Development of a portable electronic nose system for the detection and classification of fruity odors

Exhaled Breath Analysis Using a Novel Electronic Nose for Different Respiratory Disease Entities

PURPOSE

Electronic noses (eNose) and gas chromatography mass spectrometry (GC-MS) are two important breath analysis approaches for differentiating between respiratory diseases. We evaluated the performance of a novel electronic nose for different respiratory diseases, and exhaled breath samples from patients were analyzed by GC-MS.

MATERIALS AND METHODS

Patients with lung cancer, pneumonia, structural lung diseases, and healthy controls were recruited (May 2019-July 2022). Exhaled breath samples were collected for eNose and GC-MS analysis. Breathprint features from eNose were analyzed using support vector machine model and leave-one-out cross-validation was performed.

RESULTS

A total of 263 participants (including 95 lung cancer, 59 pneumonia, 71 structural lung disease, and 38 healthy participants) were included. Three-dimensional linear discriminant analysis (LDA) showed a clear distribution of breathprints. The overall accuracy of eNose for four groups was 0.738 (194/263). The accuracy was 0.86 (61/71), 0.81 (77/95), 0.53 (31/59), and 0.66 (25/38) for structural lung disease, lung cancer, pneumonia, and control groups respectively. Pair-wise diagnostic performance comparison revealed excellent discriminant power (AUC: 1-0.813) among four groups. The best performance was between structural lung disease and healthy controls (AUC: 1), followed by lung cancer and structural lung disease (AUC: 0.958). Volatile organic compounds revealed a high individual occurrence rate of cyclohexanone and N,N-dimethylacetamide in pneumonic patients, ethyl acetate in structural lung disease, and 2,3,4-trimethylhexane in lung cancer patients.

CONCLUSIONS

Our study showed that the novel eNose effectively distinguishes respiratory diseases and holds potential as a point-of-care diagnostic tool, with GC-MS identifying candidate VOC biomarkers.

Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends

An electronic nose (e-nose) is a device designed to recognize and classify odors. The equipment is built around a series of sensors that detect the presence of odors, especially volatile organic compounds (VOCs), and generate an electric signal (voltage), known as e-nose data, which contains chemical information. In the food business, the use of e-noses for analyses and quality control of fruits and plantation crops has increased in recent years. Their use is particularly relevant due to the lack of non-invasive and inexpensive methods to detect VOCs in crops. However, the majority of reports in the literature involve commercial e-noses, with only a few studies addressing low-cost e-nose (LC-e-nose) devices or providing a data-oriented description to assist researchers in choosing their setup and appropriate statistical methods to analyze crop data. Therefore, the objective of this study is to discuss the hardware of the two most common e-nose sensors: electrochemical (EC) sensors and metal oxide sensors (MOSs), as well as a critical review of the literature reporting MOS-based low-cost e-nose devices used for investigating plantations and fruit crops, including the main features of such devices. Miniaturization of equipment from lab-scale to portable and convenient gear, allowing producers to take it into the field, as shown in many appraised systems, is one of the future advancements in this area. By utilizing the low-cost designs provided in this review, researchers can develop their own devices based on practical demands such as quality control and compare results with those reported in the literature. Overall, this review thoroughly discusses the applications of low-cost e-noses based on MOSs for fruits, tea, and coffee, as well as the key features of their equipment (i.e., advantages and disadvantages) based on their technical parameters (i.e., electronic and physical parts). As a final remark, LC-e-nose technology deserves significant attention as it has the potential to be a valuable quality control tool for emerging countries.

An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.

Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani

Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors' responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.

Design and Validation of a Portable Machine Learning-Based Electronic Nose

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography-mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

Compared with traditional gas chromatography-mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors' response to the odors' exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models' performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.

