E-nose System Research Articles

An electronic nose for the detection of Sarin, Soman and Tabun mimics and interfering agents

An electronic nose system (E-nose) with metal oxide semiconductor sensors (MOS) has been designed to discriminate and quantify different chemical warfare agents (CWA) mimics. The E-nose consists of an array of commercial MOS for different gases, two sensors for temperature sensing, a sample handling system, a data acquisition system and a laptop with the data acquisition system control. With this device, discrimination studies have been carried out to detect specific CWA simulants (diethyl chlorophosphonate (DCP), diethyl cyanophosphate (DCNP), diisipropyl fluoride (DFP)), their derivatives (diethyl 1-phenylethyl phosphonate (OP-1), diethyl (2- cyanoethyl)phosphonate (OP-2), dimethyl methyl phosphonate (OP-3) and diethyl (2-oxopropyl)phosphonate (OP-4)) and some potential interfering substances (sulfuric acid, ammonia, ethanol and acetone). Principal components analyses (PCA) show that it is possible to discriminate the studied organophosphorous CWA mimics (DCP, DCNP and DFP) from the other studied derivatives and potential interfering agents. In addition, DCNP quantification studies have been done by using partial least squares (PLS) and a mathematical model has been obtained to predict DCNP concentrations in air. In this model, the coefficient of determination (R2) is 0.9567, the root mean square error of prediction (RMSEP) is 30 and the limit of detection (LOD) is 5ppm so the model is considered valid. These results suggest that this E-nose system is capable to discriminate and quantify CWA mimics and it would be a feasible system to be used in a real scenario.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification.

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Mass spectrometry-based electric nose system for assessing rice quality during storage at different temperatures

A mass spectrometry-based electronic nose (MS e-nose) was used to measure changes in rice quality during storage at different storage conditions. Rice was stored for 4 months at four different temperatures (0 °C, 20 °C, 30 °C, and 40 °C) and tested for fat acidity, sensory characteristics, and flavor pattern analysis using the MS e-nose. When the rice was stored for long durations at higher temperatures, fat acidity increased and sensory quality was low. Flavor volatile profiles of the rice determined by MS e-nose revealed a tendency for the results to separate into three groups (months 1 + 2, 3, and 4). Volatile profile changes in rice during storage depended on the storage time, regardless of storage temperature. It is likely that the fat acidity and sensory evaluation results, which were related, could be distinguished by their volatile-producing metabolic activities. Accordingly, MS e-nose system was successfully used to screen and qualitatively evaluate stored rice.

A miniature electronic nose system based on an MWNT–polymer microsensor array and a low-power signal-processing chip

This article introduces a power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)-polymer based microsensor array, and a low-power signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 "walls," which restrained the material-solvent liquid in a defined area of 650 × 760μm(2). To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-power signal-processing chip included array data acquisition circuits and a signal-processing core. The MWNT-polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-power signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05mW of power at supply voltages of 1 and 1.8V for the array data acquisition circuits and the signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-power signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.

Regression Approach for Concentration Estimation of Volatile Chemical Compounds by E-nose Response Analysis

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.

Paddy and maize moldy status characterization using electronic nose

Paddy and maize moldy status rapid detection using electronic nose (E-nose) was investigated in this paper. An E-nose system with eight metal oxide semiconductor gas sensors was used to measure moldy status of paddy and maize samples at room temperature. Principal component analysis (PCA) and stochastic resonance (SR) are used for data processing. PCA results showed that E-nose could distinguish paddy and maize samples in different moldy status. Signal-to-noise ratio (SNR) spectrum calculated by SR discriminated paddy and maize samples. Moldy status predicting models were developed based on SNR maximums linear fitting regression. Validating experiments demonstrated that the developed models presented high detecting accuracy. The proposed method is promising in grain quality rapid analysis applications.

