Experimental setup to study poisoning effects of different materials on chemical sensors used in E-nose systems

Commercial metal-oxide semiconductor (MOS) gas sensors have been widely used by recent studies as detection units of electronic noses (e-nose) in various applications including disease diagnosis. However, the enoses employing the MOS sensors can only discriminate a limited number of odor groups due to their poor selectivity. Preliminary studies have shown that the selectivity of the MOS sensors can be enhanced by jointly integrating with other sensory units such as QCM or potentiometric sensors, which, however, involves complex interface circuitry and measurement procedures. In contrast, this paper presents a hybrid electronic nose that combines olfactory information from an MOS sensor array together with a compact paperbased colorimetric sensor array which is simpler and easier to utilize. The proposed system employs total 8 MOS sensors, and the compact paper-based colorimetric sensors are fabricated with indicator dyes such as phenol red, methyl red, and methylene blue. Color profiles of the paper-based sensors are captured using a USB-microscope and the alterations of the dyes during the gas exposure are monitored. The improvement of the system performance in classifying six volatile organic compounds (VOC) are investigated by comparing the classification results of the system with and without the colorimetric sensors. The measurement data from both sensor arrays are mapped to the feature space using principal component analysis (PCA) for pattern extraction. It was confirmed that pattern separation among the target VOCs could be improved based on data fusion of these two sensor arrays. This hybrid e-nose system may be useful for improvement of VOC classification performance.

Commercial metal oxide semiconductor (MOS) sensors usually employed in electronic noses (e-noses) are well known for being low-cost, portability, and ease of use. However, these sensors can only identify a limited number of odors due to insufficient selectivity. Recent studies improved the selectivity by jointly integrating the MOS sensors with additional sensor sources. However, the published hybrid systems involved complex fabrication and measurement procedures. On the contrary, this work utilizes paper-based colorimetric sensors which are simpler and easier to use. This proposed hybrid system consists of 8 commercial metal oxide sensors and a paper-based colorimetric sensor coated with phenol red, methyl red, and methylene blue. Six volatile organic compounds (VOCs) are classified using this developed hybrid system. Each sensor system is compared with the hybrid system using principal component analysis (PCA) and hierarchical clustering analysis (HCA). It was found that the metal oxide sensors alone can identify 5 VOCs, while the colorimetric sensors can identify 2 VOCs at best. Finally, the hybrid system can discriminate all the 6 target VOCs based on 12 features selected by ANOVA (Analysis of Variance) feature selection coupled with support vector machine (SVM) classfier.

An electronic nose (E-nose) system equipped with a gas sensor array and real-time control panel was developed for a fast diagnosis of whitefly infestation in tomato plants. Profile changes of volatile organic compounds (VOCs) released from tomato plants under different treatments (i.e., whitefly infestation, mechanical damage, and no treatment) were successfully determined by the developed E-nose system. A rapid sensor response with high sensitivity towards whitefly-infested tomato plants was observed in the E-nose system. Results of principal component analysis (PCA) and hierarchical clustering analysis (HCA) indicated that the E-nose system was able to provide accurate distinguishment between whitefly-infested plants and healthy plants, with the first three principal components (PCs) accounting for 87.4% of the classification. To reveal the mechanism of whitefly infestation in tomato plants, VOC profiles of whitefly-infested plants and mechanically damaged plants were investigated by using the E-nose system and GC-MS. VOCs of 2-nonanol, oxime-, methoxy-phenyl, and n-hexadecanoic acid were only detected in whitefly-infested plants, while compounds of dodecane and 4,6-dimethyl were only found in mechanically damaged plant samples. Those unique VOC profiles of different tomato plant groups could be considered as bio-markers for diagnosing different damages. Moreover, the E-nose system was demonstrated to have the capability to differentiate whitefly-infested plants and mechanically damaged plants. The relationship between sensor performance and VOC profiles confirmed that the developed E-nose system could be used as a fast and smart device to detect whitefly infestation in greenhouse cultivation.

An electronic nose (E-nose) system equipped with a sensitive sensor array was developed for fast diagnosis of aphid infestation on greenhouse tomato plants at early stages. Volatile organic compounds (VOCs) emitted by tomato plants with and without aphid attacks were detected using both the developed E-nose system and gas chromatography mass spectrometry (GC-MS), respectively. Sensor performance, with fast sensor responses and high sensitivity, were observed using the E-nose system. A principle component analysis (PCA) indicated accurate diagnosis of aphid-stressed plants compared to healthy ones, with the first two PCs accounting for 86.7% of the classification. The changes in VOCs profiles of the healthy and infested tomato plants were quantitatively determined by GC-MS. Results indicated that a group of new VOCs biomarkers (linalool, carveol, and nonane (2,2,4,4,6,8,8-heptamethyl-)) played a role in providing information on the infestation on the tomato plants. More importantly, the variation in the concentration of sesquiterpene VOCs (e.g., caryophyllene) and new terpene alcohol compounds was closely associated with the sensor responses during E-nose testing, which verified the reliability and accuracy of the developed E-nose system. Tomato plants growing in spring had similar VOCs profiles as those of winter plants, except several terpenes released from spring plants that had a slightly higher intensity.

