Metal oxide sensors for electronic noses and their application to food analysis

System Design Based on Biological Olfaction for Meat Analysis Using E-Nose Sensors

The deterioration of food, especially in meat products, can lead to serious health problems. Even with modern preservation technologies, a significant amount of food is lost due to microbial deterioration. As the very first step of the preservation process, the microflora that grows during the storage time and in spoiling foods should be well-known to identify critical levels. Electronic nose and gas chromatography analysis systems can provide sensitive and promising results. Similarly, bacterial analysis is an important process for determining bacterial groups that result in the emergence of such gases in gas chromatography-mass spectrometry (GC-MS) analysis during the degradation time. This study aims to determine the degradation levels for some meat types under different environmental conditions, such as temperature and duration, to compare with other measurement techniques for evaluating the verification of data. E-nose device, developed in this study, can detect carbon monoxide (CO), methane (CH4), ethanol (C2H5OH), and ammonia (NH3) using metal oxide semiconductor (MOS) sensors. In order to test sensory measurements during this period, GC-MS and microbial measurements were used. E-nose measurements show that the results are in accord with each other. This system can easily be made portable, occupying very little space.

Polycrystalline Transparent Al-Doped ZnO Thin Films for Photosensitivity and Optoelectronic Applications

Thin nanocrystalline transparent Al-doped ZnO (1-10 at.% Al) films were synthesized by solid-phase pyrolysis at 700 °C. Synthesized Al-doped ZnO films were investigated by X-ray diffraction (XRD), scanning and transmission electron microscopy (SEM, TEM). All obtained materials were crystallized into the wurtzite structure, which was confirmed by XRD. The material crystallinity decreases with the introduction of aluminum. SEM and TEM showed that the films are continuous and have a uniform distribution of nanoparticles with an average size of 15-20 nm. TEM confirmed the production of Al-doped ZnO films. The transmittance of Al-doped ZnO films in the range of 400-1000 nm is more than 94%. The introduction of 1% Al into ZnO leads to a narrowing of the band gap compared to ZnO to a minimum value of 3.26 eV and a sharp decrease in the response time to the radiation exposure with a wavelength of 400 nm. An increase in aluminum concentration leads to a slight increase in the band gap, which is associated with the Burstein-Moss effect. The minimum response time (8 s) was shown for film containing 10% Al, which is explained by the shortest average lifetime of charge carriers (4 s).

Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases

Gastrointestinal (GI) diseases have a high prevalence throughout the United States. Screening and diagnostic modalities are often expensive and invasive, and therefore, people do not utilize them effectively. Lack of proper screening and diagnostic assessment may lead to delays in diagnosis, more advanced disease at the time of diagnosis, and higher morbidity and mortality rates. Research on the intestinal microbiome has demonstrated that dysbiosis, or unfavorable alteration of organismal composition, precedes the onset of clinical symptoms for various GI diseases. GI disease diagnostic research has led to a shift towards non-invasive methods for GI screening, including chemical-detection tests that measure changes in volatile organic compounds (VOCs), which are the byproducts of bacterial metabolism that result in the distinct smell of stool. Many of these tools are expensive, immobile benchtop instruments that require highly trained individuals to interpret the results. These attributes make them difficult to implement in clinical settings. Alternatively, electronic noses (E-noses) are relatively cheaper, handheld devices that utilize multi-sensor arrays and pattern recognition technology to analyze VOCs. The purpose of this review is to (1) highlight how dysbiosis impacts intestinal diseases and how VOC metabolites can be utilized to detect alterations in the microbiome, (2) summarize the available VOC analytical platforms that can be used to detect aberrancies in intestinal health, (3) define the current technological advancements and limitations of E-nose technology, and finally, (4) review the literature surrounding several intestinal diseases in which headspace VOCs can be used to detect or predict disease.