Detection of Lethal Bronzing Disease in Cabbage Palms (Sabal palmetto) Using a Low-Cost Electronic Nose

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO^® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

Design and development of instrumentation for the measurement of sensor array responses

Indigenous instrumentation has been designed and developed for the measurement of the concentration of analytes from eight conductometric metal oxide sensors. The hardware scheme of instrumentation is based on the astable multivibrator configuration. The hardware measures the resistance output from the sensors, conditions, processes, and displays the data on the liquid crystal display. An 8051 based processor averages the data, converts them into engineering units, and sends them to remote PC through ethernet communication for post-data analysis. A graphical user interface (GUI) is developed to acquire, monitor, and display the eight channels' sensor output. GUI plots the online data and offline data as a popup window. The hardware and software of the instrument were tested with standard resistors for calibration and found that in-house developed instrumentation is able to measure with an accuracy of ±0.5% with a resolution of 500 Ω. The instrument has been tested with a semiconductor metal oxide sensor, viz., chromium niobate (CrNbO₄).

Discrimination of Volatiles of Shiitakes (Lentinula edodes) Produced during Drying Process by Electronic Nose

This study aimed to investigate the potential of electronic nose (E-nose) to differentiate volatiles of shiitakes produced at different drying stages. Shiitakes at different drying time slots were categorized into four groups (fresh, early, middle and late stage) by sensory evaluation. E-nose was used to analyze the volatiles and compared with headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry (HS/GC-MS). The principal component analysis results showed that shiitakes at each stage could be successfully discriminated by E-nose and HS/GC-MS. The differences in volatile organic compounds produced at each stage were mainly caused by sulfurs and alcohols, leading to apparent changes of sensors sensitive to sulfurs, alcohols and aromatic compounds. The discriminant models were established by partial least squares discriminant analysis and support vector machine classification, with accuracy rates of 91.25 % and 95.83 %, respectively. The results demonstrated the potential use of E-nose in classifying and monitoring shiitakes during drying process.

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification

Electronic nose (E-nose) systems have become popular in food and fruit quality evaluation because of their rapid and repeatable availability and robustness. In this paper, we propose an E-nose system that has potential as a non-destructive system for monitoring variation in the volatile organic compounds produced by fruit during the maturing process. In addition to the E-nose system, we also propose a camera system to monitor the peel color of fruit as another feature for identification. By incorporating E-nose and camera systems together, we propose a non-destructive solution for fruit maturity monitoring. The dual E-nose/camera system presents the best Fisher class separability measure and shows a perfect classification of the four maturity stages of a banana: Unripe, half-ripe, fully ripe, and overripe.

An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses.

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Preservation Mechanism of Chitosan-Based Coating with Cinnamon Oil for Fruits Storage Based on Sensor Data

The chitosan-based coating with antimicrobial agent has been developed recently to control the decay of fruits. However, its fresh keeping and antimicrobial mechanism is still not very clear. The preservation mechanism of chitosan coating with cinnamon oil for fruits storage is investigated in this paper. Results in the atomic force microscopy sensor images show that many micropores exist in the chitosan coating film. The roughness of coating film is affected by the concentration of chitosan. The antifungal activity of cinnamon oil should be mainly due to its main consistent trans-cinnamaldehyde, which is proportional to the trans-cinnamaldehyde concentration and improves with increasing the attachment time of oil. The exosmosis ratios of Penicillium citrinum and Aspergillus flavus could be enhanced by increasing the concentration of cinnamon oil. Morphological observation indicates that, compared to the normal cell, the wizened mycelium of A. flavus is observed around the inhibition zone, and the growth of spores is also inhibited. Moreover, the analysis of gas sensors indicate that the chitosan-oil coating could decrease the level of O₂ and increase the level of CO₂ in the package of cherry fruits, which also control the fruit decay. These results indicate that its preservation mechanism might be partly due to the micropores structure of coating film as a barrier for gas and a carrier for oil, and partly due to the activity of cinnamon oil on the cell disruption.

A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors

Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field.

