Application Of Electronic Nose Research Articles

Lightweight Memristive Neural Network for Gas Classification Based on Heterogeneous Strategy

The memristive neuromorphic computing system (MNCS) can complete related calculations with lower power consumption and higher speed, which has attracted widespread attention. However, due to the limitations of memristor and circuit, the realization of MNCS faces many challenges. In this paper, we propose a heterogeneous deployment strategy for the MNCS and construct a lightweight heterogeneous memristive gas classification neural network (LHM-GSNN) based on the electronic nose (e-nose) application. In addition, the model parameters are quantified by clustering strategy to adapt to the nonideal characteristics of memristor. The experimental results show that the complex structure in the model is visibly simplified, and the number of parameters is correspondingly reduced using the heterogeneous deployment strategy. Furthermore, we also analyze the power consumption of the LHM-GSNN model deployed to the MNCS. This work may provide new solutions for constructing and implementing the MNCS.

An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications.

Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance.

Accuracy of a new electronic nose for prostate cancer diagnosis in urine samples.

ObjectiveTo evaluate the accuracy of a new electronic nose to recognize prostate cancer in urine samples.MethodsA blind, prospective study on consecutive patients was designed. Overall, 174 subjects were included in the study: 88 (50.6%) in prostate cancer group, and 86 (49.4%) in control group. Electronic nose performance for prostate cancer was assessed using sensitivity and specificity. The diagnostic accuracy of electronic nose was reported as area under the receiver operating characteristic curve.ResultsThe electronic nose in the study population reached a sensitivity 85.2% (95% confidence interval 76.1–91.9; 13 false negatives out of 88), a specificity 79.1% (95% confidence interval 69.0–87.1; 18 false positives out of 86). The accuracy of the electronic nose represented as area under the receiver operating characteristic curve 0.821 (95% confidence interval 0.764–0.879).ConclusionsThe diagnostic accuracy of electronic nose for recognizing prostate cancer in urine samples is high, promising and susceptible to supplemental improvement. Additionally, further studies will be necessary to design a clinical trial to validate electronic nose application in diagnostic prostate cancer nomograms.

Application of Electronic Nose to Discriminate Species of Mold Strains in Synthetic Brines.

The chemical composition of the brine for Spanish-style table olives plays a crucial role during the fermentation process. Traditional laboratory analysis requires a high consumption of reagents, highly qualified personnel, sophisticated equipment, long analysis times, and large amounts of samples. Analysis carried out using an electronic nose (E-nose) offers an alternative, non-destructive technique and is useful in determining alterations in brines caused by microorganisms. In the present research, nine mold strains isolated from spoiled olives were inoculated in synthetic brines to determine the effect of microbial development on sensory quality, volatile profile, and the capacity of E-nose to discriminate altered brines from the healthy ones. The brines inoculated with the mold strains presented negative attributes related to aromas of mold, wood, leather, rancidity and, organic solvents among others. The highest intensity of defect was presented by the brines inoculated with the strains Galactomyces geotricum (G.G.2); three Penicillium expansum (P.E.3, P.E.4, and P.E.20); one Penicillium glabrum (P.G.19); three Aspergillus flavus (A.F.9, A.F.18, and A.F.21); and one Fusarium solani (F.S.11). A total of 19 volatile compounds were identified by gas chromatography. Sensory analysis allowed us to classify the synthetic brines based on the degree of alteration produced by the mold strains used. Also, the E-nose data were able to discriminate the inoculated brines regardless of the intensity of the defect. These results demonstrate the capacity of the E-nose to discriminate alterations in brines produced by molds, thereby making it a useful tool to be applied during the elaboration process to detect early alterations in table olive fermentation.

