed using two color components from FTPPFeCl (component B) and MTPPTE (component B), can obtain better results on the basis of both the comprehensive consideration of the model performance and the economic benefit. In the validation set, the average of determination coefficient R P 2 was 0.8837 and the variance was 0.0725, while the average of root mean square error of prediction (RMSEP) was 1.0033 and the variance was 0.1452. The overall results sufficiently demonstrate that the optimized sensor array can satisfy the monitoring accuracy and stability of the cell concentration in the process of yeast culture.

A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks-VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50-were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.

The Odor Characterizations and Reproductions in Machine Olfactions: A Review

Machine olfaction is a novel technology and has been developed for many years. The electronic nose with an array of gas sensors, a crucial application form of the machine olfaction, is capable of sensing not only odorous compounds, but also odorless chemicals. Because of its fast response, mobility and easy of use, the electronic nose has been applied to scientific and commercial uses such as environment monitoring and food processing inspection. Additionally, odor characterization and reproduction are the two novel parts of machine olfaction, which extend the field of machine olfaction. Odor characterization is the technique that characterizes odorants as some form of general odor information. At present, there have already been odor characterizations by means of the electronic nose. Odor reproduction is the technique that re-produces an odor by some form of general odor information and displays the odor by the olfactory display. It enhances the human ability of controlling odors just as the control of light and voice. In analogy to visual and auditory display technologies, is it possible that the olfactory display will be used in our daily life? There have already been some efforts toward odor reproduction and olfactory displays.

Wound odor: current methods of treatment and need for objective measures

Chronic wounds are an enormous burden to society, costing billions of dollars annually in the USA alone. Despite the extensive research into methods to heal chronic wounds, many remain unhealed for months to years. There is a need to focus on patient reported outcomes to improve quality of life in patients with non-healing wounds. Wound odor has a significant impact on patient quality of life; however, relatively little information is available on the management of wound odor. We review the current data available on wound odor and discuss the need for standardized objective measures of odor to improve research quality. An independent search of the PubMed and Embase databases was conducted using combinations of the following words or phrases: "wounds," "chronic wounds," "diabetic ulcers," "venous leg ulcers (VLUs)," "malignant ulcers," "odor," "odour," "smell," "malodor," "artificial olfaction," "electronic nose," and "e-nose." Article references were also searched for significance. There are few overall studies on wound odor, and fewer randomized controlled trials. Current trials on odor have consistent weaknesses such as subjective measures and poor methodology. No single odor treatment modality has been demonstrated to be widely effective for wound odor or superior to other methods. Future research should incorporate objective measures of odor such as electronic noses into clinical trials.

Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks

This paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected to investigation. Evaluation of predicted odour intensity and hedonic tone was performed with selected artificial neural network structures with the activation functions tanh and Leaky rectified linear units (Leaky ReLUs) with the parameter a=0.03. Correctness of identification of odour interactions in the odorous mixtures was determined based on the results obtained with the electronic nose instrument and non-linear data analysis. This value (average) was at the level of 88% in the case of odour intensity, whereas the average was at the level of 74% in the case of hedonic tone. In both cases, correctness of identification depended on the number of components present in the odorous mixture.

Determination of Odour Interactions of Three-Component Gas Mixtures Using an Electronic Nose

The paper presents an application of an electronic nose prototype comprised of six TGS-type sensors and one PID-type sensor to identify odour interaction phenomena in odorous three-component mixtures. The investigation encompassed eight odorous mixtures—toluene-acetone-triethylamine and formaldehyde-butyric acid-pinene—characterized by different odour intensity and hedonic tone. A principal component regression (PCR) calibration model was used for evaluation of predicted odour intensity and hedonic tone. Correctness of identification of odour interactions in the odorous three-component mixtures was determined based on the results obtained with the electronic nose. The results indicated a level of 75–80% for odour intensity and 57–73% for hedonic tone. The average root mean square error of prediction amounted to 0.03–0.06 for odour intensity determination and 0.07–0.34 for hedonic tone evaluation of the odorous three-component mixtures.

Chronic exposure to odorous chemicals in residential areas and effects on human psychosocial health: dose-response relationships