Evaluation of a Commercial Electronic Nose Based on Carbon Nanotube Chemiresistors

Recently a hand-held, carbon-nanotube-based electronic nose became available on the market. Such an electronic nose could be interesting for applications in the food industry, health monitoring, environmental monitoring, and security services. However, not much is known about the performance of such an electronic nose. In a series of measurements, the instrument was exposed to low ppm vapor concentrations of four volatile organic compounds with different scent profiles and polarities. Detection limits, linearity of response, repeatability, reproducibility, and scent patterns were determined. The results indicate detection limits in the range of 0.1-0.5 ppm and a linear signal response in the range of 0.5-8.0 ppm. The repeatability of the scent patterns at compound concentrations of 2 ppm allowed the identification of the tested volatiles based on their scent pattern. However, the reproducibility was not sufficient, since different scent profiles were produced on different measurement days. In addition, it was noted that the response of the instrument diminished over time (over several months) possibly by sensor poisoning. The latter two aspects limit the use of the current instrument and make future improvements necessary.

Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages

Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.

Analysis of Floral Scent and Volatile Profiles of Different Aster Species by E-nose and HS-SPME-GC-MS

Plants from the Aster species are known to be a rich source of bioactive chemical compositions and are popularly known for their medicinal properties. To investigate the relationship between the nine species of Aster, the floral fragrance and volatile profile patterns were characterized using E-nose and HS-SPME-GC-MS. Initial optimization for fragrance analysis was performed with Aster yomena using E-nose by evaluating the scent patterns in different flowering stages. Aster yomena exhibited varied scent patterns in each flowering stage, with the highest relative aroma intensity (RAI) in the full flowering stage. PCA analysis to compare and analyze the scent characteristics of nine Aster species, showed a species-specific classification. HS-SPME-GC-MS analysis of flowers from nine Aster species revealed 52 volatile compounds including β-myrcene, α-phellandrene, D-limonene, trans-β-ocimene, caryophyllene, and β-cadinene. The terpenoid compounds accounted for the largest proportion. Among the nine Aster species flowers, Aster koraiensis had sesquiterpenes as the major component, and the remaining eight varieties had monoterpenes in abundance. These results could distinguish the species according to the scent patterns and volatile components of the nine Aster species. Additionally, flower extracts from the Aster species’ plants exhibited radical scavenging antioxidant activity. Among them, it was confirmed that Aster pseudoglehnii, Aster maackii, and Aster arenarius had high antioxidant activity. In conclusion, the results of this study provide fundamental data of the volatile compound properties and antioxidant activity of Aster species, offering basic information of valuable natural sources that can be utilized in the pharmaceutical, perfume, and cosmetic industries.

A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition

The sensitivity and selectivity profiles of gas sensors are always changed by sensor drifting, sensor aging, and the surroundings (e.g., temperature and humidity changes), which lead to a serious decline in gas recognition accuracy or even invalidation. To address this issue, the practical solution is to retrain the network to maintain performance, leveraging its rapid, incremental online learning capacity. In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-incremental learning, and can be retrained quickly with a new gas at a low accuracy cost. Compared with gas recognition approaches such as support vector machine (SVM), k-nearest neighbor (KNN), principal component analysis (PCA) +SVM, PCA+KNN, and artificial neural network (ANN), our network achieves the highest accuracy of 98.75% in five-fold cross-validation for identifying nine types of gases, each with five different concentrations. In particular, the proposed network has a 5.09% higher accuracy than that of other gas recognition algorithms, which validates its robustness and effectiveness for real-life fire scenarios.

Effect of Ultrasound Combinated with Sodium Hypochlorite Treatment on Microbial Inhibition and Quality of Fresh-Cut Cucumber

The influence of ultrasound combined with sodium hypochlorite (US-NaClO) treatment on microorganisms and quality of fresh-cut cucumber during storage were investigated. Ultrasound (400 W, 40 kHz, US: 5, 10 and 15 min) and sodium hypochlorite (NaClO: 50, 75, 100 ppm) were used to treat fresh-cut cucumber in a single or combined treatment and stored at 4 °C for 8 days and analyzed for texture, color and flavor. The results showed that US-NaClO treatment had a synergistic effect on the inhibition of microorganisms during storage. It could significantly reduce (p < 0.05) the number of microorganisms by 1.73 to 2.17 log CFU/g. In addition, US-NaClO treatment reduced the accumulation of malondialdehyde (MDA) during storage (4.42 nmol/g) and water mobility, and maintained the integrity of the cell membrane, delayed the increase of weight loss (3.21%), reduced water loss, thus slowing down the decline of firmness (9.20%) of fresh-cut cucumber during storage. The degradation of chlorophyll (6.41%) was reduced to maintain the color of freshly cut cucumbers. At the same time, US-NaClO could maintain the content of aldehydes, the main aromatic substance of cucumber, and reduced the content of alcohols and ketones during storage. Combined with the electronic nose results, it could maintain the cucumber flavor at the end of the storage period and reduce the odor produced by microorganisms. Overall, US-NaClO was helpful to inhibit the growth of microorganisms during storage, improve the quality of fresh-cut cucumber.

Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics

Cancers, chronic diseases and respiratory infections are major causes of mortality and present diagnostic and therapeutic challenges for health care. There is an unmet medical need for non-invasive, easy-to-use biomarkers for the early diagnosis, phenotyping, predicting and monitoring of the therapeutic responses of these disorders. Exhaled breath sampling is an attractive choice that has gained attention in recent years. Exhaled nitric oxide measurement used as a predictive biomarker of the response to anti-eosinophil therapy in severe asthma has paved the way for other exhaled breath biomarkers. Advances in laser and nanosensor technologies and spectrometry together with widespread use of algorithms and artificial intelligence have facilitated research on volatile organic compounds and artificial olfaction systems to develop new exhaled biomarkers. We aim to provide an overview of the recent advances in and challenges of exhaled biomarker measurements with an emphasis on the applicability of their measurement as a non-invasive, point-of-care diagnostic and monitoring tool.

Formation and Analysis of Volatile and Odor Compounds in Meat-A Review

The volatile composition and odor of meat and meat products is based on the precursors present in the raw meat. These are influenced by various pre-slaughter factors (species, breed, sex, age, feed, muscle type). Furthermore, post-mortem conditions (chiller aging, cooking conditions, curing, fermentation, etc.) determine the development of meat volatile organic compounds (VOCs). In this review, the main reactions leading to the development of meat VOCs such as the Maillard reaction; Strecker degradation; lipid oxidation; and thiamine, carbohydrate, and nucleotide degradation are described. The important pre-slaughter factors and post-mortem conditions influencing meat VOCs are discussed. Finally, the pros, cons, and future perspectives of the most commonly used sample preparation techniques (solid-phase microextraction, stir bar sorptive extraction, dynamic headspace extraction) and analytical methods (gas chromatography mass spectrometry and olfactometry, as well as electronic noses) for the analysis of meat VOCs are discussed, and the continued importance of sensorial analysis is pinpointed.

Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees

Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014−2015 and 2017−2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach “Classification and regression trees” (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

Artificial Olfactory Neuron for an In-Sensor Neuromorphic Nose

A neuromorphic module of an electronic nose (E-nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T-neuron) made of a metal-oxide-semiconductor field-effect transistor (MOSFET). By mimicking a biological olfactory neuron, it simultaneously detects a gas and encoded spike signals for in-sensor neuromorphic functioning. It identifies an odor source by analyzing the complicated mixed signals using a spiking neural network (SNN). The proposed E-nose does not require conversion circuits, which are essential for processing the sensory signals between the sensor array and processors in the conventional bulky E-nose. In addition, they do not have to include a central processing unit (CPU) and memory, which are required for von Neumann computing. The spike transmission of the biological olfactory system, which is known to be the main factor for reducing power consumption, is realized with the SNN for power savings compared to the conventional E-nose with a deep neural network (DNN). Therefore, the proposed neuromorphic E-nose is promising for application to Internet of Things (IoT), which demands a highly scalable and energy-efficient system. As a practical example, it is employed as an electronic sommelier by classifying different types of wines.

Gas Recognition in E-Nose System: A Review

Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.

The Use of Electronic Nose as Alternative Non-Destructive Technique to Discriminate Flavored and Unflavored Olive Oils

Cv. Arbequina extra virgin olive oils (EVOO) were flavored with cinnamon, garlic, and rosemary and characterized. Although flavoring significantly affected the physicochemical quality parameters, all oils fulfilled the legal thresholds for EVOO classification. Flavoring increased (20 to 40%) the total phenolic contents, whereas oxidative stability was dependent on the flavoring agent (a slight increase for rosemary and a decrease for cinnamon and garlic). Flavoring also had a significant impact on the sensory profiles. Unflavored oils, cinnamon, and garlic flavored oils had a fruity-ripe sensation while rosemary flavored oils were fruity-green oils. Fruit-related sensations, perceived in unflavored oils, disappeared with flavoring. Flavoring decreased the sweetness, enhanced the bitterness, and did not influence the pungency of the oils. According to the EU regulations, flavored oils cannot be commercialized as EVOO. Thus, to guarantee the legal labelling requirement and to meet the expectations of the market-specific consumers for differentiated olive oils, a lab-made electronic nose was applied. The device successfully discriminated unflavored from flavored oils and identified the type of flavoring agent (90 ± 10% of correct classifications for the repeated K-fold cross-validation method). Thus, the electronic nose could be used as a practical non-destructive preliminary classification tool for recognizing olive oils’ flavoring practice.

