Development Of Electronic Nose Research Articles

Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer

(1) Background: Colorectal cancer is one of the leading causes of cancer-related death, while early detection decreases incidence and mortality. Current screening programs involving fecal immunological testing and colonoscopy commonly bring about unnecessary colonoscopies, which adds burden to healthcare systems. The objective of this study was to provide an assessment of the diagnostic performance of an electronic nose serving as a complementary screening tool to improve current screening programs in clinical settings. (2) Methods: We conducted a case–control study that included patients from a medical center with colorectal cancer and non-colorectal cancer controls. We analyzed the composition of volatile organic compounds in their exhaled breath using the electronic nose. We then used machine learning algorithms to develop predictive models and provided the estimated accuracy and reliability of the breath testing. (3) Results: We enrolled 77 patients, with 40 cases and 37 controls. The area under the curve, Kappa coefficient, sensitivity, and specificity of the selected model were 0.87 (95% CI 0.76–0.95), 0.66 (95% CI 0.49–0.83), 0.81, and 0.85. For subjects at an early stage of disease, the sensitivity and specificity were 0.90 and 0.85. Excluding smokers, the sensitivity and specificity were 0.88 and 0.92. (4) Conclusions: This study highlights the promising potential of breath testing using an electronic nose for enabling early detection and reducing unnecessary treatments. However, more independent data for external validation are required to ensure applicability and generalizability.

Development of electronic nose for detection of micro-mechanical damages in strawberries.

A self-developed portable electronic nose and its classification model were designed to detect and differentiate minor mechanical damage to strawberries. The electronic nose utilises four metal oxide sensors and four electrochemical sensors specifically calibrated for strawberry detection. The selected strawberries were subjected to simulated damage using an H2Q-C air bath oscillator at varying speeds and then stored at 4°C to mimic real-life mechanical damage scenarios. Multiple feature extraction methods have been proposed and combined with Principal Component Analysis (PCA) dimensionality reduction for comparative modelling. Following validation with various models such as SVM, KNN, LDA, naive Bayes, and subspace ensemble, the Grid Search-optimised SVM (GS-SVM) method achieved the highest classification accuracy of 0.84 for assessing the degree of strawberry damage. Additionally, the Feature Extraction ensemble classifier achieved the highest classification accuracy (0.89 in determining the time interval of strawberry damage). This experiment demonstrated the feasibility of the self-developed electronic nose for detecting minor mechanical damage in strawberries.

Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection.

We adapted an existing, spaceflight-proven, robust "electronic nose" (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using "leave-one-out" training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home.

Electronic nose and wireless sensor network for environmental monitoring application in pulp and paper industry: a review.

Pulp and paper industries emit various odorous gases during the pulp production and paper-making phase, which are unpleasant and have harmful effects on the human body. The working staffs are continuously exposed to these gases and develop various health issues. Hence, regular monitoring and analysis of such gases are necessary to avoid any sudden high concentration exposure and to prevent adverse health effects on the staff. An electronic nose (EN) has an array of gas sensors with an alert system for early detection of gases. Various ENs have been developed for varying applications till date. The detailed knowledge of the sensors used, their sensitivity and technology is helpful in development of any EN. The objective of this study is to comprehensively review various developed ENs with respect to their gas sensing and pattern recognition (PR) technologies. The information on gases released from pulp and paper industries is also compiled. The evolution of EN technology, its various applications, challenges in developing EN and its utility in safeguarding the industrial workers' life have been described. Further, gap analysis among previously developed EN, contemporary EN and wireless sensor network (WSN) is elaborated. It will facilitate future researchers for better selection of sensors and PR technologies while developing EN. The commonly used sensing technologies are described with their advantages, disadvantages and working principles. Metal oxide semiconductor (MOS) gas sensor and ANN algorithm show better result and hence recommended in the development of EN, whereas ZigBee protocol has been widely used for WSN.

Trends in the Development of Electronic Noses Based on Carbon Nanotubes Chemiresistors for Breathomics.

The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath.

Study on the Discrimination of Possible Error Sources That Might Affect the Quality of Volatile Organic Compounds Signature in Dairy Cattle Using an Electronic Nose.