Perceived air pollution, including environmental odor pollution, is known to be an environmental stressor that affects individuals' psychosocial health and well-being. However, very few studies have been able to quantify exposure-response associations based on individual-specific residential exposures to a proxy gas and to examine the mechanisms underlying these associations. In this study, individual-specific exposures in non-urban residential environments during 2005-2010 on a gas released from animal biodegradable wastes (ammonia, NH3) were calculated by the Danish Eulerian long-range transport model and the local-scale transport deposition model. We used binomial and multinomial logistic regression and mediation analyses to examine the associations between average exposures and questionnaire-based data on psychosocial responses, after controlling for person-specific covariates. About 45% of the respondents were annoyed by residential odor pollution. Exposures were associated with annoyance (adjusted odds ratio [ORadj]=3.54, 95% confidence interval [CI]=2.33-5.39), health risk perception (ORadj=4.94; 95% CI=1.95-12.5) and behavioral interference (ORadj=3.28; 95% CI=1.77-6.11), for each unit increase in loge(NH3 exposure). Annoyance was a strong mediator in exposure-behavior interference and exposure-health risk perception relationships (81% and 44% mediation, respectively). Health risk perception did not play a mediating role in exposure-annoyance or exposure-behavioral interference relationships. This is the first study to provide a quantitative estimation of the dose-response associations between ambient NH3 exposures and psychosocial effects caused by odor pollution in non-urban residential outdoor environments. It further shows that these effects are both direct and mediated by other psychosocial responses. The results support the use of NH3 as a proxy gas of air pollution from animal biodegradable wastes in epidemiologic studies.

Characterization of the olfactory impact around a wastewater treatment plant: optimization and validation of a hydrogen sulfide determination procedure based on passive diffusion sampling

A monitoring program based on an indirect method was conducted to assess the approximation of the olfactory impact in several wastewater treatment plants (in the present work, only one is shown). The method uses H2S passive sampling using Palmes-type diffusion tubes impregnated with silver nitrate and fluorometric analysis employing fluorescein mercuric acetate. The analytical procedure was validated in the exposure chamber. Exposure periods ofat least 4 days are recommended. The quantification limit of the procedure is 0.61 ppb for a 5-day sampling, which allows the H2S immission (ground concentration) level to be measured within its low odor threshold, from 0.5 to 300 ppb. Experimental results suggest an exposure time greater than 4 days, while recovery efficiency of the procedure, 93.0+/-1.8%, seems not to depend on the amount of H2S collected by the samplers within their application range. The repeatability, expressed as relative standard deviation, is lower than 7%, which is within the limits normally accepted for this type of sampler. Statistical comparison showed that this procedure and the reference method provide analogous accuracy. The proposed procedure was applied in two experimental campaigns, one intensive and the other extensive, and concentrations within the H2S low odor threshold were quantified at each sampling point. From these results, it can be concluded that the procedure shows good potential for monitoring the olfactory impact around facilities where H2S emissions are dominant.

Residential exposure to outdoor air pollution from livestock operations and perceived annoyance among citizens

Epidemiological studies have shown that residential exposure to livestock odors can affect the health and wellbeing of rural citizens. However, exposure-response models for this relationship have not been developed. One of the main challenges is to identify a compound that can be used as proxy for livestock odor exposure. In this paper we developed models that describe the relationship between long-term averaged outdoor residential ammonia (NH(3)) exposures and livestock odor annoyance experienced by rural residents, and investigated person-related variables associated with annoyance responses. We used emission-based atmospheric dispersion modeling data to estimate household-specific outdoor concentrations and survey data to characterize the study subjects. Binomial and multinomial logistic regressions were used for model development. Residential NH(3) exposure was positively associated with moderate, high and extreme odor annoyance (adjusted odds ratio=10.59; 95% confidence interval: 1.35-83.13, for each unit increase in Log(e)NH(3) exposure). Specific characteristics of the exposed subjects (i.e., age, time per week spent at home, presence of children at home and job) act as co-determinants of odor annoyance responses. Predictive models showed classification accuracies of 67-72%. The results suggest that NH(3) exposure in the residential outdoor environment can be used as a predictor of livestock odor annoyance in population studies.

Characterization of combinatorial patterns generated by multiple two-component sensors in E. coli that respond to many stimuli

Two-component systems enable bacteria to sense changes in their environment and adjust gene expression in response. Multiple two-component systems could function as a combinatorial sensor to discriminate environmental conditions. A combinatorial sensor is composed of a set of sensors that are non-specifically activated to different magnitudes by many stimuli, such that their collective activity pattern defines the signal. Using promoter reporters and flow cytometry, we measured the response of three two-component systems in Escherichia coli that have been previously reported to respond to many environmental stimuli (EnvZ/OmpR, CpxA/CpxR, and RcsC/RcsD/RcsB). A chemical library was screened for the ability to activate the sensors and 13 inducers were identified that produce different patterns of sensor activity. The activities of the three systems are uncorrelated with each other and the osmolarity of the inducing media. Five of the seven possible non-trivial patterns generated by three sensors are observed. This data demonstrate one mechanism by which bacteria are able to use a limited set of sensors to identify a diverse set of compounds and environmental conditions.