An Overview of Artificial Olfaction Systems with a Focus on Surface Plasmon Resonance for the Analysis of Volatile Organic Compounds

The last three decades have witnessed an increasing demand for novel analytical tools for the analysis of gases including odorants and volatile organic compounds (VOCs) in various domains. Traditional techniques such as gas chromatography coupled with mass spectrometry, although very efficient, present several drawbacks. Such a context has incited the research and industrial communities to work on the development of alternative technologies such as artificial olfaction systems, including gas sensors, olfactory biosensors and electronic noses (eNs). A wide variety of these systems have been designed using chemiresistive, electrochemical, acoustic or optical transducers. Among optical transduction systems, surface plasmon resonance (SPR) has been extensively studied thanks to its attractive features (high sensitivity, label free, real-time measurements). In this paper, we present an overview of the advances in the development of artificial olfaction systems with a focus on their development based on propagating SPR with different coupling configurations, including prism coupler, wave guide, and grating.

Principles of odor coding in vertebrates and artificial chemosensory systems

The biological olfactory system is the sensory system responsible for the detection of the chemical composition of the environment. Several attempts to mimic biological olfactory systems have led to various artificial olfactory systems using different technical approaches. Here we provide a parallel description of biological olfactory systems and their technical counterparts. We start with a presentation of the input to the systems, the stimuli, and treat the interface between the external world and the environment where receptor neurons or artificial chemosensors reside. We then delineate the functions of receptor neurons and chemosensors as well as their overall I-O relationships. Up to this point, our account of the systems goes along similar lines. The next processing steps differ considerably: while in biology the processing step following the receptor neurons is the "integration" and "processing" of receptor neuron outputs in the olfactory bulb, this step has various realizations in electronic noses. For a long period of time, the signal processing stages beyond the olfactory bulb, i.e., the higher olfactory centers were little studied. Only recently there has been a marked growth of studies tackling the information processing in these centers. In electronic noses, a third stage of processing has virtually never been considered. In this review, we provide an up-to-date overview of the current knowledge of both fields and, for the first time, attempt to tie them together. We hope it will be a breeding ground for better information, communication, and data exchange between very related but so far little connected fields.

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.

Sniffing Bacteria with a Carbon-Dot Artificial Nose

HIGHLIGHTS

Novel artificial nose based upon electrode-deposited carbon dots (C-dots). Significant selectivity and sensitivity determined by "polarity matching" between the C-dots and gas molecules. The C-dot artificial nose facilitates, for the first time, real-time, continuous monitoring of bacterial proliferation and discrimination among bacterial species, both between Gram-positive and Gram-negative bacteria and between specific strains. Machine learning algorithm furnishes excellent predictability both in the case of individual gases and for complex gas mixtures. Continuous, real-time monitoring and identification of bacteria through detection of microbially emitted volatile molecules are highly sought albeit elusive goals. We introduce an artificial nose for sensing and distinguishing vapor molecules, based upon recording the capacitance of interdigitated electrodes (IDEs) coated with carbon dots (C-dots) exhibiting different polarities. Exposure of the C-dot-IDEs to volatile molecules induced rapid capacitance changes that were intimately dependent upon the polarities of both gas molecules and the electrode-deposited C-dots. We deciphered the mechanism of capacitance transformations, specifically substitution of electrode-adsorbed water by gas molecules, with concomitant changes in capacitance related to both the polarity and dielectric constants of the vapor molecules tested. The C-dot-IDE gas sensor exhibited excellent selectivity, aided by application of machine learning algorithms. The capacitive C-dot-IDE sensor was employed to continuously monitor microbial proliferation, discriminating among bacteria through detection of distinctive "volatile compound fingerprint" for each bacterial species. The C-dot-IDE platform is robust, reusable, readily assembled from inexpensive building blocks and constitutes a versatile and powerful vehicle for gas sensing in general, bacterial monitoring in particular.

Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose

The organoleptic profile of a Virgin Olive Oil is a key quality parameter that is currently obtained by human sensory panels. The development of an instrumental technique capable of providing information about this profile quickly and online is of great interest. This work employed a general purpose e-nose, in lab conditions, to predict the level of fruity aroma and the presence of defects in Virgin Olive Oils. The raw data provided by the e-nose were used to extract a set of features that fed a regressor to predict the level of fruity aroma and a classifier to detect the presence of defects. The results obtained were a mean validation error of 0.5 units for the prediction of fruity aroma using lasso regression; and 88% accuracy for the defect detection using logistic regression. Finally, the identification of two out of ten specific sensors of the e-nose that can provide successful results paves the way to the design of low-cost specific electronic noses for this application.

Recognizing lung cancer and stages using a self-developed electronic nose system

Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that could be used as non-invasive biomarkers of lung cancer. Breath-based lung cancer screening has attracted wide attention on account of its convenience, low cost and easy popularization. In this paper, the research of lung cancer detection and staging is conducted by the self-developed electronic nose (e-nose) system. In order to investigate the performance of the device in distinguishing lung cancer patients from healthy controls, two feature extraction methods and two different classification models were adopted. Among all the models, kernel principal component analysis (KPCA) combined with extreme gradient boosting (XGBoost) achieved the best results among 235 breath samples. The accuracy, sensitivity and specificity of e-nose system were 93.59%, 95.60% and 91.09%, respectively. Meanwhile, the device could innovatively classify stages of 90 lung cancer patients (i.e., 44 stage III and 46 stage IV). Experimental results indicated that the recognition accuracy of lung cancer stages was more than 80%. Further experiments of this research also showed that the combination of sensor array and pattern recognition algorithms could identify and distinguish the expiratory characteristics of lung cancer, smoking and other respiratory diseases.

Nanoengineering Approaches Toward Artificial Nose

Significant scientific efforts have been made to mimic and potentially supersede the mammalian nose using artificial noses based on arrays of individual cross-sensitive gas sensors over the past couple decades. To this end, thousands of research articles have been published regarding the design of gas sensor arrays to function as artificial noses. Nanoengineered materials possessing high surface area for enhanced reaction kinetics and uniquely tunable optical, electronic, and optoelectronic properties have been extensively used as gas sensing materials in single gas sensors and sensor arrays. Therefore, nanoengineered materials address some of the shortcomings in sensitivity and selectivity inherent in microscale and macroscale materials for chemical sensors. In this article, the fundamental gas sensing mechanisms are briefly reviewed for each material class and sensing modality (electrical, optical, optoelectronic), followed by a survey and review of the various strategies for engineering or functionalizing these nanomaterials to improve their gas sensing selectivity, sensitivity and other measures of gas sensing performance. Specifically, one major focus of this review is on nanoscale materials and nanoengineering approaches for semiconducting metal oxides, transition metal dichalcogenides, carbonaceous nanomaterials, conducting polymers, and others as used in single gas sensors or sensor arrays for electrical sensing modality. Additionally, this review discusses the various nano-enabled techniques and materials of optical gas detection modality, including photonic crystals, surface plasmonic sensing, and nanoscale waveguides. Strategies for improving or tuning the sensitivity and selectivity of materials toward different gases are given priority due to the importance of having cross-sensitivity and selectivity toward various analytes in designing an effective artificial nose. Furthermore, optoelectrical sensing, which has to date not served as a common sensing modality, is also reviewed to highlight potential research directions. We close with some perspective on the future development of artificial noses which utilize optical and electrical sensing modalities, with additional focus on the less researched optoelectronic sensing modality.