Simple SummaryIn recent decades, remarkable progress in the development of electronic nose (EN) technologies, particularly for disease detection, has been accomplished through the disclosure of novel methods and associated devices, mainly for the detection of volatile organic compounds (VOCs). Herein, we assessed the ability of a novel EN technology (MENT-EGAS prototype) to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. Principal Component Analyses (PCA) evidenced the presence in the analyzed samples of sufficient information to consent the discrimination of different environmental backgrounds, feed headspaces and exhalated breath between two groups of cows fed with two different types of feed. Moreover, discrimination was also observed within the same group between exhalated breaths sampled before and after feed intake. Based on these findings, we provided evidence that the MENT-EGAS prototype can identify error sources with accuracy. Livestock precision farming technologies are powerful tools for monitoring animal health and welfare parameters in a continuous and automated way.Electronic nose devices (EN) have been developed for detecting volatile organic compounds (VOCs). This study aimed to assess the ability of the MENT-EGAS prototype-based EN to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. This study was performed on a dairy farm using 11 (n = 11) multiparous Holstein-Friesian cows. The cows were divided into two groups housed in two different barns: group I included six lactating cows fed with a lactating diet (LD), and group II included 5 non-lactating late pregnant cows fed with a far-off diet (FD). Each group was offered 250 g of their respective diet; 10 min later, exhalated breath was collected for VOC determination. After this sampling, 4 cows from each group were offered 250 g of pellet concentrates. Ten minutes later, the exhalated breath was collected once more. VOCs were also measured directly from the feed’s headspace, as well as from the environmental backgrounds of each. Principal component analyses (PCA) were performed and revealed clear discrimination between the two different environmental backgrounds, the two different feed headspaces, the exhalated breath of groups I and II cows, and the exhalated breath within the same group of cows before and after the feed intake. Based on these findings, we concluded that the MENT-EGAS prototype can recognize several error sources with accuracy, providing a novel EN technology that could be used in the future in precision livestock farming.

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes.

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the “smell”, “taste”, and “color” of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review’s purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.

Combinatorial selectivity with an array of phthalocyanines functionalized TiO2/ZnO heterojunction thin film sensors

The development of electronic noses requires the control of the selectivity pattern of each sensor of the array. Organic chemistry offers a manifold of possibilities to this regard but in many cases the chemical sensitivity is not matched with the response of electronic sensor. The combination of organic and inorganic materials is an approach to transfer the chemical sensitivities of the sensor to the measurable electronic signals. In this paper, this approach is demonstrated with a hybrid material made of phthalocyanines and a bilayer structure of ZnO and TiO2. Results show that the whole spectrum of sensitivity of phthalocyanines results in changes of the resistance of the sensor, and even the adsorption of compounds, such as hexane, which cannot change the resistance of pure phthalocyanine layers, elicits changes of the sensor resistance. Furthermore, since phthalocyanines are optically active, the sensitivity in dark and visible light are different. Thus, operating the sensor in dark and light two different signals per sensors can be extracted. As a consequence, an array of 3 sensors made of different phthalocyanines results in a virtual array of six sensors. The sensor array shows a remarkable selectivity respect to a set of test compounds. Principal component analysis scores plot illustrates that hydrogen bond basicity and dispersion interaction are the dominant mechanisms of interaction.

Subspace alignment based on an extreme learning machine for electronic nose drift compensation

The drift caused by gas sensors has always been a bottleneck in the development of electronic nose (E-nose) systems. Traditional drift compensation methods directly correct the drift components, making such approaches time-consuming and laborious. In the field of E-nose drift compensation, cross-domain adaption learning is an efficient technique. In this paper, we propose a novel subspace alignment extreme learning machine (SAELM) that considers multiple criteria to construct a unified extreme learning machine (ELM)-based feature representation space and thus achieve domain alignment. First, the method minimizes both the geometric and statistical distributions between different domains. Second, the dependence between features and labels is enhanced using the Hilbert–Schmidt independence criterion (HSIC) to alleviate the blurring of the correspondence between the two caused by drift. Third, to improve the feature extraction ability of the subspace learning method, the l2,1 norm is leveraged to constrain the output weights of the ELM. The aim of this method is to learn a robust subspace to increase the consistency between domains and enhance the feature–label​ dependency of the source domain while preserving the intrinsic information of both domains. Extensive experiments on sensor drift data are conducted, and the proposed SAELM method yields the greatest improvements on E-nose drift datasets.