Monitoring techniques for odour abatement assessment

Odorous emissions from sewers and wastewater treatment plants are a complex mixture of volatile chemicals that can cause annoyance to local populations, resulting in complaints to wastewater operators. Due to the variability in hedonic tone and chemical character of odorous emissions, no analytical technique can be applied universally for the assessment of odour abatement performance. Recent developments in analytical methodologies, specifically gas chromatography, odour assessment approaches (odour wheels, the odour profile method and dynamic olfactometry), and more recently combined gas chromatography-sensory analysis, have contributed to improvements in our ability to assesses odorous emissions in terms of odorant concentration and composition. This review collates existing knowledge with the aim of providing new insight into the effectiveness of sensorial and characterisation approaches to improve our understanding of the fate of odorous emissions during odour abatement. While research in non-specific sensor array (e-nose) technology has resulted in progress in the field of continuous odour monitoring, more successful long term case-studies are still needed to overcome the early overoptimistic performance expectations. Knowledge gaps still remain with regards to the decomposition of thermally unstable volatile compounds (especially sulfur compounds), the inability to predict synergistic, antagonistic, or additive interactions among odorants in combined chemical/sensorial analysis techniques, and the long term stability of chemical sensors due to sensor drift, aging, temperature/relative humidity effects, and temporal variations. Future odour abatement monitoring will require the identification of key odorants to facilitate improved process selection, design and management.

A new intelligent electronic nose system for measuring and analysing livestock and poultry farm odours

This paper introduces a new portable intelligent electronic nose system developed especially for measuring and analysing livestock and poultry farm odours. It can be used in both laboratory and field. The sensor array of the proposed electronic nose consists of 14 gas sensors, a humidity sensor, and a temperature sensor. The gas sensors were especially selected for the main compounds from the livestock farm odours. An expert system called "Odour Expert" was developed to support researchers' and farmers' decision making on odour control strategies for livestock and poultry operations. "Odour Expert" utilises several advanced artificial intelligence technologies tailored to livestock and poultry farm odours. It can provide more advanced odour analysis than existing commercially available products. In addition, a rank of odour generation factors is provided, which refines the focus of odour control research. Field experiments were conducted downwind from the barns on 14 livestock and poultry farms. Experimental results show that the predicted odour strengths by the electronic nose yield higher consistency in comparison to the perceived odour intensity by human panel. The "Odour Expert" is a useful tool for assisting farmers' odour management practises.

Prospects for clinical application of electronic-nose technology to early detection of Mycobacterium tuberculosis in culture and sputum

Ziehl-Neelsen (ZN) staining for the diagnosis of tuberculosis (TB) is time-consuming and operator dependent and lacks sensitivity. A new method is urgently needed. We investigated the potential of an electronic nose (EN) (gas sensor array) comprising 14 conducting polymers to detect different Mycobacterium spp. and Pseudomonas aeruginosa in the headspaces of cultures, spiked sputa, and sputum samples from 330 culture-proven and human immunodeficiency virus-tested TB and non-TB patients. The data were analyzed using principal-component analysis, discriminant function analysis, and artificial neural networks. The EN differentiated between different Mycobacterium spp. and between mycobacteria and other lung pathogens both in culture and in spiked sputum samples. The detection limit in culture and spiked sputa was found to be 1 x 10(4) mycobacteria ml(-1). After training of the neural network with 196 sputum samples, 134 samples (55 M. tuberculosis culture-positive samples and 79 culture-negative samples) were used to challenge the model. The EN correctly predicted 89% of culture-positive patients; the six false negatives were the four ZN-negative and two ZN-positive patients. The specificity and sensitivity of the described method were 91% and 89%, respectively, compared to culture. At present, the reasons for the false negatives and false positives are unknown, but they could well be due to the nonoptimized system used here. This study has shown the ability of an electronic nose to detect M. tuberculosis in clinical specimens and opens the way to making this method a rapid and automated system for the early diagnosis of respiratory infections.

Monitoring haemodialysis using electronic nose and chemometrics

An ever-increasing number of patients have to undergo regular renal dialysis to compensate for acute or chronic renal failure. The adequacy of the treatment has a profound effect on patients' morbidity and mortality. Therefore, it is necessary to assess the delivered dialysis dose. For the quantification of the dialysis dose, two parameters are most commonly used, namely the K(t)/V value (normalised dose of dialysis) and the urea reduction rate, yet the prescribed dialysis dose often differs from the actual delivered dialysis dose. Currently, no interactive process is available to ensure optimal treatment. The aim of this study was to investigate the potential for an "electronic nose" as a novel monitoring tool for haemodialysis. Blood samples were analysed using an electronic nose, comprising an array of 14 conducting polymer sensors, and compared to traditional biochemistry. Principal component analysis and hierarchical cluster analysis were applied to evaluate the data, and demonstrated the ability to distinguish between pre-dialysis blood from post-dialysis blood independent of the method used. It is concluded that the electronic nose is capable of discriminating pre-dialysis from post-dialysis blood and hence, together with an appropriate classification model, suitable for on-line monitoring.