Search Results (338)

Soil pesticide-residue screening is important for ecological protection, food safety, and public health. However, conventional chromatographic and spectroscopic methods often require complex sample pretreatment, expensive instruments, trained operators, and long analysis times, which limits their use for rapid and large-scale screening. In this study, an electronic-nose system was developed for volatile-fingerprint classification of pesticide-treated loess soil. Six commercial pesticide formulations from three chemical categories were evaluated: deltamethrin and cyfluthrin as pyrethroids, glyphosate and chlorpyrifos as organophosphorus pesticides, and zineb and mancozeb as organosulfur pesticides. These compounds were selected to represent commonly used pesticides with different chemical structures and volatile profiles. A mirror-symmetric gas-sensing chamber was designed for a 26-sensor metal oxide semiconductor (MOS) array to improve gas-flow uniformity and response repeatability. A total of 960 pesticide-treated electronic-nose response curves were collected from four soil depths. Eight feature extraction methods and four classifiers were compared. The Synthetic Minority Over-sampling Technique (SMOTE) and Geometric SMOTE (G-SMOTE) were then evaluated using training-fold-only oversampling to reduce data-leakage risk in imbalanced classification. The results showed that k-nearest neighbors (KNN) combined with direct or transform-based features provided strong classification performance under controlled laboratory conditions. For minority-class recognition, SMOTE showed more consistent improvement than G-SMOTE in the tested pesticide–depth–feature combinations, although the effect depended on feature representation and pesticide class. These findings indicate that the proposed chamber/sensor-array/SMOTE framework is feasible for rapid volatile-fingerprint classification of pesticide-treated soil, but larger independent field datasets and quantitative chemical validation are still required before general deployment. Full article

This study presents a novel approach for model-based calibration transfer and drift compensation for metal oxide semiconductor (MOS) gas sensors. The sensors are lab-calibrated and different calibration models for each of the eight volatiles contained in the calibration are trained to allow for an interpretable quantification of individual volatiles in complex mixtures. Calibration transfer and drift compensation are used to compensate for domain shifts that particularly affect the model accuracy. Here, several domain shifts are considered, e.g., sensor-to-sensor variation among different production batches (calibration transfer) or time-related changes in sensor response like poisoning and aging (drift compensation). Such domain shifts can lead to a substantial performance degradation and are critical for reliable field deployment. Since interpretable and robust machine learning algorithms based on feature extraction, feature selection, and regression (FESR) are not inherently capable of model-based calibration transfer and drift compensation, recalibration typically requires time-consuming and labor-intensive laboratory calibration procedures. To address this challenge, a novel approach represents the interpretable FESR machine learning models as a deep neural network (IDNNRep), enabling the application of transfer learning techniques from the field of deep neural networks (DNNs). This allows the reuse of knowledge gained in an initial calibration domain and facilitates model transfer using only a small amount of new calibration data, thereby reducing calibration effort and time. The proposed method is evaluated across multiple gases, including acetone and toluene, for four domain-shift scenarios and compared with FESR models retrained exclusively on data from the new domain and orthogonal signal correction (OSC). The results demonstrate that the proposed approach reduces the root mean square error (RMSE) compared to the initial model, achieving values of 18.0–28.0 ppb (normalized RMSE: 6.3–9.3%) for both gases with only 0.1 of the calibration data, resulting in a reduction of up to 93% compared to the initial calibration model and 89% compared to the OSC. Furthermore, due to the interpretable nature of the underlying FESR structure, the calibration transfer enables additional sensor- and gas-specific insights. Full article

This paper presents the design, development, and large-scale deployment of a vehicular IoT-based methane sensing system for urban environmental monitoring. The proposed solution integrates a low-cost metal oxide semiconductor (MOS) methane sensor with a dual chamber gas sampling mechanism, embedded processing, and wireless communication via 4G/5G networks using a smartphone as a gateway. Methane concentration data are collected from sensors installed in moving vehicles, georeferenced in real time using GNSS, and transmitted to a cloud-based platform for storage and analysis. Field experiments were conducted in the metropolitan region of São Paulo, Brazil, using 16 instrumented vehicles over a 20-month period, covering approximately 192,274 km and generating more than 48 million measurements. The results reveal spatially consistent methane concentration patterns and identify urban areas with elevated levels exceeding global background concentrations. A comparative analysis with a commercial infrared-based mobile methane monitoring system showed consistent agreement in the identification of spatial methane concentration patterns and potential emission hotspots. These results demonstrate the effectiveness of the proposed system for scalable urban methane monitoring. Full article

