☆ 4.7 Review

Recognition Algorithms in E-Nose: A Review

IEEE SENSORS JOURNAL (2023)

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Article Chemistry, Analytical

A comprehensive gas recognition algorithm with label-free drift compensation based on domain adversarial network

Xiaofang Pan et al.

Summary: In order to address the performance issues of gas sensor systems in complex gas mixtures, we propose a comprehensive algorithm called HATN-DA. This algorithm utilizes multi-head transformers and channelwise attention mechanisms for feature extraction, and encodes gas species into concentration information using capsule vectors. It also achieves label-free drift compensation and transfer calibration effects through a domain discriminator.

SENSORS AND ACTUATORS B-CHEMICAL (2023)

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Article Agriculture, Multidisciplinary

Advances in gas sensors and electronic nose technologies for agricultural cycle applications

Thara Seesaard et al.

Summary: This review summarizes the utilization of gas sensors and E-nose technologies in agricultural best management practices, including cultivation preparation, crop production, harvesting, and storage. These technologies show great promise but also have limitations.

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Article Chemistry, Analytical

Monitoring hand hygiene with commercial gas sensors: A pattern recognition approach

S. Braun et al.

Summary: This study introduces a new method for monitoring hand hygiene compliance of healthcare workers using commercial Ethanol gas sensors. Different machine learning algorithms are applied to categorize hand disinfection techniques as correct or incorrect, with the stationary sensor setup showing significantly higher accuracy compared to the portable setup.

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Article Chemistry, Multidisciplinary

High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm

Mingu Kang et al.

Summary: Semiconductor metal oxide (SMO) gas sensors are considered as next-generation environmental monitoring sensors, but they have limitations in actual applications due to low selectivity. This study solves these problems by fabricating uniform gas sensor arrays and applying a deep learning algorithm, achieving real-time selective gas detection for various gases.

ACS SENSORS (2022)

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Article Materials Science, Multidisciplinary

Quantitatively Discriminating Alcohol Molecules by Thermally Modulating NiO-Based Sensor Arrays

Meng Li et al.

Summary: This study demonstrates that using a sensor array composed of NiO sensors for thermal modulation can improve the discrimination of different categories of gas molecules. The peak temperature of the temperature profile is crucial in distinguishing concentration differences induced by subtle variations in electrical responses.

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Article Food Science & Technology

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Changquan Huang et al.

Summary: In this study, a novel framework utilizing a one-dimensional convolutional neural network (1DCNN) and a random forest regressor (RFR) was proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. The effectiveness of this framework was verified and compared with other models, and the results showed that it performed best in the detection of meat adulteration.

FOODS (2022)

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Article Chemistry, Analytical

A low-cost and efficient electronic nose system for quantification of multiple indoor air contaminants utilizing HC and PLSR

Hongli Ma et al.

Summary: The novel quantification technique for electronic nose presented in this study, utilizing a double-step strategy combined with hierarchical classifier and partial least squares regression, demonstrates outstanding performance in identifying toxic gases and estimating concentrations. The approach is applicable for E-nose-based odor quantification.

SENSORS AND ACTUATORS B-CHEMICAL (2022)

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Article Chemistry, Analytical

An electronic nose based on carbon nanotube-titanium dioxide hybrid nanostructures for detection and discrimination of volatile organic compounds

Mostafa Shooshtari et al.

Summary: This paper presents a modification to carbon nanotubes (CNTs) to improve the sensitivity to volatile organic compounds (VOCs) and enhance the stability of CNT sensors. By growing titanium dioxide nanowires (TiO2-NW) on the surface of CNTs, the sensors show increased sensitivity and reduced response time. Using virtual arrays of an electronic nose (E-nose), new features are extracted from the data and classified using support vector machine (SVM) algorithms. The paper achieves high accuracy in classifying different VOC gases.

SENSORS AND ACTUATORS B-CHEMICAL (2022)

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Artificial Olfactory Neuron for an In-Sensor Neuromorphic Nose

Joon-Kyu Han et al.