Application of Electronic Nose to Predict the Optimum Fermentation Time for Low-Country Sri Lankan Tea

The fermentation stage is vital during the black tea manufacturing process to produce the best-quality tea. The oxidation of tea biochemical compounds results in the appearance of characteristic smell peaks during the fermentation stage. These subtle changes in tea aroma are hard to detect unless one is a trained personnel. Here for the first time, we applied e-nose to monitor the fermentation process of Sri Lankan low-country tea. In this study, detection of smell peaks during fermentation was conducted by a custom-made e-nose (Digi-Nose) with four gas sensors. Singular value decomposition (SVD) is applied to eliminate the noise and dimensionality reduction in the sensor responses observed. The prediction of the time of appearance of smell peaks was conducted with a support-vector machine (SVM). Finally, theaflavin content with time was compared to validate the optimum fermentation times observed with an e-nose.

Surfactant-like Peptide Self-Assembled into Hybrid Nanostructures for Electronic Nose Applications.

An electronic nose (e-nose) utilizes a multisensor array, which relies on the vector contrast of combinatorial responses, to effectively discriminate between volatile organic compounds (VOCs). In recent years, hierarchical structures made of nonbiological materials have been used to achieve the required sensor diversity. With the advent of self-assembling peptides, the ability to tune nanostructuration, surprisingly, has not been exploited for sensor array diversification. In this work, a designer surfactant-like peptide sequence, CG7-NH2, is used to fabricate morphologically and physicochemically heterogeneous "biohybrid" surfaces on Au-covered chips. These multistructural sensing surfaces, containing immobilized hierarchical nanostructures surrounded by self-assembled monolayers, are used for the detection and discrimination of VOCs. Through a simple and judicious design process, involving changes in pH and water content of peptide solutions, a five-element biohybrid sensor array coupled with a gas-phase surface plasmon resonance imaging system is shown to achieve sufficient discriminatory capabilities for four VOCs. Moreover, the limit of detection of the multiarray system is bench-marked at <1 and 6 ppbv for hexanoic acid and phenol (esophago-gastric biomarkers), respectively. Finally, the humidity effects are characterized, identifying the dissociation rate constant as a robust descriptor for classification, further exemplifying their efficacy as biomaterials in the field of artificial olfaction.

Artificial Nociceptor Using Liquid Ionic Memory

AbstractThe incorporation of the different functions of a biological nervous system into electronic devices is challenging in the realization of humanoid robotics, which can recognize and respond in a manner that is similar to humans. Emerging synaptic devices that can process electronic signals by neuromorphic functions that operate in a similar manner to that of biological nervous systems. In this study, an acid‐sensitive artificial nociceptor device, which simultaneously detects acid concentration and generates synaptic signals, is developed for electronic nose application. The transition metal oxide (WO3)‐based device electrochemically performs neuromorphic sulfuric acid (H2SO4) sensing. The current–voltage curves demonstrate loop opening, with the change in the molarity of the acid, indicating memory storage ability and tunability of the developed nociceptor device.

Application of a Low-Cost Electronic Nose to Detect of Forest Tree Pathogens: Fusarium oxysporum and Phytophthora plurivora

The presence of forest tree pathogens may lead to substantial problems and their early detection in the stage of seeds storage or nurseries may be critical for the choice of appropriate management strategy. A new construction of a low-cost electronic nose was tested on the samples of pathogenic fungi and oomycetes of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fusarium oxysporum</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Phytopthora plurivora</i> . The electronic nose uses Figaro Inc. TGS series sensors with applied heater voltage modulation. Such a mode of electronic nose operation may be more appropriate for application for constant monitoring of seeds storage, when we compare it to the the method applying modulation of the gas concentration. A rectangular shape of the sensors’ heater voltage modulation pattern, with a shallow drop of the heater voltage from the nominal voltage, was proposed. Data visualization using the principal component analysis method and the random forest machine learning technique used to build classification models. The classification accuracy of 97% was obtained by a fusion of data collected by TGS 2610 and TGS 2602 sensors.

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known min−max approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy.