Considerably decrease hunger and food insecurity in the world is one of sustainable development goals of the horizon 2030. Agriculture, which is one of most countries main sector and the only factor in the diet of the world’s population, is challenged by pest attack. Technology tools offer real opportunities to better protect farms from many damages caused to crops. In this work, an e-nose system using Metal Oxide Semiconductor sensors for early detection of fall armyworm (FAW) pest is proposed. This is based on a special architecture designed to have an affordable and efficient e-nose. Detailed investigations were carried out to identify sensors with potential sensitivity to FAW odors. Then, the sensors were used in a sensor matrix as electronic nose. An electronic acquisition card was achieved to interface the electrical output of the array of seven metal oxide semiconductor gas sensors exposed to an odor diffusion system with the computer. A LabVIEW program was developed for data analysis. The system was successfully exploited to study the response of the sensor array to volatile organic compounds (VOC) released by FAW and for optimizing the data acquisition, as well as signal preprocessing, storage, and wave forms presentation. Experiments were carried out using real FAW. The results and analysis presented in this paper show evidence of discrimination of Fall armyworm’s VOC signature, thus the first detection of FAW presence by e-nose system.

All fruits emit some specific volatile organic compounds (VOCs) during their life cycle. These VOCs have specific characteristics, by using these characteristics fruit ripening stage can be identified without destructing the fruit. In this study, an application-specific electronic nose device was designed for monitoring fruit ripeness.The proposed electronic nose is cost-efficient and does not require any modern or costly laboratory instruments. Metal oxide semiconductor (MOS) sensors were used for designing the proposed electronic nose. These MOS sensors were integrated with a microcontroller board to detect and extract the meaningful features of VOCs, and an artificial neural network (ANN) algorithm was used for pattern recognition. Measurements were done with apples, bananas, oranges, grapes, and pomegranates. The designed electronic nose proved to be reliable in classifying fruit samples into three different fruit ripening stage (unripe, ripe, and over-ripe) with high precision and recall. The proposed electronic nose performed uniformly on all three fruit ripening stages with an average accuracy of ≥ 95%.

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspended 1D nanoheater, driven by duty cycling─characterized by repeated pulsed power inputs. The sensor's ultrafast thermal response, enabled by its small size, effectively decouples the effects of temperature and surface charge exchange on the MOS nanomaterial's conductivity. This provides distinct sensing signals that alternate between responses coupled with and decoupled from the thermally enhanced conductivity, all within a single time domain during duty cycling. The magnitude and ratio of these dual responses vary depending on the gas type and concentration, facilitating the early stage gas identification of five gas types within 30 s via a convolutional neural network (classification accuracy = 93.9%, concentration regression error = 19.8%). Additionally, the duty-cycling mode significantly reduces power consumption by up to 90%, lowering it to 160 μW to heat the sensor to 250 °C. Manufactured using only wafer-level batch microfabrication processes, this innovative e-nose system promises the facile implementation of battery-driven, long-term, and cost-effective IoT monitoring systems.

The integration of sensor technology and artificial intelligence (AI) is transforming agriculture, particularly in post-harvest management. This study focuses on utilizing an electronic nose (e-nose) system in conjunction with machine learning (ML) models to monitor and detect potato spoilage in storage environments. The e-nose system, equipped with sensitive gas sensors, detects volatile organic compounds (VOCs) emitted by potatoes during different spoilage stages. By analyzing these emissions, the system can identify early signs of spoilage, offering a valuable solution for mitigating post-harvest losses, which remain a significant challenge in the agricultural sector. Through a series of controlled experiments, VOCs were captured and analyzed using a neural network model, classifying the potatoes into three categories: fresh, mildly spoiled, and fully spoiled. The neural network was trained on data from multisensory gas analysis, achieving a high level of classification accuracy. This study demonstrates that the integration of e-nose technology and ML algorithms can effectively monitor potato quality in storage, providing real-time insights to optimize storage conditions, extend shelf life, and reduce wastage.

