Electronic Nose System Research Articles

Multipurpose fluorescent carbon dots from papaya seed waste as sensing materials for Cu2+ detection and diethyl ether vapor sensor via electronic nose system

Herein, carbon dots (CDs) as biocompatible, fluorescent carbon-based nanomaterials were synthesized from papaya seed waste as renewable carbon sources for the first time via a facile acid pyrolysis method. The papaya seed-derived CDs showed blue fluorescence emission under UV light (365 nm) with a quantum yield of 2.74%, and contained oxygen-, and nitrogen- containing functional groups. Due to their surface functionality, the CDs have a great potential for using as fluorescence sensing probe in metal ion sensing application. The CD solution exhibited the most selective detection to Cu2+ as presented the highest fluorescence quenching with the limit of detection (LOD) of 5.16 μM. The CD-paper-based fluorescent sensor was also developed for practical application, and the RGB value was used to compare the sensitivity of CDs toward metal ions. The CD sensing film was also prepared for diethyl ether vapor sensing via optical electronic nose system. The principal component analysis (PCA) score plots revealed the total variance of 99.3%, indicating that the CDs can be used to discriminate different concentrations of diethyl ether/ethanol vapor mixtures. This work demonstrated that the papaya seed-derived CDs have a great attention to be alternative materials for developing sensing materials in both solution and film forms.

Advancements in Gas Recognition Techniques for Electronic Nose Systems: A Comparative Review of Classical Methods and Spiking Neural Networks

An electronic nose (E-nose) system's ability to recognize multivariate responses from gas sensors in a variety of applications necessitates gas recognition. Principal component analysis (PCA) and other traditional gas recognition methods have been widely used in E-nose systems for decades. ANNs have transformed the field of E- nose, particularly spiking neural networks (SNNs), significantly in recent years. In this paper, we compare and contrast recent E-nose gas recognition techniques in terms of algorithms and hardware implementations. Each classical gas recognition method has a relatively fixed framework and few parameters, making it easy to design. It works well with few gas samples but poorly with multiple gas recognition when noise is present. Keywords: Gas detection, electronic nose, artificial neural network, and spiking neural network.

A Novel Approach for Apple Freshness Prediction Based on Gas Sensor Array and Optimized Neural Network.

Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing.

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques.

A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.

MEMS-based electronic nose system for measurement of industrial gases with drift correction methodology

This work explores the use of microelectromechanical system (MEMS) sensors in electronic nose systems for the measurement of prominent industrial pollutants and proposes a methodology for the correction of drift induced in sensors during prolonged use for measurement of corrosive compounds. MEMS sensors have the advantages of low recovery and response time, lower power consumption and improved sensitivity. An electronic nose system is developed to quantify the four major pollutants emitted from various process industries, i.e. hydrogen sulfide (H2S), carbon monoxide (CO), ammonia (NH3) and ethanol (C2H5OH). All four pollutants represent major environmental and human health hazards and rapid quantification is vital. A drift correction algorithm was developed using experimental design and robust regression to correct the drift induced in the sensors. The experiments consisted of eight experimental sets with mixtures of H2S, CO, NH3 and C2H5OH measured on the same sensor array at the start and after a gap of 1 year. The methodology consisted of mapping the response of sensors after 1 year compared with that observed at the very start (day 1) of the experiment. The results showed successful implementation of the methodology, with the root mean square error values being significantly reduced at 0.235, 0.354, 0.145 and 0.651, respectively, for the four studied odorants (H2S, NH3, CO, and C2H5OH).

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors.

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

Insight into the role of lactic acid bacteria in the development of a novel fermented pistachio (Pistacia vera L.) beverage