Neural mechanism mimetic selective electronic nose based on programmed M13 bacteriophage

The electronic nose is a reliable practical sensor device that mimics olfactory organs. Although numerous studies have demonstrated excellence in detecting various target substances with the help of ideal models, biomimetic approaches still suffer in practical realization because of the inability to mimic the signal processing performed by olfactory neural systems. Herein, we propose an electronic nose based on the programable surface chemistry of M13 bacteriophage, inspired by the neural mechanism of the mammalian olfactory system. The neural pattern separation (NPS) was devised to apply the pattern separation that operates in the memory and learning process of the brain to the electronic nose. We demonstrate an electronic nose in a portable device form, distinguishing polycyclic aromatic compounds (harmful in living environment) in an atomic-level resolution (97.5% selectivity rate) for the first time. Our results provide practical methodology and inspiration for the second-generation electronic nose development toward the performance of detection dogs (K9).

Layered Double Hydroxide-Based Gas Sensors for VOC Detection at Room Temperature.

Miniaturized low-cost sensors for volatile organic compounds (VOCs) have the potentiality to become a fundamental tool for indoor and outdoor air quality monitoring, to significantly improve everyday life. Layered double hydroxides (LDHs) belong to the class of anionic clays and are largely employed for NOx detection, while few results are reported on VOCs. In this work, a novel LDH coprecipitation method is proposed. For the first time, a study comparing four LDHs (ZnAl–Cl, ZnFe–Cl, ZnAl–NO3, and MgAl–NO3) is carried out to investigate the sensing performances. As explored through several microscopy and spectroscopy analyses, LDHs show a morphology characterized by a large surface area and a three-dimensional hierarchical flowerlike architecture with micro- and nanopores that induce a fast diffusion and highly effective surface interaction of the target gases. The fabricated sensors, operating at room temperature, are able to reversibly and selectively detect acetone, ethanol, ammonia, and chlorine vapors, reaching significant sensing response values up to 6% at 21 °C. The results demonstrate that by changing the LDHs’ composition, it is possible to modulate the sensitivity and selectivity of the sensor, helping the discrimination of different analytes, and the consequent integration on a sensor array paves the way for electronic nose development.

The effects of secondary doping on ink-jet printed PEDOT:PSS gas sensors for VOCs and NO2 detection

In the study of conductive conjugated polymers, electrical doping has long played an important role. A new polymeric gas sensor has been successfully fabricated by means of an ink-jet printer using a conductive aqueous formulation of poly(3,4-ethylenedioxythiophene) poly(styrene-sulfonate) (PEDOT:PSS). A simple yet robust treatment method for the irreversible secondary doping was performed (by H2SO4 and MeOH post-treatments) to enhance conductivity and improve gas sensing performance. Real time gas sensing measurements were carried out by exposing the devices with eight different analytes in a low concentrations range of VOCs vapors up to 5 % of the saturated vapor pressure, 10 ppm of NO2 and up to 10 % of relative humidity (RH) at 21 °C, exploiting dry air as carrier and diluting gas. The gas response, obtained as the ratio between the steady-state resistance variation and the baseline resistance of the device, was evaluated for different PEDOT:PSS post-treated sensors. An unexpected behavior of PEDOT:PSS post-treated with concentrated H2SO4 was observed, while MeOH and diluted H2SO4 post-treated sensors exhibited improved response towards all investigated analytes. The best performances were obtained towards 5 % of ammonia and NO2 with a gas response of 6 % and 28 % respectively with the device post-treated with pure methanol and 16 % with the sensor post-treated with diluted sulfuric acid. Furthermore, long-term stability and the influence of temperature were evaluated on the fabricated sensors. Altogether, these promising results allow a better understanding of the secondary doping effects on the electrical and sensing properties, paving the way for electronic nose development.