Development of fabric-based chemical gas sensors for use as wearable electronic noses

Novel gas sensors embroidered into fabric substrates based on polymers/ SWNT-COOH nanocomposites were proposed in this paper, aiming for their use as a wearable electronic nose (e-nose). The fabric-based chemical gas sensors were fabricated by two main processes: drop coating and embroidery. Four potential polymers (PVC, cumene-PSMA, PSE and PVP)/functionalized-SWCNT sensing materials were deposited onto interdigitated electrodes previously prepared by embroidering conductive thread on a fabric substrate to make an optimal set of sensors. After preliminary trials of the obtained sensors, it was found that the sensors yielded a electrical resistance in the region of a few kilo-Ohms. The sensors were tested with various volatile compounds such as ammonium hydroxide, ethanol, pyridine, triethylamine, methanol and acetone, which are commonly found in the wastes released from the human body. These sensors were used to detect and discriminate between the body odors of different regions and exist in various forms such as the urine, armpit and exhaled breath odor. Based on a simple pattern recognition technique, we have shown that the proposed fabric-based chemical gas sensors can discriminate the human body odor from two persons.

Improving short term instability for quantitative analyses with portable electronic noses

One of the main problems when working with electronic noses is the lack of reproducibility or repeatability of the sensor response, so that, if this problem is not properly considered, electronic noses can be useless, especially for quantitative analyses. On the other hand, irreproducibility is increased with portable and low cost electronic noses where laboratory equipment like gas zero generators cannot be used. In this work, we study the reproducibility of two portable electronic noses, the PEN3 (commercial) and CAPINose (a proprietary design) by using synthetic wine samples. We show that in both cases short term instability associated to the sensors' response to the same sample and under the same conditions represents a major problem and we propose an internal normalization technique that, in both cases, reduces the variability of the sensors' response. Finally, we show that the normalization proposed seems to be more effective in the CAPINose case, reducing, for example, the variability associated to the TGS2602 sensor from 12.19% to 2.2%.

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 × 760 μm(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 1.05 mW of power at supply voltages of 1 and 1.8 V 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.

Hardware friendly probabilistic spiking neural network with long-term and short-term plasticity

This paper proposes a probabilistic spiking neural network (PSNN) with unimodal weight distribution, possessing long- and short-term plasticity. The proposed algorithm is derived by both the arithmetic gradient decent calculation and bioinspired algorithms. The algorithm is benchmarked by the Iris and Wisconsin breast cancer (WBC) data sets. The network features fast convergence speed and high accuracy. In the experiment, the PSNN took not more than 40 epochs for convergence. The average testing accuracy for Iris and WBC data is 96.7% and 97.2%, respectively. To test the usefulness of the PSNN to real world application, the PSNN was also tested with the odor data, which was collected by our self-developed electronic nose (e-nose). Compared with the algorithm (K-nearest neighbor) that has the highest classification accuracy in the e-nose for the same odor data, the classification accuracy of the PSNN is only 1.3% less but the memory requirement can be reduced at least 40%. All the experiments suggest that the PSNN is hardware friendly. First, it requires only nine-bits weight resolution for training and testing. Second, the PSNN can learn complex data sets with a little number of neurons that in turn reduce the cost of VLSI implementation. In addition, the algorithm is insensitive to synaptic noise and the parameter variation induced by the VLSI fabrication. Therefore, the algorithm can be implemented by either software or hardware, making it suitable for wider application.

Towards a chemiresistive sensor-integrated electronic nose: a review

Electronic noses have potential applications in daily life, but are restricted by their bulky size and high price. This review focuses on the use of chemiresistive gas sensors, metal-oxide semiconductor gas sensors and conductive polymer gas sensors in an electronic nose for system integration to reduce size and cost. The review covers the system design considerations and the complementary metal-oxide-semiconductor integrated technology for a chemiresistive gas sensor electronic nose, including the integrated sensor array, its readout interface, and pattern recognition hardware. In addition, the state-of-the-art technology integrated in the electronic nose is also presented, such as the sensing front-end chip, electronic nose signal processing chip, and the electronic nose system-on-chip.

A compact and low cost electronic nose for aroma detection

This article explains the development of a prototype of a portable and a very low-cost electronic nose based on an mbed microcontroller. Mbeds are a series of ARM microcontroller development boards designed for fast, flexible and rapid prototyping. The electronic nose is comprised of an mbed, an LCD display, two small pumps, two electro-valves and a sensor chamber with four TGS Figaro gas sensors. The performance of the electronic nose has been tested by measuring the ethanol content of wine synthetic matrices and special attention has been paid to the reproducibility and repeatability of the measurements taken on different days. Results show that the electronic nose with a neural network classifier is able to discriminate wine samples with 10, 12 and 14% V/V alcohol content with a classification error of less than 1%.