This study aimed to assess the fermentability of a pistachio-based beverage. The effects of lactic acid fermentation on the odor profile and the stability of the beverage were also assessed. To this end, a procedure for obtaining a pistachio beverage was developed, and the nutritional composition of the beverage was obtained. The beverage was characterized by a high content of fiber (2.9%), fat (4.6%), and protein (2%), and contained high amounts of K and Mg. For the experiment, 43 cultures of the following lactic acid bacteria were used: Companilactibacillus paralimentarius, Companilactibacillus kimchi, Levilactibacillus brevis, Lactiplantibacillu pentosus, Lactiplantibacillus plantarum, Lactiplantibacillus paraplantarum, Latilactobacillus curvatus, Leuconostoc pseudomesenteroides, and Furfurilactobacillus rossiae. The cultures were evaluated for the following technological characteristics: i) growth and acidifying activity, ii) production of exopolysaccharides; iii) presence of histidine decarboxylase (hdcA) gene; iv) antimicrobial activity against Listeria innocua. The cultures were used in model pistachio-based fermentations to assess their ability to grow and acidify. The odor profile of the beverage after 24 h fermentation was assessed using an electronic nose system (E-nose). The pistachio-based beverage resulted an optimal substrate for lactic acid bacteria that reached loads between 8 and 10 log CFU/mL and acidified the beverage to pH values between 4 and 5.5. In the beverage, viable counts of all cultures remained above 8 log CFU/mL after 30 days of storage at 4 °C. The E-nose analysis represented a valid screening method to evaluate the effects of the fermentative activity of microbial starters on the qualitative characteristics of this novel food matrix.

IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection.

Food spoilage is a major concern in the food industry, especially for highly perishable foods such as beef. In this paper, we present a versatile Internet of Things (IoT)-enabled electronic nose system to monitor food quality by evaluating the concentrations of volatile organic compounds (VOCs). The IoT system consists mainly of an electronic nose, temperature/humidity sensors, and an ESP32-S3 microcontroller to send the sensors' data to the server. The electronic nose consists of a carbon dioxide gas sensor, an ammonia gas sensor, and an ethylene gas sensor. This paper's primary focus is to use the system for identifying beef spoilage. Hence, the system performance was examined on four beef samples stored at different temperatures: two at 4 °C and two at 21 °C. Microbial population quantifications of aerobic bacteria, Lactic Acid Bacteria (LAB), and Pseudomonas spp., in addition to pH measurements, were conducted to evaluate the beef quality during a period of 7 days to identify the VOCs concentrations that are associated with raw beef spoilage. The spoilage concentrations that were identified using the carbon dioxide, ammonia, and ethylene sensors were 552 ppm-4751 ppm, 6 ppm-8 ppm, and 18.4 ppm-21.1 ppm, respectively, as determined using a 500 mL gas sensing chamber. Statistical analysis was conducted to correlate the bacterial growth with the VOCs production, where it was found that aerobic bacteria and Pseudomonas spp. are responsible for most of the VOCs production in raw beef.

A selective feature optimized multi-sensor based e-nose system detecting illegal drugs validated in diverse laboratory conditions

Detecting illegal drugs, such as cannabis and methamphetamine, with high accuracy and speed is a complex problem that requires an innovative solution. To address this challenge, we propose a new method that utilizes a newly developed electronic nose (e-nose) system with an unprecedented total of 56 sensors, including four different types: metal-oxide-semiconductor (MOS), electrochemical (EC), non-dispersive infrared (NDIR), and photoionization detector (PID). Previous studies on gas sensors have typically validated results in a single controlled laboratory condition. In contrast, our study evaluated the performance of our system in different environments from the original training setting. To evaluate the detection performance of our system in unfamiliar environments and its robustness, we diluted the drug gas with normal air from six different laboratory environments. In addition, we evaluated the detection accuracy of our method using forward-feature selection, which allowed us to evaluate the impact of different combinations of sensors. By selectively optimizing sensors based on their ability to capture unique features of different drugs, our proposed method reduced the number of optimal sensors to less than half of 56 (24 selected). The proposed method achieved a detection accuracy of 93.03% and reduced the error rate from 12.23% to 6.97% using 5166 datasets including cannabis, methamphetamine, and tobacco. Our research not only provides rapid and enhanced accuracy, but also has the potential to be an effective tool for detecting illegal drugs in various settings, which could greatly contribute to strengthening national and social security.

Array Optimization Based on Weighted and Hilbert–Schmidt Schemes of Multisensor Detection System

This paper presents a novel sensor array optimization scheme for multisensor electronic nose detection system. A system architecture with multisensor is first proposed to implement the medical detection, including the bacterial culture medium detection and animal wound infection detection. The system efficiency is evaluated by comparing with the field asymmetric ion mobility spectrometry (FAIMS) system. To further improve the detection effect and reduce the number of sensors of the electronic nose system, we then derive two sensor array optimization procedures based on factor analysis and Hilbert-Schmidt independence criterion, respectively. Specifically, the weighted factor analysis method and non-weighted factor analysis method are proposed via factor analysis. Besides, the Hilbert-Schmidt independence criterion optimization design of linear kernel function and Gaussian kernel function are also exploited. The experiment results highlight that compared with the existing approaches, the proposed weighted factor analysis optimization method and Hilbert-Schmidt independence criterion optimization method (Gaussian kernel function) can achieve a significant system performance.