Development of electronic nose (Shrimp-Nose) for the determination of perishable quality and shelf-life of cultured Pacific white shrimp (Litopenaeus Vannamei)

Production and consumption of Pacific white shrimp (Litopenaeus vannamei) have drastically increased over the years, owing to its taste and high nutritional value. At the same time, monitoring its perishable quality and shelf-life to meet export standards is proving to be a major concern. In this context, Shrimp-Nose has been designed and developed using six partially selective metal oxide-based gas sensors (TGS 880, 823, 826, 2600, 2602, and 822 (Figaro, Japan)) for the rapid freshness assessment. To extensively investigate the quality changes in both the perishable and iced storage conditions, Shrimp-Nose measurements were observed at different time intervals (in terms of hours (h) for un-iced conditions and days for 2 °C refrigerated storage conditions). Pattern recognition algorithms based on multivariate analyses such as Principal Component Analysis (PCA), Decision Tree, Random Forest, K-Nearest Neighbour (KNN) and Soft-max Regression were implemented in which Soft-max Regression algorithm showed the decision accuracies of 96.29% and 95.73% towards un-iced and iced samples respectively. Supporting experiments such as pH, Total Volatile Base Nitrogen (TVBN), Fourier Transform Infrared (FTIR) spectra, texture, microbial, and sensory analysis were investigated at different time intervals to monitor the quality changes. Also, melanosis was investigated at different time intervals to estimate the shelf-life of shrimp samples. Shrimp-Nose measurements are found to be in good agreement with the results observed using supporting experiments.

Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids.

A real-time electric nose (E-nose) with a metal oxide sensor (MOS) array was developed to monitor 5 highly flammable liquids (ethanol, tetrahydrofuran, turpentine, lacquer thinner, and gasoline) in this work. We found that temperature had a significant impact on the test results and temperature control could efficiently improve the performance of our E-nose. The results of our qualitative analysis showed that principal component analysis (PCA) could not efficiently distinguish these samples compared to a back-propagation artificial neural network (BP-ANN) which had a 100% accuracy rate on the test samples. Quantitative analysis was performed by regression analysis and the average errors were 9.1%–18.4%. In addition, through anti-interference training, the E-nose could filter out the potential false alarm caused by mosquito repellent, perfume and hair jelly.

Advances in Electronic Nose Development for Application to Agricultural Products

High agricultural product quality is a fundamental requirement for consumers and odor is an important indicator that reflects product quality. Conventional analysis methods are based on sensory evaluation or on physic-chemical methods (e.g., high-performance liquid chromatography, liquid chromatography with tandem mass spectrometry). Analysis methods should be simple, quick, nondestructive, inexpensive, and specific, with good reproducibility and repeatability. Electronic noses can meet many of these requirements. Electronic nose development for agricultural product quality analysis has been increasing since the 1980s. This review summarizes the extensive achievements to date in electronic nose development for quality analysis/evaluation of agricultural products. First, we briefly introduce electronic noses and describe commonly used data analysis methods (e.g., artificial neural networks (ANNs), principal component analysis (PCA), linear discriminant analysis (LDA)). We then discuss the application of electronic noses to analysis of agricultural products (e.g., fruit, vegetables, tea, grain, meat from livestock and poultry, fish), including freshness evaluation, quality classification, and authenticity assessment variety identification, geographical origin identification, and disease detection. Finally, the problems, prospects, and likely future development of electronic noses for agricultural product quality analysis are highlighted.

Wearable electronic nose for human skin odor identification: A preliminary study

Human skin odor is an important pheromone that can serve as an indicator for physiological status, or as a “fingerprint” for individual identification. It is a complex task to identify the organic components that constitute skin odor. An electronic nose is proposed to identify skin odors. This paper consists four parts. First is to prepare a gas sensor array, two functionalized carbon nanotubes (CNTs) are encapsulated by three different polymers to form six sensing materials, and they are deposited on a substrate to form a sensing array. A circuit is designed for data acquisition and wireless transmission. Second, gas chromatograph-mass spectrometry (GS-MC) is carried out to demonstrate the organic components of skin odors. Then, humidity and gas sensing properties of the sensor array are evaluated and tested in the laboratory. Third, human study is designed and conducted for monitoring of skin odor using the prepared electronic nose. The last is signal processing, to achieve highly accurate odor identification, contrast response is extracted as the feature. The best clustering accuracy, which is computed by unsupervised K-means, is 91.67%. This paper represents an important step in the research and development of electronic nose for human skin odor identification.

Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases

The development of electronic-nose (e-nose) technologies for disease diagnostics was initiated in the biomedical field for detection of biotic (microbial) causes of human diseases during the mid-1980s. The use of e-nose devices for disease-diagnostic applications subsequently was extended to plant and animal hosts through the invention of new gas-sensing instrument types and disease-detection methods with sensor arrays developed and adapted for additional host types and chemical classes of volatile organic compounds (VOCs) closely associated with individual diseases. Considerable progress in animal disease detection using e-noses in combination with metabolomics has been accomplished in the field of veterinary medicine with new important discoveries of biomarker metabolites and aroma profiles for major infectious diseases of livestock, wildlife, and fish from both terrestrial and aquaculture pathology research. Progress in the discovery of new e-nose technologies developed for biomedical applications has exploded with new information and methods for diagnostic sampling and disease detection, identification of key chemical disease biomarkers, improvements in sensor designs, algorithms for discriminant analysis, and greater, more widespread testing of efficacy in clinical trials. This review summarizes progressive advancements in utilizing these specialized gas-sensing devices for numerous diagnostic applications involving noninvasive early detections of plant, animal, and human diseases.

Development of Electronic Nose with High Stable Sample Heater to Classify Quality Levels of Local Black Tea

An electronic nose (e-nose) equipped with a stable temperature controller has been successfully developed. Sensor response is very influenced by the temperature of the sample. The performance of the e-nose was then evaluated to differentiate the quality levels of black tea (Q1, Q2, and Q3). The black tea samples were purchased from Tambi Tea Industry in Central Java, Indonesia. Here, the quality is based on the information from the factory. For each quality level, the measurement was repeated 31 times. Therefore, the total of measurements is 93, and only six sensors, i.e. MQ-7, TGS 813, TGS 2602, TGS 826, TGS 2620 and TGS 825 have the highest influence on the patterns. By using linear discriminant analysis (LDA), the e-nose is able to classify the quality levels of all sample almost 100%. The Q1 and Q2 are 100% predicted, while for the 31 data from Q3, total 29 data can be predicted correctly.

Diagnosing gastrointestinal illnesses using fecal headspace volatile organic compounds.

Volatile organic compounds (VOCs) emitted from stool are the components of the smell of stool representing the end products of microbial activity and metabolism that can be used to diagnose disease. Despite the abundance of hydrogen, carbon dioxide, and methane that have already been identified in human flatus, the small portion of trace gases making up the VOCs emitted from stool include organic acids, alcohols, esters, heterocyclic compounds, aldehydes, ketones, and alkanes, among others. These are the gases that vary among individuals in sickness and in health, in dietary changes, and in gut microbial activity. Electronic nose devices are analytical and pattern recognition platforms that can utilize mass spectrometry or electrochemical sensors to detect these VOCs in gas samples. When paired with machine-learning and pattern recognition algorithms, this can identify patterns of VOCs, and thus patterns of smell, that can be used to identify disease states. In this review, we provide a clinical background of VOC identification, electronic nose development, and review gastroenterology applications toward diagnosing disease by the volatile headspace analysis of stool.

Development of Electronic Nose with Low-Cost Dynamic Headspace for Classifying Vegetable Oils and Animal Fats

A portable electronic nose (e-nose) using low-cost dynamic headspace and commercially metal oxide gas sensors has been developed. This paper reports evaluation on the performance of the e-nose to classify vegetable oils (sunflower and grape seed oils) and animal fats (mutton, chicken and pig fats). The e-nose consists of a dynamic headspace sampling, a gas sensor array and a real-time data acquisition system based on ATMega-16 microcontroller. The dynamic headspace can divided into two chambers, i.e. sample and gas sensor array room. It is also equipped with three small fans for adjusting sensing and purging processes. Principal component analysis (PCA) was used for measurement data analysis after all features being extracted. The first two principal components were kept because they accounted for 91.1% of the variance in the data set (first and second principals accounted for 72.9, 18.2% of the variance, respectively). This results show that the e-nose can distinguish vegetable oils and animal fats. This work demonstrates for the future that the e-nose with low-cost dynamic headspace technique may be applied to the identification of oils and fats in halal authentication.