Bacteria Classification Using Electronic Nose for Diabetic Wound Monitoring

Array based gas sensor technology namely Electronic Nose (E-nose) now offers the potential of a rapid and robust analytical approach to odor measurement for medical use. Wounds become infected when a microorganism which is bacteria from the environment or patients body enters the open wound and multiply. The conventional method consumes more time to detect the bacteria growth. However, by using this E-Nose, the bacteria can be detected and classified according to their volatile organic compound (VOC) in shorter time. Readings were taken from headspace of samples by manually introducing the portable e-nose system into a special container that containing a volume of bacteria in suspension. The data will be processed by using statistical analysis which is Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods. The most common bacteria in diabetic foot are Staphylococcus aureus, Escherchia coli, Pseudomonas aeruginosa, and many more.

Algorithmic mitigation of sensor failure: Is sensor replacement really necessary?

Data-driven models built on metal oxide (MOX) gas sensor arrays are broadly used to detect and discriminate a wide variety of chemical substances. Usually, a system composed of gas sensors and data processing algorithms is initially trained under controlled conditions with the aim to make accurate predictions of the new samples acquired. However, MOX gas sensors undergo two major impairments in the forms of sensor failures and drift that deteriorate the predictions’ capabilities of the previously calibrated models. While a variety of algorithms has been proposed to cope with the sensor drift problem, sensor failures have received much less attention despite their recurrent appearance after a certain time of operation. In this paper we propose a novel methodology based on multiple kernels to increase the robustness of e-nose systems against sensor failures. We built various multi-kernel models using multiple subsets of sensors and analyzed their performance when increasing the number of faulty sensors. Using an 8-sensor array module exposed to six different gases, we show that our proposed multi-kernel approach significantly increases the robustness of the system. In particular, we estimate that the percentage of multi-kernels free of faulty sensors has to be of at least 50% to maintain the performance of the classifier stable. The main conclusion drawn from this analysis is that instead of identifying which specific sensor fails, which is a nontrivial computational task and can be just temporal, the most convenient way would be to design a more robust system that requires minimal human intervention. Since the multi-kernel strategy copes with the sensor failure by itself, we claim that the lifetime of chemo-sensory systems, particularly their constituent chemical sensor devices, can be extended before the replacement of any particular chemical sensor is required.

Practical discrimination of good and bad cooked food using metal oxide semiconductor odour sensor

An increasing concentration of ammonia in cooked food is in direct proportion to the extent of decay. This fact is used to design an electronic nose (e-nose) based on metal oxide semiconductor odour sensor circuit capable of discriminating good and bad cooked food. On the basis of the data produced by the e-nose circuit, a feedforward multilayer neural network is designed and trained to recognize varying concentrations of ammonia in the food. Test results of the prototype e-nose system show that it is capable of classifying cooked food as being good or bad with over 92% average success rate.

Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System

We present an effective portable e-nose system that performs well even in noisy environments. Considering the characteristics of the e-nose data, we use an image covariance matrix-based method for extracting discriminant features for vapor classification. To construct composite vectors, primitive variables of the data measured by a sensor array are rearranged. Then, composite features are extracted by utilizing the information about the statistical dependency among multiple primitive variables, and a classifier for vapor classification is designed with these composite features. Experimental results with different volatile organic compounds data show that the proposed system has better classification performance than other methods in a noisy environment.

Hybrid Optical-Electrochemical Electronic Nose System Based on Zn-Porphyrin and Multi-Walled Carbon Nanotube Composite

In this work, we have enhanced the capability of an e-nose system based on combined optical and electrochemical transduction within a single gas sensor array. The optical part of this e-nose is based on detection of the absorption changes of light emitted from eight light emitting diodes (LEDs) as measured by a CMOS photo-detector. The electrochemical part works by measuring the change in electrical resistivity of the sensing materials upon contact with the sample vapor. Zinc-5,10,15,20-tetra-phenyl-21H,23H-porphyrin (ZnTPP) and multi-walled carbon nanotube (MWCNT) composite was used as the sensing materials based on its good optoelectronic properties. This sensing layer was characterized by UV-Vis spectroscopy and atomic force microscope and tested with various VOC vapors. Density functional theory (DFT) calculations were performed to investigate the electronic properties and interaction energies between ZnTPP and analyte molecules. It can be clearly seen that this hybrid optical-electrochemical electronic nose system can classify the vapor of different volatile organic compounds.