Summary: A neuromorphic module of an electronic nose is demonstrated using a chemoresistive gas sensor and a single transistor neuron. It simultaneously detects gases and encodes spike signals, mimicking the biological olfactory system. An analysis of the mixed signals using a spiking neural network allows the identification of odor sources. This approach eliminates the need for conversion circuits and reduces power consumption compared to traditional electronic noses.

ADVANCED SCIENCE (2022)

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Article Engineering, Electrical & Electronic

Classification and Concentration Prediction of VOCs With High Accuracy Based on an Electronic Nose Using an ELM-ELM Integrated Algorithm

Tao Wang et al.

Summary: An electronic nose (E-nose) based on three metal oxide semiconductor (MOS) gas sensors is designed to quantitatively analyze six types of volatile organic compounds (VOCs). Support vector machine (SVM), extreme learning machine (ELM), and back-propagation (BP) neural network are used to design different models, which can predict the concentration of different types of VOCs. The integrated model based on ELM-ELM structure exhibits the best performance with high classification accuracy and regression score.

IEEE SENSORS JOURNAL (2022)

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Review Medicine, General & Internal

Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath A Systematic Review and Meta-analysis

Max H. M. C. Scheepers et al.

Summary: This study provides an overview of the diagnostic accuracy and methodological challenges of using electronic noses (e-noses) for cancer detection in exhaled breath. Despite high diagnostic accuracy, most studies were feasibility studies with small sample sizes, lack of standardization, and a high risk of bias, highlighting the need for further research to address standardization and reproducibility in e-nose studies.

JAMA NETWORK OPEN (2022)

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Article Chemistry, Multidisciplinary

Modular Assembly of MXene Frameworks for Noninvasive Disease Diagnosis via Urinary Volatiles

Xuyin Ding et al.

Summary: In this study, a coordination-driven modular assembly strategy was used to develop a library of gas-sensing materials based on porous MXene frameworks (MFs). A high-discriminative MHMF e-nose was assembled using laser-induced graphene interdigital electrodes array and microchamber. By utilizing the MHMF e-nose as a plug-and-play module, a wireless and real-time monitoring POCT platform for urinary volatiles was established, achieving noninvasive diagnosis of multiple diseases with a high accuracy of 91.7%.

ACS NANO (2022)

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Article Engineering, Environmental

Toward Point-of-Care chronic disease Management: Biomarker detection in exhaled breath using an E-Nose sensor based on rGO/SnO2 superstructures

Arunkumar Shanmugasundaram et al.

Summary: This paper discusses a high-performance formaldehyde sensor that incorporates three-dimensional reduced graphene oxide into tin dioxide nanospheres. The sensor exhibits excellent sensitivity and can effectively differentiate between healthy individuals and lung cancer patients based on formaldehyde levels in their breath. Therefore, this sensor has the potential to be used for lung cancer screening and indoor formaldehyde monitoring.

CHEMICAL ENGINEERING JOURNAL (2022)

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Article Chemistry, Multidisciplinary

Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose

Nyssa S. S. Capman et al.

Summary: Rapid detection of volatile organic compounds (VOCs) using graphene-based variable capacitor arrays has been demonstrated in this study. This versatile material allows for selective and rapid detection of multiple VOCs at different concentrations. Machine learning methods achieved high accuracy in classifying the VOCs.

ACS NANO (2022)

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Article Engineering, Electrical & Electronic

A Portable E-Nose Endowed With Subjective Evaluation Function of Air Quality in Vehicles

Xuan Deng et al.

Summary: A portable instrument based on an electronic nose was designed for subjective assessment of in-vehicle air quality. The instrument consists of three modules, and the measured odor fingerprints and ensemble learning method are used for subjective grading. This instrument can effectively evaluate the air quality in vehicles.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2022)

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Article Engineering, Electrical & Electronic

A Fast Pearson Graph Convolutional Network Combined With Electronic Nose to Identify the Origin of Rice

Yan Shi et al.