Application of Electronic Nose and Eye Systems for Detection of Adulteration in Olive Oil based on Chemometrics and Optimization Approaches

In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses. 

A Residual Dense Lightweight Group Convolution Neural Network for Identifying the Gas Information of Different Levels of Tea

In the tea market, it is common for low-quality tea to cheat consumers by pretending to be high-quality. This work proposes a fast and nondestructive tea quality detection method, which combines an electronic nose (e-nose) system with an adaptive gas information recognition method. First, based on a PEN3 e-nose system, the gas information of different levels of tea is obtained. Second, a lightweight group convolution (LGC) module is proposed to adaptively focus on the important features that affect the classification performance of gas information and reduce the number of parameters. Finally, residual dense block (RDB) is introduced to fuse the shallow and deep features to avoid feature degradation, and residual LGC neural network (RLGCNet) is designed to recognize the different levels of tea gas information effectively. In the comparison results of the ablation study and multiclassification model, RLGCNet obtained the best classification accuracy of 98.50%, the precision of 98.49%, and the recall of 97.69%. In conclusion, the theoretical research results provide an effective detection method for the quality supervision of the tea market.

Advanced Algorithms for Low Dimensional Metal Oxides-Based Electronic Nose Application: A Review

Low-dimensional metal oxides-based electronic noses have been applied in various fields, such as food quality, environmental assessment, coal mine risk prediction, and disease diagnosis. However, the applications of these electronic noses are limited for conditions such as precise safety monitoring because electronic nose systems have problems such as poor recognition ability of mixed gas signals and sensor drift caused by environmental factors. Advanced algorithms, including classical gas recognition algorithms and neural network-based algorithms, can be good solutions for the key problems. Classical gas recognition methods, such as support vector machines, have been widely applied in electronic nose systems in the past. These methods can provide satisfactory results if the features are selected properly and the types of mixed gas are under five. In many situations, this can be challenging due to the drift of sensor signals. In recent years, neural networks have undergone revolutionary changes in the field of electronic noses, especially convolutional neural networks and recurrent neural networks. This paper reviews the principles and performances of typical gas recognition methods of the electronic nose up to now and compares and analyzes the classical gas recognition methods and the neural network-based gas recognition methods. This work can provide guidance for research in related fields.

Research on electronic nose for compound malodor recognition combined with artificial neural network and linear discriminant analysis

The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods.

Tomato storage quality predicting method based on portable electronic nose system combined with WOA-SVM model

Tomato storage quality predicting method based on portable electronic nose system combined with WOA-SVM model

A CNN-Based E-Nose Using Time Series Features for Food Freshness Classification

In this article, a convolutional neural network (CNN)-based electronic nose system is proposed, which utilizes time series features to classify the freshness of food. The electronic nose is composed of three elements: a highly sensitive miniaturized micro-electromechanical systems (MEMS) metal–oxide (MOX)–semiconductor gas sensor array, a complementary metal–oxide–semiconductor (CMOS) integrated circuit, and a CNN-based classification unit. The gas sensors exhibit exceptional sensitivity, low power consumption, and a small size. The interface circuit for sensor readout is fabricated using a standard 180-nm CMOS process, with an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.5\times1.5$ </tex-math></inline-formula> mm. The interface chip mainly consists of an analog-to-digital converter (ADC), an electrically erasable programmable read-only memory (EEPROM), an oscillator (OSC), power management, a heater driver, and a digital control circuit. Fixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}{(}{t}{)}$ </tex-math></inline-formula> exposures are taken at specific intervals under gas input and output conditions. Time series features are extracted from a diverse set of sensor signals, which allows the subtle differences in various food odors at different freshness levels to be identified. The incorporation of time series feature extraction extends the data features, resulting in enhanced classification accuracy with a limited number of sensors. An abstract odor map input to the CNN is formed by combining the steady-state and transient information of the gas sensor response. The final freshness classification accuracy of 20 types of foods is 97.3%. The accuracy is improved by 6.5% after the implementation of time series feature extraction, demonstrating the significance of instantaneous fluctuation information for classification accuracy.