<b>Background and Objective:</b> The Huanglongbing (HLB) is one of the most destructive diseases affecting citrus worldwide. A major challenge in its management is its ability to remain asymptomatic for extended periods, delaying timely detection and control. This study aims to develop and evaluate a compact electronic nose (e-nose) system equipped with metal-oxide semiconductor (MOS) sensors for the early detection of Candidatus Liberibacter asiaticus infection in citrus leaves through Volatile Organic Compound (VOC) analysis under field-like conditions. <b>Materials and Methods:</b> A total of 454 Purworejo Siamese citrus leaf samples were collected from two orchards. The infection status of each sample was confirmed using conventional Polymerase Chain Reaction (PCR) prior to headspace VOC extraction. The cross-sensitive MOS sensor array converted VOC interactions into electrical signals, which were subsequently preprocessed, feature-extracted and analyzed using machine learning pipelines. Model selection and optimization were performed on baseline-shifted data. <b>Results:</b> A stratified 5-fold cross-validation using the Extra Trees algorithm successfully discriminated between PCR-confirmed Candidatus Liberibacter asiaticus-infected leaves and healthy controls, achieving an accuracy of 84.57% (95% confidence interval: 80.98%-88.15%). These results were obtained under field-like conditions and were further validated using headspace gas chromatography-mass spectrometry (HS-GC/MS), which revealed distinct VOC profiles for each group. <b>Conclusion:</b> This study demonstrates the potential of the electronic nose (e-nose) as a rapid, in-field screening tool capable of prioritizing samples for laboratory confirmation, thereby supporting effective HLB management.

In this study, we proposed a portable electronic nose (e-nose) system based on a microcontroller (MSP430-FG439) combined with a tin oxide ( SnO2 ) gas sensor, which was heated by a cyclic heating method, to recognize the volatile organic compounds (VOCs). We had demonstrated that this e-nose system can classify and quantify VOCs, such as methanol and ethanol. The sensitivity of the e-nose system had good linearity in the concentration range of 10–40 ppm of these two VOCs, respectively. This portable e-nose system was implemented with a microcontroller acted as CPU, an LCD for displaying information of gases in real time, a wireless communication system, ZigBee, and a warning system.

In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

A small electronic nose (E-Nose) system has been developed using an 8-channel vapor detection array and personal digital assistant (PDA). The sensor array chip, integrated on a single microheater-embedded polyimide substrate, was made of carbon black-polymer composites with different kinds of polymers and plasticizers. We have successfully classified various volatile organic compounds such as methanol, ethanol, i-propanol, benzene, toluene, n-hexane, n-heptane, and c-hexane with the aid of the sensor array chip, and have evaluated the resolution factors among them, quantitatively. To achieve a PDA-based E-Nose system, we have also elaborated small sensor-interrogating circuits, simple vapor delivery components, and data acquisition and processing programs. As preliminary results show, the miniaturized E-Nose system has demonstrated the identification of essential oils extracted from mint, lavender, and eucalyptus plants.

As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (μLED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In2O3 film coated with plasmonic metal nanoparticles (NPs) directly on the μLED. The e-nose system consists of two different μLED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-μLED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO2, acetone, methanol) was developed. The μLED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications.

This study presents regression methods for response analysis of electronic nose (E-nose) in concentration estimation of volatile organic chemicals (VOCs). Particularly study focuses on nonlinear support vector regression (SVR) methods and its performance comparison with linear regression analysis (LRA) method. E-nose system is realized with combination of polymers and carbon molecular sieves based odor filtering and 8 metal oxide semiconductor (MOX) sensors based sensing system. MOX sensor resistance Ra, due to chemical vapor adsorption is measured for five target VOCs including acetone, benzene, ethanol, pentanal, and propenoic acid and used in analysis. VOCs are exposed on sensor array at distinct concentration in between 3‒500 parts per million (ppm). Scatter plot and Spearman's rank correlation coefficient (ρ) is used to determine the dependence of sensor resistance on VOCs concentration prior to the regression analysis. Coefficient of determination R (R-squared) is computed to compare the performance of build regression models and to search optimal filtering material for VOCs quantification. 6 sensor in array containing pure polyvinyl chloride (PVC) as filtering material results maximum value of R = 0.9825 for ethanol vapors with radial basis kernel function based SVR method. Though the 5 sensor in array results minimum value of R = 0.0864 for pentanal vapors using linear kernel function based SVR method.

Common bunt disease in wheat is a serious threat to crops and food security. Rapid assessments of its severity are essential for effective management. The electronic nose (e-nose) system is used to capture volatile organic compounds (VOCs), particularly trimethylamine (TMA), which serves as a key marker of common bunt disease in wheat. In this paper, the GFNN (gas feature neural network) model is proposed for detecting VOCs from the e-nose system, providing a lightweight and efficient approach for assessing disease severity. Multiscale convolution is employed to extract both global and local features from gas data, and three attention mechanisms are used to focus on important features. GFNN achieves 98.76% accuracy, 98.79% precision, 98.77% recall, and an F1-score of 98.75%, with only 0.04 million parameters and 0.42 million floating-point operations per second (FLOPS). Compared to traditional and current deep learning models, GFNN demonstrates superior performance, particularly in small-sample-size scenarios. It significantly improves the deep learning performance of the model in extracting key gas features. This study offers a practical, rapid, and cost-effective method for monitoring and managing common bunt disease in wheat, enhancing crop protection and food security.