Summary: A fast Pearson graph convolutional network (FPGCN) was proposed to enhance the detection performance of e-nose and realize the origin tracking of rice, achieving a good classification result.

IEEE SENSORS JOURNAL (2021)

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Article Chemistry, Analytical

DWTLSTM for electronic nose signal processing in beef quality monitoring

Dedy Rahman Wijaya et al.

Summary: The proposed DWTLSTM method shows promising performance in monitoring beef quality, with high average accuracy and F-measure in beef quality classification task. It also demonstrates satisfactory performance in predicting microbial population, outperforming conventional methods such as k-NN, LDA, SVM/SVR, MLP, and LSTM.

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Article Chemistry, Analytical

Application of a lab-made electronic nose for extra virgin olive oils commercial classification according to the perceived fruitiness intensity

Guilherme G. Teixeira et al.

Summary: An electronic nose with nine metal oxide sensors was developed to classify and differentiate olive oils based on fruity intensity, successfully distinguishing standard solutions mimicking positive and negative attributes in a semi-quantitative manner. Multivariate models based on gas sensors were established to achieve high classification accuracy for leave-one-out cross validation. This E-nose device proved effective in accurately labeling the fruitiness intensity of olive oils, showcasing its potential as a non-destructive tool for quality assessment.

TALANTA (2021)

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Article Agriculture, Dairy & Animal Science

Application of E-nose technology combined with artificial neural network to predict total bacterial count in milk

Yongheng Yang et al.

Summary: Through the combination of E-nose technology and artificial neural network, this study proposes a method for rapid and accurate prediction of TBC in milk. The qualitative model achieved 100% accuracy, while the quantitative model demonstrated strong correlation with reference values, indicating reliable predictions.

JOURNAL OF DAIRY SCIENCE (2021)

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Article Engineering, Biomedical

A High performance electronic nose system for the recognition of myocardial infarction and coronary artery diseases

Bilge Han Tozlu et al.

Summary: Electronic noses are devices that can detect the presence of chemicals in odors through gas sensors. Research shows that using an electronic nose to analyze exhaled breath can accurately diagnose myocardial infarction and stable coronary artery disease.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

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Article Engineering, Electrical & Electronic

Hybrid Pattern Recognition for Rapid Explosive Sensing With Comprehensive Analysis

Vijay S. Palaparthy et al.

Summary: This paper introduces a hybrid pattern recognition with temperature compensation (HPR-TC) used within an E-Nose system, which has two modes of operation for rapid detection and comprehensive analysis in detecting explosives with high accuracy. The system utilizes a combination of different data analysis techniques, allowing for improved selectivity and detection accuracy of explosive compounds such as TNT, RDX, and PETN. The E-Nose system with HPR-TC can achieve high detection accuracy within a short time, with the temperature compensated analog PR providing detailed classification of explosives.

IEEE SENSORS JOURNAL (2021)

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Article Chemistry, Multidisciplinary

Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System

Hongyu Liu et al.

Summary: This study demonstrates a method of using temperature modulation of a single MOS sensor to recognize BTX molecules, by applying a rectangular heating waveform to activate the redox reaction at specific gas/oxide interfaces. Through signal preprocessing and data analysis methods, the features of BTX molecules can be extracted to improve recognition accuracy and stability.

ACS SENSORS (2021)

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Article Engineering, Biomedical

Estimation of Concentration Values of Different Gases Based on Long Short-Term Memory by Using Electronic Nose

Hande Bakiler et al.

Summary: An electronic nose is commonly used in different areas, with one important subject being the estimation of gas concentrations. This study aimed to increase the classification and regression successes of gas concentrations of four different gases detected by sensors. The proposed method of feature extraction using Fully Connected Layer of Long Short-Term Memory networks showed improvement in both classification and regression results.

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Mint treatment day prediction using a multi-sensors system and machine learning algorithms

Ali Amkor et al.