Rapid detection of pork and pig derivatives based on volatile compounds aroma using electronic nose

The development of food processing technology has increased the number and types of food products to meet human needs. The quality of a food product is not only seen from nutritional value and safety, but also from halalness aspect. For the purposes of quality control and halal authentication, the existence of a food testing method that is fast, accurate and easy to handling is needed. The application of electronic nose for various volatile compound detection purposes has been widely reported. This paper discusses electronic nose applications for quality control and halal testing purposes on food products. Furthermore, it also discussed the advantages and disadvantages of testing with an electronic nose. Based on the discussion, the electronic nose is a portable detection tool that is user friendly, fast and accurate for the identification and detection of food products based on the presence of the aroma of volatile compounds in the material. The advantages of this electronic nose have the potential to be used as an alternative rapid detection instrument in food testing, especially in authenticating pork and pig derivative.

Halal e-commerce in the muslim fashion sector as an effort to encourage the development of halal industry in Indonesia

The development of food processing technology has increased the number and types of food products to meet human needs. The quality of a food product is not only seen from nutritional value and safety, but also from halalness aspect. For the purposes of quality control and halal authentication, the existence of a food testing method that is fast, accurate and easy to handling is needed. The application of electronic nose for various volatile compound detection purposes has been widely reported. This paper discusses electronic nose applications for quality control and halal testing purposes on food products. Furthermore, it also discussed the advantages and disadvantages of testing with an electronic nose. Based on the discussion, the electronic nose is a portable detection tool that is user friendly, fast and accurate for the identification and detection of food products based on the presence of the aroma of volatile compounds in the material. The advantages of this electronic nose have the potential to be used as an alternative rapid detection instrument in food testing, especially in authenticating pork and pig derivative.

An Overview of Applications of Electronic Nose and Electronic Tongue in Food and Dairy Industry

The dairy and food industries aims to achieve targeted productivity and very keen on product quality and consumer demand. Due to increasing technological advances, these food and dairy industries gained numerous technological support. Among the diverse development, the usages of e-nose and e-tongue paved the way to handle the production without any kind of deviation. In the food industries product perfection, uniform taste and aroma plays a supreme role because these parameters determine the marketing strategy. The electronic nose (e-nose) is a non-destructive intelligent electronic sensing instrument, which mimics the human olfactory system to detect, discriminate and classify odour samples. e-nose and e-tongue is an electronic tool used as a fast screening method to provide information about the product quality which is not easily done by manual methods as it is a time-consuming process. In this paper, the principle of e-nose and e-tongue and their applications in the dairy and food industries are explained.

Detection of binary and ternary mixtures of volatile organic compounds using quartz tuning fork based sensor array

Polymer modified Quartz tuning fork based sensors are a versatile replacement for the conventional sensors available in the market for the development of sensor array for electronic nose applications. However, there is not enough work towards detection of mixture of analytes using these sensors. In this work, a sensor array is developed to detect mixture of styrene, propylbenzene and acetone. Based on the working principle of the sensor, parameters like recovery time, response time, and frequency shift, among others, are utilized for data classification of the sensor response. The data collected from the sensor is used to classify the several groups using ensemble classifier, where, support vector machines, k-nearest neighbour and artificial neural network participated as the voting algorithms. Initially, the classifier was able to make prediction with approximately 93% accuracy. After modification in the prediction process where classifier was trained in a progressive manner, in which it predicted only if the data point belonged to mixture or not and subsequently, if it belonged to binary or ternary mixture. This improved the overall accuracy to 98%. The work demonstrate that the combination of QTF sensor array and a suitable classifier can be used to detect binary and ternary mixtures and therefore, can be used for several applications of electronic nose.

Electronic-Nose as Non-destructive Tool to Discriminate "Ferrovia" Sweet Cherries Cold Stored in Air or Packed in High CO2 Modified Atmospheres.