Study of peach freshness predictive method based on electronic nose

An electronic nose (E-nose) technique based peach freshness predictive model is discussed in this paper. Peaches are measured by a self-developed E-nose system with eight metal oxide semiconductors gas sensor array. Principal component analysis (PCA) and stochastic resonance (SR) are used for measurement data analysis. Results show that the E-nose can distinguish peaches between fresh and stale conditions. Microbiology, peach firmness and contents of total soluble solids (TSS) indices are measured to determine the peach freshness. The primary volatile gases emitted by peaches are characterized by gas chromatography–mass spectrometry (GC–MS) method. Signal-to-noise ratio (SNR) spectrum of peach E-nose measurement data is calculated through SR. The peach freshness predicting model is developed based on SNR maximums (Max-SNR) linear fitting regression. Validating experiments results demonstrate that the predicting accuracy of this model is 85%. The method takes some advantages including easy operation, rapid detection, high accuracy, good repeatability, etc.

Effectiveness of electronic nose systems to detect bergamot (Citrus bergamia Risso et Poiteau) essential oil quality and genuineness

The quality of an essential oil is determined by its composition as well as by the complex aroma features. The aim of this work was to analyze the effectiveness of electronic nose (e-nose) systems to detect quality and genuineness of bergamot essential oils (BEOs). Tested methods included volatile analysis with the e-nose based on metal-oxide semiconductor sensors and analysis of chemical composition of essential oil by chromatography (GC/FID, GC/MS). The paper reports preliminary results on the potential of an e-nose to discriminate natural cold-pressed bergamot oil (BEO C-P) from those deterpenated and bergapten-free; between samples obtained from different cultivars; from samples obtained from fruit grown in the Protected Denomination of Origin (PDO) area and those outside the PDO; and finally from synthetic essential oils (SEO). This feasibility study has confirmed that an e-nose demonstrates the ability to supply a concrete support to BEO quality control. The e-noses, with subsequent DFA treatment of data, offer the advantages of rapidity and reliability. Further validation studies are currently under way with the aim of enriching and updating the databases of reference for BEOs. In order to broaden the range of e-nose use, tests on sensors chiral comportment are in progress, in order to bring this same approach in the study of adulteration of BEOs.

E-nose Development for Safety Monitoring Applications in Refinery Environment

The E-nose system reported is designed to address the problem of early and distributed detection of dangerous gas mixtures. It is made of a selection of Commercial Off-The-Shelf (COTS) sensors, facing a small volume chamber, whose signals are conditioned and sampled by a multifunction board connected to a personal computer. A program, implementing efficient Support Vector Machine and least square model algorithms, executes the gas classification, the concentration estimation and warns about set risk thresholds overcoming. The system training was performed in laboratory, over a wide range of concentrations in air of: methane, hexane, pentane, and hydrogen sulfide. Other boundary conditions, such as oxygen concentration, temperature and RH are also taken into account. The overall cost of the system can be made very low, adopting an embedded architecture approach, allowing to overcome the limitations of the monitoring systems deployment inside refinery plants due to the high costs of traditional GC systems.

An Electronic Nose for Amine Detection Based on Polymer/SWNT-COOH Nanocomposite

An electronic nose (e-nose) system based on polymer/carboxylic-functionalized single-walled carbon nanotubes (SWNT-COOH) was developed for sensing various volatile amines. The SWNT-COOH dispersed in the matrix of different polymers; namely, polyvinyl chloride (PVC), cumene terminated polystyrene-co-maleic anhydride (cumene-PSMA), poly(styrenecomaleic acid) partial isobutyl/methyl mixed ester (PSE), and polyvinylpyrrolidon (PVP), were deposited on interdigitated gold electrodes to make the gas sensors. The response of these sensors to volatile amines was studied by both static and dynamic flow measurements. It was found that all sensors exhibited behaviors corresponding to Plateau-Bretano-Stevens law (R2 = 0.81 to 0.99) as the response to volatile amines. Real-world application was demonstrated by applying this e-nose to monitor the odor of sun-dried snakeskin gourami that was pre-processed by salting-preservation. This electronic nose can discriminate sun-dried fish odors with different stored days using a simple pattern recognition based on the principal component analysis (PCA).