Beer quality assessment increasingly requires rapid and scalable analytical tools for product discrimination and authenticity control. In this study, a data-driven metal oxide semiconductor (MOX) chemosensing approach was investigated for the discrimination of commercial lager beers with different alcohol contents and brands. Alcoholic and alcohol-free beer samples from four commercial brands were analyzed using a six-element SnO₂-based MOX sensor array, and the resulting response patterns were classified using supervised machine-learning algorithms. Headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC–MS) was employed as a reference technique to characterize volatile organic compound profiles and support the interpretation of sensor-based fingerprints. GC–MS analysis highlighted a shared volatile backbone dominated by fermentation-related compounds, while also revealing brand- and category-dependent differences in VOC distribution. The MOX sensor array captured these differences as multidimensional volatile fingerprints. Machine-learning models achieved high classification performance in brand-matched alcoholic versus alcohol-free comparisons, with balanced accuracy ranging from 0.937 to 1.000, while brand discrimination within the same category reached balanced accuracy values of 0.875 (alcoholic) and 0.933 (alcohol-free). These results highlight MOX-based chemosensing combined with data-driven analysis as a rapid, portable platform for beer discrimination, with applications in food quality screening, authenticity assessment, and at-line monitoring. Full article

Hydrogen is a clean energy carrier essential for carbon neutrality, but its invisible and odorless nature poses significant safety risks, particularly during low-concentration leaks. Although metal oxide semiconductor (MOS) sensors offer fast response and high sensitivity, their ability to detect ppb-level hydrogen remains limited. In this work, we present a high-performance hydrogen gas sensor based on nickel-doped tin dioxide (Ni-SnO₂) nanorods, directly grown on planar electrodes via aerosol-assisted chemical vapor deposition (AACVD). By optimizing the Ni doping ratio and nanorod morphology, the 3 wt% Ni-SnO₂ sensor achieves a low detection limit of 100 ppb for H₂, demonstrating promising potential for low-concentration hydrogen detection. Moreover, the sensor exhibits outstanding selectivity, with a response to 100 ppm H₂ nearly six times higher than that to the next most responsive interfering gas (NH₃). Comprehensive XPS and Raman analyses reveal that Ni doping introduces abundant oxygen vacancies and lattice defects, which are the key origins of the enhanced sensing performance. Notably, the 3 wt% Ni-SnO₂ sensor strikes an optimal balance between lattice defects and structural stability, delivering both high sensitivity and good moisture resistance with minimal baseline drift over weeks of operation. This work establishes a facile and scalable AACVD strategy for engineering defect-rich SnO₂ nanostructures, enabling sub-ppm hydrogen detection with high selectivity and long-term stability—addressing a critical gap in practical hydrogen safety monitoring. Full article

Recent demand for reliable, low-power, and cost-effective gas sensors has spurred research into chemiresistive materials that operate under ambient conditions. PEDOT and PEDOT:PSS combined with semiconductor metal oxides (SMOs) have attracted attention due to their complementary properties: polymer flexibility and stability, alongside oxide reactivity and robustness. This review highlights the integration of PEDOT and PEDOT:PSS with n- and p-type SMOs, concentrating on fabrication techniques, sensing mechanisms, and performance indicators, such as sensitivity, selectivity, and response time. Emphasis is placed on heterojunction engineering, morphology control, and the influence of particle size and environmental factors. Despite notable progress, challenges persist in long-term stability, selectivity in mixed gases, and performance under varying conditions. Interface engineering and composite optimisation show promise, with potential applications in environmental monitoring, industrial safety, and wearable diagnostics. Full article

Nowadays, the medical sector challenges young research teams to develop and propose new non-invasive diagnostic methods. As a potential response, gas sensors for biomarker detection in exhaled breath show promising results. In this paper, various gas sensors based on metal–oxide semiconductors and coated with different polymers are proposed, demonstrating the potential of these sensors in breathomics and health breath tests. The proposed sensors are based on TiO₂ sensing structures and are tuned through different methods. Furthermore, they are coated with polymers such as PV4D4, PTFE, PV3D3, and copolymers such as P(V3D3 + TFE). These polymers show improved efficiency for gas sensing structures as they act as filters for certain molecules. Full article