This study aimed to explore the applicability of electronic-nose (E-nose) as a rapid method in discriminating samples of sweet cherry cv “Ferrovia” stored in high-CO2 (16% O2 + 20% CO2 + 64% N2) or air (control) up to 21 days. Projection to Latent Structures (PLS) methods applied to E-nose data showed that fresh fruit and the packaged or unpackaged samples can be distinguished, according to both the storage condition and the storage days. Moreover, a correlation analysis between E-nose sensors and 45 volatile compounds were overall, obtained from all the investigated sweet cherry samples by Headspace Solid-Phase Microextraction (HS SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). These methods allowed to associate samples with a specific flavour profile to one or more E-nose sensors. Finally, quality attributes (visual quality, colour, firmness, antioxidant activity, total phenols, and sugar content) were assessed during storage. Among these, visual quality and berry deformation resulted affected by storage conditions, showing that high-CO2 treatment better preserved the fruit quality than control.

Solution-Processed Chloroaluminum Phthalocyanine (ClAlPc) Ammonia Gas Sensor with Vertical Organic Porous Diodes.

In this research work, the gas sensing properties of halogenated chloroaluminum phthalocyanine (ClAlPc) thin films were studied at room temperature. We fabricated an air-stable ClAlPc gas sensor based on a vertical organic diode (VOD) with a porous top electrode by the solution process method. The surface morphology of the solution-processed ClAlPc thin film was examined by field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM). The proposed ClAlPc-based VOD sensor can detect ammonia (NH3) gas at the ppb level (100~1000 ppb) at room temperature. Additionally, the ClAlPc sensor was highly selective towards NH3 gas compared to other interfering gases (NO2, ACE, NO, H2S, and CO). In addition, the device lifetime was tested by storing the device at ambient conditions. The effect of relative humidity (RH) on the ClAlPc NH3 gas sensor was also explored. The aim of this study is to extend these findings on halogenated phthalocyanine-based materials to practical electronic nose applications in the future.

Identification of volatile organic compounds in the urine of patients with cervical cancer. Test concept for timely screening

The objective of this research was to identify a global chemical pattern of volatile organic compounds (VOCs) in urine capable of discriminating between women with cervical cancer (CC) and control women using an electronic nose and to elucidate potential biomarkers by gas chromatography-mass spectrometry (GC–MS). A cross-sectional study was performed, with 12 control women, 5 women with CIN (Cervical Intraepithelial Neoplasia) and 12 women with CC. Global VOCs in urine were assessed using an electronic nose and specific by GC–MS. Multivariate analysis was performed: Principal Component Analysis (PCA), Canonical Principal Coordinate Analysis (CAP) and Partial Least Squares Discriminant Analysis (PLS-DA) and the test's diagnostic power was evaluated through ROC (Receiver Operating Characteristic) curves. Results from the PCA between the control group compared to the CC present variability of 98.4% (PC1 = 93.9%, PC2 = 2.3% and PC3 = 2.1%). CAP model shows a separation between the overall VOCs profile of the control and CC group with a correct classification of 94.7%. PLS-DA indicated that 8 sensors have a higher contribution in the CC group. The sensitivity, specificity, value reached 91.6% (61.5%-99.7%) and 100% (73.5%-100%) respectively, according to the ROC curve. GC–MS analysis indicated that 33 compounds occur only in the CC group and some of them have been found in other types of cancer. In all, this study provides the basis for the development of an accessible, non-invasive, sensitive and specific screening platform for cervical cancer through the application of electronic nose and chemometric analysis.

Gas Sensor Array Using a Hybrid Structure Based on Zeolite and Oxide Semiconductors for Multiple Bio-Gas Detection.