Identification of Mint Scents Using a QCM Based E-Nose

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

Discrimination of Zanthoxylum bungeanum Maxim through volatile aroma compounds analysis with artificial neural network

Zanthoxylum bungeanum Maxim (ZBM), a special spice from Chinese different areas, have a widespread variation in quality and price. To avoid the commercial adulteration of ZBM, it is necessary to discriminate them from different areas. As volatile aroma compounds (VAC) have the potential to discriminate ZBM, electronic nose (E-nose) was used to preliminarily discriminate the VAC through sensor response analysis, radar chart analysis, and principal component analysis. Then, Gas chromatography-mass spectrometry (GC-MS) was utilized to identify VAC through hierarchical cluster analysis and quantitative analysis. Finally, artificial neural network (ANN) was employed to assess the accuracy of the discrimination of ZBM. As a result, we found that ZBM could be successfully discriminated between Chinese Sichuan and the other areas. Our findings would provide guidance for evaluating and predicting the variation of VAC of ZBM from different areas in further study. PRACTICAL APPLICATIONS: Zanthoxylum bungeanum Maxim (ZBM) is a traditional and important spice used in Sichuan cuisine especially hotpot, which are famous all over overseas. However, the ZBM from different producing areas bring various flavors, hampering the quality of Sichuan cuisine developing toward to standardization. Therefore, the authors in this work pursuit an effective way to distinguish the ZBM produced in Sichuan rather than in other province. According to the results of the present study, ZBM could be successfully discriminated between Chinese Sichuan and the other producing areas by using E-nose and GC-MS through artificial neural network. These findings would provide the guidance for evaluating the producing areas of ZBM to be whether or not Sichuan, which could offer the practical help in the purchase of the raw material in the supply chain. Besides, these also can be applied to predict the variation of volatile aroma compounds of the ZBM in the further study.

Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods

Crataegi Fructus (CF) is widely used as a medicinal and edible material around the world. Currently, different types of processed CF products are commonly found in the market. Quality evaluation of them mainly relies on chemical content determination, which is time and money consuming. To rapidly and nondestructively discriminate different types of processed CF products, an electronic nose coupled with chemometrics was developed. The odour detection method of CF was first established by single-factor investigation. Then, the sensor array was optimised by a stepwise discriminant analysis (SDA) and analysis of variance (ANOVA). Based on the best-optimised sensor array, the digital and mode standard were established, realizing the odour quality control of samples. Meanwhile, mathematical prediction models including the discriminant formula and back-propagation neural network (BPNN) model exhibited good evaluation with a high accuracy rate. These results suggest that the developed electronic nose system could be an alternative way for evaluating the odour of different types of processed CF products.

Utilization of a Gas-Sensing System to Discriminate Smell and to Monitor Fermentation during the Manufacture of Oolong Tea Leaves

The operational duration of shaking tea leaves is a critical factor in the manufacture of oolong tea; this duration influences the formation of its flavor and fragrance. The current method to control the duration of fermentation relies on the olfactory sense of tea masters; they monitor the entire process through their olfactory sense, and their experience decides the duration of shaking and setting. Because of this human factor and olfactory fatigue, it is difficult to define an optimum duration of shaking and setting; an inappropriate duration of shaking and setting deteriorates the quality of the tea. In this study, we used metal-oxide-semiconductor gas sensors to establish an electronic nose (E-nose) system and tested its feasibility. This research was divided into two experiments: distinguishing samples at various stages and an on-line experiment. The samples of tea leaves at various stages exhibited large differences in the level of grassy smell. From the experience of practitioners and from previous research, the samples could be categorized into three groups: before the first shaking (BS1), before the shaking group, and after the shaking group. We input the experimental results into a linear discriminant analysis to decrease the dimensions and to classify the samples into various groups. The results show that the smell can also be categorized into three groups. After distinguishing the samples with large differences, we conducted an on-line experiment in a tea factory and tried to monitor the smell variation during the manufacturing process. The results from the E-nose were similar to those of the sense of practitioners, which means that an E-nose has the possibility to replace the sensory function of practitioners in the future.

Noticeable improvement in the toxic gas-sensing activity of the Zn-doped TiO2 films for sensing devices

Sensing element view, sening mechanism, stability, response and recovery time of the Zn-doped TiO₂ films.

Development of a Tuneable NDIR Optical Electronic Nose

Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO₂ and CH₄), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO₂ and CH₄. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications.

Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review

Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.

Odor Detection Using an E-Nose With a Reduced Sensor Array

Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper, we examine a procedure to extract and select modeling features for optimal e-nose performance. The usefulness of this approach is demonstrated in detail. We calculated the model’s performance using cross-validation with the standard leave-one-group-out and group shuffle validation methods. Our analysis of wine spoilage data from the sensor array shows when a transient sensor response is considered, both from gas adsorption and desorption phases, it is possible to obtain a reasonable level of odor detection even with data coming from a single sensor. This requires adequate extraction of modeling features and then selection of features used in the final model.