Food waste is often driven by consumer uncertainty about the spoilage of stored food, especially for cooked meal leftovers where microbial growth is the main concern. We analyzed whether metal oxide semiconductor (MOS) gas sensors placed inside ordinary food containers can monitor the edibility of leftovers, specifically cooked meatballs. Sensors were operated using temperature cycling to enhance selectivity, and cycle-aligned features were extracted. A prior calibration campaign produced information used to map cycle-aligned features into estimated gas concentrations for relevant VOCs. Total viable counts, which represent the growth of total number of spoilage microorganisms, were analyzed on days 0, 5 and 7 to determine the food’s freshness. Both the raw sensor features and the calibration-derived gas concentration estimates were analyzed with principal component analysis (PCA) and evaluated with a leave-one-sensor-out (LOSO) binary classifier for multiple food containers. PCA on the calibrated gas estimates revealed a dominant axis that consistently tracks food degradation over time across various containers. LOSO classification accuracy improved from 81.7% using raw sensor features to 87.8% using calibrated gas concentration estimates. These findings represent a proof of principle that calibrated MOS sensor systems can robustly support in situ edibility assessment for cooked food. Full article

High-sensitivity detection of formaldehyde is critically important for environmental protection and public health. Zinc oxide (ZnO) is a widely used core material for chemiresistive gas sensors; however, its conventional low-index facets suffer from a limited number of active sites, creating a bottleneck for further sensitivity enhancement. To overcome this limitation, this study pioneers the application of the highly reactive ZnO $\left[20\overline{2}1\right]$ high-index polar surface for formaldehyde detection. By leveraging its unique stepped atomic configuration and unprecedented density of coordination-unsaturated active sites, we systematically investigate the formaldehyde adsorption behavior and the underlying sensing mechanism using first-principles calculations based on density functional theory (DFT). The pristine ZnO $\left[20\overline{2}1\right]$ surface exhibits intrinsic metallic character. At a coverage of 1 monolayer (ML), the most stable G1 configuration achieves an adsorption energy of −1.54 eV per CH₂O molecule. Within a 2 × 1 supercell, formaldehyde adopts both associative and dissociative adsorption modes. At a lower coverage, formaldehyde forms a stable bidentate structure through dual C–O and Zn–O bonding interactions. Electronic structure analysis reveals significant orbital hybridization and interfacial charge redistribution upon adsorption. Notably, associative adsorption opens a bandgap of 0.04 eV at the Fermi level, inducing a metal-to-semiconductor transition. In contrast, dissociative adsorption results in pronounced n-type doping, thereby elucidating the microscopic origin of the resistivity decrease observed in ZnO-based sensors. Overall, this work highlights the structural advantages of high-index facets and demonstrates for the first time the superior formaldehyde adsorption capability of the ZnO $\left[20\overline{2}1\right]$ facet, providing robust theoretical guidance for the rational design of next-generation, high-performance gas-sensing materials. Full article

Efficient monitoring of pork freshness is essential to minimize spoilage-related losses in the meat industry. To address the limitations of existing detection technologies, namely high cost, poor timeliness and high environmental sensitivity, this study developed a novel electronic nose system integrating a hybrid sensor array with dynamic gas path control. By combining metal oxide semiconductor (MOS) and electrochemical sensors (e.g., MQ137, MQ136), the system exhibits high sensitivity to the key volatile organic compounds (VOCs) released during pork spoilage, achieving a detection accuracy of over 90% in identifying spoilage stages. Combined with a dual-mode gas circuit design (solenoid valve switching time: 0.85 s), the reliability of the system was further demonstrated. This technology offers an economical and efficient real-time monitoring solution for slaughterhouses and cold chain logistics, providing a new low-cost scientific approach for pork freshness assessment. Full article

Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the average headspace concentration but in the temporal shape of volatile organic compound (VOC) release under controlled heating. Conventional electronic noses obscure this signal: they rely on multi-sensor arrays, compress each response into summary statistics, and report accuracy only at the level of individual measurements. Whether a single low-cost metal–oxide–semiconductor (MOS) gas sensor can recover grade-defining aroma chemistry, and whether waveform-level modeling can exploit it, was therefore investigated. A portable electronic nose built around a Bosch BME688 sensor recorded 90 time series, each comprising four directly measured channels (temperature, humidity, pressure, gas sensor resistance) and a derived indoor-air-quality (IAQ) proxy computed from them by the on-chip BSEC library, from 16 commercial Turkish black teas across three quality grades. Two representations were compared on the same data: a feature-based pipeline reducing 25 statistical descriptors to seven principal components for six classifiers (best F1-macro = 0.624, MLP), and a raw-waveform Multi-Scale 1D-CNN with Squeeze–Excitation and temporal self-attention (MS-CNN-Attention). Under product-grouped cross-validation, the deep model reached F1-macro = 0.811 (+30%) and graded 14 of 16 products correctly by majority vote, against 11 of 16 for the MLP, with the largest gain in the medium grade (F1: 0.52 → 0.79), where summary-statistic compression destroys the release-kinetic signal. The contributions are threefold: one programmable MOS sensor operated as a thermal-desorption profiler rather than a sensor array; a direct comparison of feature-based classical learning against raw-waveform deep learning on the same small, non-normally distributed dataset; and a product-level decision-consistency metric suited to batch screening. Pairing a low-cost MOS sensor with waveform-level modeling offers a rapid, non-destructive route to aroma-chemistry-based tea quality screening. Full article