A Low-Power Electronic Nose Signal-Processing Chip for a Portable Artificial Olfaction System

The bulkiness of current electronic nose (E-Nose) systems severely limits their portability. This study designed and fabricated an E-Nose signal-processing chip by using TSMC 0.18-μ m 1P6M complementary metal-oxide semiconductor technology to overcome the need to connect the device to a personal computer, which has traditionally been a major stumbling block in reducing the size of E-Nose systems. The proposed chip is based on a conductive polymer sensor array chip composed of multiwalled carbon nanotubes. The signal-processing chip comprises an interface circuit, an analog-to-digital converter, a memory module, and a microprocessor embedded with a pattern-recognition algorithm. Experimental results have verified the functionality of the proposed system, in which the E-Nose signal-processing chip successfully classified three odors, carbon tetrachloride (CCl4), chloroform (CHCl3), and 2-Butanone (MEK), demonstrating its potential for portable applications. The power consumption of this signal-processing chip was maintained at a very low 2.81 mW using a 1.8-V power supply, making it highly suitable for integration as an electronic nose system-on-chip.

Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

Electronic nose (E-nose) technique was attempted to discriminate green tea quality instead of human panel test in this work. Four grades of green tea, which were classified by the human panel test, were attempted in the experiment. First, the E-nose system with eight metal oxide semiconductors gas sensors array was developed for data acquisition; then, the characteristic variables were extracted from the responses of the sensors; next, the principal components (PCs), as the input of the discrimination model, were extracted by principal component analysis (PCA); finally, three different linear or nonlinear classification tools, which were K-nearest neighbors (KNN), artificial neural network (ANN) and support vector machine (SVM), were compared in developing the discrimination model. The number of PCs and other model parameters were optimized by cross-validation. Experimental results showed that the performance of SVM model was superior to other models. The optimum SVM model was achieved when 4 PCs were included. The back discrimination rate was equal to 100% in the training set, and predictive discrimination rate was equal to 95% in the prediction set, respectively. The overall results demonstrated that E-nose technique with SVM classification tool could be successfully used in discrimination of green tea's quality, and SVM algorithm shows its superiority in solution to classification of green tea's quality using E-nose data.

Express diagnostics based on selective gas analyzers and error analysis

Effective ENose system is realized with using firmware National Instruments LabVIEW.Such system contains sampler with set of selective gas sensors, amplifier-filters block, analog-digital converter and computer interface elements, which provide registration, recognition, visualization and documentation of diagnostic procedures. LabVIEW environment allows quickly calibrate the sensors channels,and if necessary to change the number andtypes of sensors that use different methods ofrecognition, just to rebuild the system to newtasks.

Electronic Nose Evaluation of the Effects of Canopy Side on Cabernet franc (Vitis viniferaL.) Grape and Wine Volatiles

The effect of grapevine canopy side (north versus south and east versus west) on grape and wine volatiles of Cabernet franc was evaluated during two growing seasons using two electronic nose systems based on conducting polymers and surface acoustic waves. Data from three sampling dates per season from both electronic noses were compared with physicochemistry and wine aroma sensory evaluations. Univariate and multivariate statistical analyses generally indicated grape physicochemistry indices could not differentiate consistently (p > 0.05) between canopy sides across growing seasons and sampling dates. Both electronic nose (ENose) systems provided complete discrimination of canopy sides for grapes and wine using canonical discriminant analysis. On average, the surface acoustic wave-based ENose explained 80% for the conducting polymer-based ENose. Wine aroma sensory evaluation differentiated canopy sides in three of four evaluations.

Data Refinement and Channel Selection for a Portable E-Nose System by the Use of Feature Feedback

We propose a data refinement and channel selection method for vapor classification in a portable e-nose system. For the robust e-nose system in a real environment, we propose to reduce the noise in the data measured by sensor arrays and distinguish the important part in the data by the use of feature feedback. Experimental results on different volatile organic compounds data show that the proposed data refinement method gives good clustering for different classes and improves the classification performance. Also, we design a new sensor array that consists only of the useful channels. For this purpose, each channel is evaluated by measuring its discriminative power based on the feature mask used in the data refinement. Through the experimental results, we show that the new sensor array improves both the classification rates and the efficiency in computation and data storage.