A Two-Step E-Nose System for Vehicle Drunk Driving Rapid Detection

With the rapid development of shared cars, to reduce the phenomenon of drunk driving in shared cars, we have studied the onboard drunk driving rapid detection electronic nose system suitable for shared cars. To accurately judge whether the driver is drunk while driving in the presence of interfering gases such as passenger exhalation and the volatile smell containing alcohol, this paper proposes a two-step drunk driving detection frame for shared cars that first judges whether someone in the car is drunk and then judges whether the driver is drunk. To reduce the cost and volume of the electronic nose, the sensor array was optimized based on the random forest algorithm. To find the optimal sampling time, we processed the original data by time slicing. Finally, using the two-step framework proposed by us, the accuracy of the first step and the second step of driver drunk driving detection reached 99.44% and 100%, respectively, with a sampling time of 5 s. After algorithm optimization, only 9 of the 21 sensors were left. This paper presents a practical electronic nose system for the detection of drunk driving in shared cars.

Detection of Counterfeit Perfumes by Using GC-MS Technique and Electronic Nose System Combined with Chemometric Tools

The Scientific Committee on Cosmetic and Non-Food Products has identified 26 compounds that may cause contact allergy in consumers when present in concentrations above certain legal thresholds in a product. Twenty-four of these compounds are volatiles and can be analyzed by gas chromatography-mass spectrometry (GC-MS) or electronic nose (e-nose) technologies. This manuscript first describes the use of the GC-MS approach to identify the main volatile compounds present in the original perfumes and their counterfeit samples. The second part of this work focusses on the ability of an e-nose system to discriminate between the original fragrances and their counterfeits. The analyses were carried out using the headspace of the aqueous solutions. GC-MS analysis revealed the identification of 10 allergens in the perfume samples, some of which were only found in the imitated fragrances. The e-nose system achieved a fair discrimination between most of the fragrances analyzed, with the counterfeit fragrances being clearly separated from the original perfumes. It is shown that associating the e-nose system to the appropriate classifier successfully solved the classification task. With Principal Component Analysis (PCA), the three first principal components represented 98.09% of the information in the database.

Inkjet-printed ZnO-based MEMS sensor array combined with feature selection algorithm for VOCs gas analysis

An electronic nose system can enhance the selectivity of semiconductor gas-sensor systems but increase system power consumption and signal data processing complexity. Herein, we developed a gas sensor array composed of six ZnO-based micro-electro-mechanical systems (MEMS) sensors prepared by inkjet printing sensing materials on a micro-hotplate. Its power consumption decreased to 36 mW. Furthermore, the particle swarm optimization (PSO) algorithm was used to select the optimal features, decreasing the total of 72 features to 5. Finally, support vector machine and artificial neural network models based on the optimal 5 features were performed to recognize and quantify volatile organic compounds (VOCs), including formaldehyde, ethanol, toluene, and xylene. The identification accuracy and R2 reach 97.9 % and 0.975, respectively. The result demonstrates that the MEMS sensor array, combined with PSO and pattern recognition algorithms, is a promising approach to accurately identifying and quantifying VOCs.

Non-Destructive Detection of Chicken Freshness Based on Electronic Nose Technology and Transfer Learning

As a non-destructive detection method, an electronic nose can be used to assess the freshness of meats by collecting and analyzing their odor information. Deep learning can automatically extract features and uncover potential patterns in data, minimizing the influence of subjective factors such as selecting features artificially. A transfer-learning-based model was proposed for the electronic nose to detect the freshness of chicken breasts in this study. First, a 3D-printed electronic nose system is used to collect the odor data from chicken breast samples stored at 4 °C for 1–7 d. Then, three conversion to images methods are used to feed the recorded time series data into the convolutional neural network. Finally, the pre-trained AlexNet, GoogLeNet, and ResNet models are retrained in the last three layers while being compared to classic machine learning methods such as K Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machines (SVM). The final accuracy of ResNet is 99.70%, which is higher than the 94.33% correct rate of the popular machine learning model SVM. Therefore, the electronic nose combined with conversion to images shows great potential for using deep transfer learning methods for chicken freshness classification.