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

Modified Screen Printed Electrode Suitable for Electrochemical Measurements in Gas Phase

We describe a convenient assembly for screen printed carbon electrodes (SPCE) suitable for analyses in gaseous samples which are of course lacking in supporting electrolytes. It consists of a circular crown of filter paper, soaked in a RTIL or a DES, placed upon a disposable screen printed carbon cell, so as to contact the outer edge of the carbon disk working electrode, as well as peripheral counter and reference electrodes. The electrical contact between the paper crown soaked in RTIL or DES and SPCE electrodes is assured by a gasket, and all components are installed in a polylactic acid holder. As a result of this configuration, a sensitive, fast-responding, membrane-free gas sensor is achieved where the real working electrode surface is the boundary zone of the carbon working disk contacted by the paper crown soaked in the polyelectrolyte. This assembly provides a portable and disposable electrochemical platform, assembled by the easy immobilization onto a porous and inexpensive supporting material such as paper of RTILs or DESs which are characterized by profitable electrical conductivity and negligible vapor pressure. The electroanalytical performance of this device was evaluated by voltammetric and flow injection analyses of oxygen which was chosen as prototype of electroactive gaseous analytes. The results obtained pointed out that this assembly is very profitable for the analysis of gaseous atmospheres, especially when used as detector for FIA in gaseous streams.

Amine-Functionalized ZIF-8 as a Fluorescent Probe for Breath Volatile Organic Compound Biomarker Detection of Lung Cancer Patients

The highly thermally and chemically stable imidazole framework ZIF-8 samples were separately postmodified with amine groups by using N,N'-dimethylethylenediamine (MMEN) and N,N-dimethylaminoethylamine (MAEA), which had the same molecular formula but different structures. The modified ZIF-8 samples (ZIF-8@amine) were thoroughly characterized, including powder X-ray diffractometry, Fourier-transformed infrared spectroscopy, and physical adsorption at 77 K by nitrogen, thermogravimetric analysis, and photophysical characterization. Results showed that after modification, the Brunauer-Emmett-Teller surface area and total pore volume both increased, almost one time higher than those of the original ZIF-8 sample, and followed the order: ZIF-8-MMEN > ZIF-8-MAEA > ZIF-8. Furthermore, the N-H group was successfully grafted into the modified ZIF-8 samples. To examine the sensing properties of the modified ZIF-8@amine samples toward the breath biomarkers of lung cancer, five potential volatile organic compound biomarkers were used as analytes. ZIF-8-MMEN and ZIF-8-MAEA revealed a unique capacity for sensing hexanal, ethylbenzene, and 1-propanol with high efficiency and sensitivity. The three samples all did not show sensing ability toward styrene and isoprene. In addition, ZIF-8, ZIF-8-MMEN, and ZIF-8-MAEA all can sense hexanal with a detection limit as low as 1 ppb.

Emerging trends of advanced sensor based instruments for meat, poultry and fish quality- a review

Meat and fish chemical composition and sensory attributes are markers of quality that require innovative assessment methods as existing ones are rather technical, laborious, and expensive. Emerging trends of advanced technology instruments have been lauded in the pharmaceutical, cosmetic and food industries for their high sensitivity, customizability, rapidness and affordability. Common among these, are the electronic tongue (e-tongue) and electronic nose (e-nose) but their use for meat and fish quality, remains scanty and scattered. This paper aims to systematically discuss the developing trends, principles and the recent use of e-tongue and e-nose for quality measurements in fish and meat. From over 90 research papers, it was observed that an arsenal of chemometric tools have been pivotal in applying these instruments for rapid quantitative, qualitative and predictive analysis of some physical properties, chemical properties, storability and the authentication of meat and fish. Both instruments require no reagent (waste free analytical procedure) and have been lauded for precision and*accuracy but e-nose may be better suited for meat and fish assessments. Unlike the e-tongue, e-nose requires no liquid sample preparation and portable versions are promising for rapid remote analysis of meat and fish samples that can save cost on transferring carcass to laboratories.