Gas sensors characterization and multilayer perceptron (MLP) hardware implementation for gas identification using a Field Programmable Gate Array (FPGA)

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO₂ thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.

Diverse applications of electronic-nose technologies in agriculture and forestry

Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

An analog multilayer perceptron neural network for a portable electronic nose

This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 μm standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.

Objective display and discrimination of floral odors from Amorphophallus titanum, bloomed on different dates and at different locations, using an electronic nose

As olfactory perceptions vary from person to person, it is difficult to describe smells objectively. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. Here we showed a virtual connection method between a human nose perceptions and electronic nose responses with the smell of standard gases. In this method, Amorphophallus titanum flowers, which emit a strong carrion smell, could objectively be described using an electronic nose, in a way resembling the skill of sommeliers. We could describe the flower smell to be close to that of a mixture of methyl mercaptan and propionic acid, by calculation of the dilution index from electronic resistances. In other words, the smell resembled that of "decayed cabbage, garlic and pungent sour" with possible descriptors. Additionally, we compared the smells of flowers which bloomed on different dates and at different locations and showed the similarity of odor intensities visually, in standard gas categories. We anticipate our assay to be a starting point for a perceptive connection between our noses and electronic noses.

Tomato Quality during Short-Term Storage Assessed by Colour and Electronic Nose

An assay based on an electronic olfactory system was set to evaluate tomato fruits by sensing the aromatic volatiles during postharvest storage of 21 days at C in darkness. Olfactory system measurements were coupled with colour values. Odour profile and senescence parameters were carried out at 7-day intervals. Discriminant function analysis applied to electronic nose data showed three components, accounting for 99.2% of the total variance. In the present assay, separation among groups according to storage time (0, 7, and 14 days) was observed for wildtype. Overexpressed (Money Maker) lines/plants of tomato showed difference between odour profile for day 0 and day 21, even tough a no clear discrimination between 7 and 14 days was observed. Fruit lost weight almost linearly with shelf life () presenting an averaged loss of 21% () for over-expressed (Money Maker) lines/plants, 13% () for silenced (Money Maker), and 14% () for wild type during 21 days of storage. Colour values , , and data showed that colour properties changed during storage for all the lines considered. Correlations between odour profiles and colour parameter were obtained showing that the electronic nose is a useful technique for monitoring short-term storage of tomato.

Invited review article: an odor-sensing system--powerful technique for foodstuff studies

This work examines gas sensor array technology combined with multivariate data processing methods and demonstrates a promising potential for rapid, non-destructive analysis of food. Main attention is focused on detailed description of sensor used in e-nose instruments, construction, and principle of operation of these systems. Moreover, this paper briefly reviews the progress in the field of artificial olfaction and future trends in electronic nose technology, namely, e-nose based on mass spectrometry. Further discussion concerns a comparison of artificial nose with gas chromatography-olfactometry and the application of e-nose instruments in different areas of food industry.

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.

Odour detection methods: olfactometry and chemical sensors

The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc.) and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality); this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants) as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective "analytical instrument" for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses) are then described, focusing on their better performances for environmental analysis. Odour emission monitoring carried out through both the techniques is finally reviewed in order to show the complementary responses of human and instrumental sensing.

An electronic-nose sensor node based on a polymer-coated surface acoustic wave array for wireless sensor network applications

This study developed an electronic-nose sensor node based on a polymer-coated surface acoustic wave (SAW) sensor array. The sensor node comprised an SAW sensor array, a frequency readout circuit, and an Octopus II wireless module. The sensor array was fabricated on a large K(2) 128° YX LiNbO3 sensing substrate. On the surface of this substrate, an interdigital transducer (IDT) was produced with a Cr/Au film as its metallic structure. A mixed-mode frequency readout application specific integrated circuit (ASIC) was fabricated using a TSMC 0.18 μm process. The ASIC output was connected to a wireless module to transmit sensor data to a base station for data storage and analysis. This sensor node is applicable for wireless sensor network (WSN) applications.