Summary: This study utilized a multi-sensors system and machine learning algorithms to predict the treatment day and monitor insecticides in mint successfully. Principal component analysis and k-nearest neighbours algorithm were employed for data processing and sample classification, while the NARX network was used for treatment day prediction, achieving excellent results.

SENSORS AND ACTUATORS A-PHYSICAL (2021)

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Article Chemistry, Analytical

Lung cancer detection via breath by electronic nose enhanced with a sparse group feature selection approach

Bei Liu et al.

Summary: This research aimed to differentiate lung cancer patients by analyzing VOCs through an E-nose platform, with 87 subjects enrolled for data collection. A sparse group feature selection method was applied to improve classification performance significantly, while statistical analysis showed that age and smoking habit had no significant influences on classification results.

SENSORS AND ACTUATORS B-CHEMICAL (2021)

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Article Engineering, Electrical & Electronic

High-Dimensional Time Series Feature Extraction for Low-Cost Machine Olfaction

Pratistha Shakya et al.

Summary: The complexity of airborne odors presents challenges and opportunities for chemical detection. By using the TruffleBot e-nose platform to collect multidimensional time series data, high-dimensional time series features can be extracted to identify subtle differences in odor concentration and composition. The application of time series features has shown great potential in improving classification accuracy and concentration estimation in various experiments.

IEEE SENSORS JOURNAL (2021)

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A survey on data-efficient algorithms in big data era

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Summary: This work investigates the issue of algorithms' data hungriness in Machine Learning, providing a comprehensive review of existing data-efficient methods categorized into four groups. These methods include using unsupervised algorithms, creating more data, transferring knowledge between domains, and modifying data-hungry algorithms. The interplay between these strategies is emphasized to explore more robust and data-efficient algorithms, while also discussing limitations, research challenges, and future opportunities in advancing data efficiency in machine learning.

JOURNAL OF BIG DATA (2021)

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Article Nanoscience & Nanotechnology

Constructing an E-Nose Using Metal-Ion-Induced Assembly of Graphene Oxide for Diagnosis of Lung Cancer via Exhaled Breath

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ACS APPLIED MATERIALS & INTERFACES (2020)

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Visualization of volatomic profiles for early detection of fungal infection on storage Jasmine brown rice using electronic nose coupled with chemometrics

Asada Jiarpinijnun et al.

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Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance Sensor Array

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Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid

Juan C. Rodriguez Gamboa et al.

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IEEE TRANSACTIONS ON ELECTRON DEVICES (2019)

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Efficient Solutions for Discreteness, Drift, and Disturbance (3D) in Electronic Olfaction

Lei Zhang et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2018)

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IEEE SENSORS JOURNAL (2017)

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Anti-drift in E-nose: A subspace projection approach with drift reduction

Lei Zhang et al.

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Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems

Lei Zhang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2015)

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Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring

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Dong Li et al.

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Alexander Vergara et al.

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Santiago Marco et al.

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Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine

Lei Zhang et al.

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Alexander Vergara et al.

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A bio-inspired two-layer multiple-walled carbon nanotube-polymer composite sensor array and a bio-inspired fast-adaptive readout circuit for a portable electronic nose

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Drift compensation of gas sensor array data by common principal component analysis

A. Ziyatdinov et al.

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Design of a Portable E-Nose Instrument for Gas Classifications

Ahmet Ozmen et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2009)

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On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario

S. De Vito et al.

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An electronic nose in the discrimination of patients with asthma and controls

Silvano Dragonieri et al.

JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY (2007)

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Characterization of Chinese vinegars by electronic nose

Qinyi Zhang et al.

SENSORS AND ACTUATORS B-CHEMICAL (2006)

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Data analysis for electronic nose systems

Simon M. Scott et al.

MICROCHIMICA ACTA (2006)

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EK Tang et al.

PATTERN RECOGNITION (2005)

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NATURE REVIEWS MICROBIOLOGY (2004)

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E Martinelli et al.

SENSORS AND ACTUATORS B-CHEMICAL (2003)

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Ricardo Gutierrez-Osuna

IEEE SENSORS JOURNAL (2002)

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