Metal oxide semiconductor (MOS) gas sensors are an important part of electronic nose technology because they are sensitive, cheap, and work well with microfabrication for system integration. But sensor drift makes them less useful for long-term, continuous gas monitoring. Changes in how sensors respond over time make pattern recognition models that were trained at first less accurate. This review looks at new ways to deal with sensor drift, with a focus on transfer learning and deep learning methods that have been developing continuously in the last five years. It emphasizes the shift from conventional recalibration and component correction to sophisticated methodologies, including deep domain adaptation, contrastive representation learning, and attention-based models. The review does not just list these methods; it also analyzes their pros and downsides, especially in situations where there is not much labeled data, drift is hard to anticipate, or the computational resources are limited, which is often the case with edge sensors. Full article

Real-time online monitoring of key parameters such as the ammonium nitrogen (NH₄⁺) concentration during biological fermentation is important for the optimization of a biological fermentation process. Traditional offline detection technologies like spectrophotometry need contact sampling, with drawbacks of monitoring lag, risk of contamination, etc. In this work, taking the gentamicin fermentation process as an example, we developed an intelligent electronic nose non-contact monitoring system on the basis of fermentation off-gas signals. We captured the typical signals of off-gas and established a quantitative relationship between the signals and the NH₄⁺ concentration in fermentation broth; the system then realized non-contact real-time monitoring. The whole system consists of a gas-switching module, a sensor array module, a signal-processing module, and an intelligent prediction module. The system adopts a five-phase gas switching strategy to suppress sensor drift in metal–oxide–semiconductor (MOS) sensors and a light neural network for prediction, which improve both prediction speed and accuracy. Experiments were conducted using a 5 L fermenter, and the prediction result was consistent with the offline measured value (coefficient of determination, R² = 0.9871, root mean square error, RMSE = 0.0317 g/L). This technique provides a new method for the non-contact measurement of key fermentation parameters, and it can be expanded to other fermentations. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Resource-constrained edge processors deployed on unmanned aerial vehicles and wearable platforms require compact, drift-robust gas classification models for a range of environmental and security monitoring applications, including CBRN-motivated scenarios. Existing approaches rely on server-grade architectures incompatible with edge-board-scale deployment, or on classifiers that chemically degrade severely under long-term sensor drift. Each UCI gas class was mapped to a CBRN behavioral category based on physicochemical analogy (molecular functional group, vapor pressure, and metal-oxide semiconductor (MOS) cross-sensitivity pattern), following established precedent. Analyzed were Ammonia (NH₃), Acetaldehyde (CH₃CHO), Acetone ((CH₃)₂CO), Ethylene (C₂H₄), Ethanol (C₂H₅OH), Toluene (C₆H₅CH₃). We propose herein an end-to-end pipeline integrating a novel 1-D convolutional neural network with depth-wise separable convolutions (LiteSensor-Net), INT8 post-training quantization, structured magnitude pruning, and a knowledge-distillation domain-adaptation module (KD–DM) for sensor drift compensation. Using the UCI Gas Sensor Array Drift Dataset (13,910 measurements; 16 metal-oxide sensors; six analyte gases; a 36-month work span). LiteSensor-Net achieved accuracy = 92.63 ± 2.02%, macro-F1 = 0.898, model size = 5.99 kB INT8 pruned, inference latency = 6.3 ms, RAM footprint = 31.7 kB, and energy per inference = 0.04 mJ (all metrics on Raspberry Pi 4B, ARM Cortex-A72). Under chronological forward-chaining evaluation, KD–DM–20 achieved 47.91 ± 18.79% mean accuracy over Batches 2–10, representing a +9.25 pp improvement over uncompensated NC (38.66%). A six-metric benchmark framework—accuracy, macro-F1, model size, inference latency, RAM footprint, and energy per inference—is introduced to standardize edge-AI gas classifier evaluation. The proposed pipeline provides an open-source, deployable foundation for edge-class gas classification systems, with CBRN detection as a motivating application. Full operational validation on certified chemical simulants remains as future work. Full article