Semiconductor-type gas sensors, composed of metal-oxide semiconductors and porous zeolite materials, are attractive devices for bio-gas detection, particularly when used as bio-gas sensors such as electronic nose application. Previous studies have shown such detection can be obtained with a separate gas concentrator and a sensor device using zeolites and oxide semiconductors of WO3 nanoparticles. By applying the gas concentrator, porous molecular structures alter both the gas sensitivity and the selectivity, and even can be used to define the sensor characteristics. Based on such a gas sensor design, we investigated the properties of an array of three sensors made of a layer of WO3 nanoparticles coated with zeolites with different interactions between gas molecule adsorption and desorption. The array was tested with four volatile organic compounds, each measured at different concentrations. The results confirm that the features of individual zeolites combined with the hybrid gas sensor behavior, along with the differences among the sensors, are sufficient for enabling the discrimination of volatile compounds when disregarding their concentration.

1D Metal Oxide Semiconductor Materials for Chemiresistive Gas Sensors: A Review

AbstractWhile numerous types of gas sensors have been developed for various industries and applications such as the automotive industry, environmental monitoring, and personal safety, nanoscale chemiresistive gas sensors have gained significant research interest due to several advantages such as high sensitivity, low power consumption, and portability. An essential component of these gas sensors is the sensing material where metal oxide semiconductor (MOS) materials are the most prevalent sensing material. Since the adoption of nanoscale synthesis methods for sensing materials development, such as electrospinning and hydrothermal synthesis, many novel 1D MOS‐based nanostructured sensing materials have been demonstrated to enhance gas sensing performance. Overall, nanoengineering approaches and mechanisms for enhancement of gas sensing performance of 1D metal oxide‐based sensing materials are systematically discussed and categorized into several overarching strategies, such as tuning of materials dimension, morphology, and composition. Furthermore, integration of 1D sensing nanomaterials into sensor devices are discussed from the perspective of different chemiresistive sensor architectures and device fabrication methods. Finally, this review also discusses use of 1D MOS materials for emerging and novel electronic nose applications.

Humidity compensation based on power-law response for MOS sensors to VOCs

• Convert temperature and relative humidity into one variable, namely absolute humidity to compensate the sensors. • Based on the law of power law response, a compensation model is established and the parameters can be calibrated. • The performance of this model is verified in experimental systems and application of electronic nose, respectively. The compensation model for humidity is proposed for Metal oxide semiconductor (MOS) sensors to respond to volatile organic compounds (VOCs) vapor. There are four frequently-used sensors were investigated for the detection of three typical VOCs, namely, acetone, ethanol and methanol. In this study, water vapor was treated as a reactant involved in the response, then a model is proposed based on the power law response. As the resistance of sensor and humidity enter the model, the detected gas concentration will be output. The effect of model is well verified on the experimental system. Even the model was applied to electronic nose to improve the recognition results. It indicates that our method is of great value to system that needs to remove the interference of water vapor.

Application of electronic nose with chemometrics methods to the detection of juices fraud

Today, consumer demands for fresh foods with desirable sensory properties are increasing. Lack of easy, cheap, and nondestructive methods to control the quality of fruit juices is one of the main challenges in the beverage industry. The present study has examined two types of natural and industrial juices. Machine olfaction with 9-metal oxide semiconductor (MOS) sensors was used for the experiments. Sensor response patterns were analyzed using chemometrics methods, including principal component analysis (PCA), linear and quadratic discriminant analyses (LDA and QDA), and support vector machine (SVM). According to the results, the classification accuracy of SVM, QDA, and LDA methods was 96.25%, 95.8%, and 94.4%, respectively. Practical applications It is important to note that most fruit juices sold in supermarkets contain only a small percentage of natural fruit juice and usually include added sweeteners. As a result, these juices lead to the intake of large amounts of calories without providing nutrients for the body. In addition, performing chemical tests to determine the quality of fruit juices are time-consuming, destructive, and costly and requires several glassware and reagents. E-nose could be used for real-time monitoring of the volatile components of the food to evaluate different features of the product. Generally, E-nose evaluates a mixture of smells released from a sample and is a reliable, nondestructive, cost-effective, and portable method with high feasibility and speed as well as simple use.