Cuprous Oxide Based Chemiresistive Electronic Nose for Discrimination of Volatile Organic Compounds

This paper reports a facile functionalization method on a metal-oxide semiconductor and a cuprous oxide (Cu₂O) based chemiresistive electronic nose for the detection of volatile organic compounds (VOCs). A library of functionalized Cu₂O nanospheres was developed through silanization using chemically diverse organosilanes. An electronic nose was fabricated with unmodified Cu₂O nanospheres and five types of functionalized Cu₂O nanospheres as the sensing elements. The electronic nose showed stable and rapid resistance responses to 25-200 ppm model VOCs, with the operating temperature of 180 °C. Single VOCs and ternary VOC mixtures could be discriminated by the electronic nose, and six types of tea leaves were also proved to be distinguishable as an illustration of the application of the electronic nose. We expected that the silanization could provide a simple approach for material diversification and the electronic nose would have further application in identification and discrimination of complex gas samples.

Discrimination of Red Wines with a Gas-Sensor Array Based on a Surface-Acoustic-Wave Technique

We applied a thermal-desorption gas-chromatograph mass-spectrometer (TD-GC–MS) system to identify the marker volatile organic compounds (VOCs) in the aroma of red wine. After obtaining the marker VOC, we utilized surface acoustic waves (SAWs) to develop a highly sensitive sensing system as ‘electronic nose’ to detect these marker VOC. The SAW chips were fabricated on a LiNbO₃ substrate with a lithographic process. We coated sensing polymers on the sensing area to adsorb the marker VOC in a sample gas. The adsorption of the marker VOC altered the velocity of the SAW according to a mass-loading effect, causing a frequency decrease. This experiment was conducted with wines of three grape varieties—cabernet sauvignon, merlot and black queen. According to the results of TD-GC–MS, the King brand of red wine is likely to have unique VOC, which are 2-pentanone, dimethyl disulfide, 2-methylpropyl acetate and 2-pentanol; Blue Nun-1 probably has a special VOC such as 2,3-butanedione. We hence used a SAW sensor array to detect the aroma of red wines and to distinguish their components by their frequency shift. The results show that the use of polyvinyl butyral (PVB) as a detecting material can distinguish Blue Nun-2 from the others and the use of polyvinyl alcohol (PVA) can distinguish King from the others. We conducted random tests to prove the accuracy and the reliability of our SAW sensors.

Effect of carbon dots with chitosan coating on microorganisms and storage quality of modified-atmosphere-packaged fresh-cut cucumber

BACKGROUND

In order to inhibit microorganisms and improve storage quality of fresh-cut cucumber, fresh-cut cucumber was treated by carbon dots (CDs) from kelp/chitosan (CH) coating solution with CD concentrations of 0%, 1.5%, 3% and 4.5% and then packaged as well as stored at 4 °C for 15 days. The effect of CDs/CH coating on microorganisms and the quality of modified-atmosphere-packaged fresh-cut cucumber during storage were investigated.

RESULTS

The CDs was monodispersed spherical morphology with size distribution of 0.54-0.83 nm. Interaction of CDs and CH had the generation of strong hydrogen bond. Inhibition zone diameters of CDs/CH coating against Staphylococcus aureus and Escherichia coli were enhanced with the increase of CD concentrations. Moreover, CDs/CH coating inhibited the growth of total number of colonies, mold, and yeast in modified-atmosphere-packaged fresh-cut cucumber during storage. A coating of 4.5% CDs/CH effectively reduced the losses of weight, firmness, and total soluble solids, the degradation of ascorbic acid content and flavor, and inhibited peroxidases activity, as well as decreased water mobility in fresh-cut cucumber during storage.

CONCLUSION

The results indicated that a CDs/CH coating was helpful for inhibiting microorganisms and improving storage quality, and could be an effective method to prolong shelf life of fresh-cut cucumber. © 2019 Society of Chemical Industry.

Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness.

Ultrasound treatment to modified atmospheric packaged fresh-cut cucumber: Influence on microbial inhibition and storage quality

Influence of ultrasound treatment on microorganisms and the quality of modified atmospheric packaged fresh-cut cucumber during storage were investigated. Fresh-cut cucumber was treated with ultrasound (US, 20 kHz) in different time (5, 10 and 15 min) and then modified atmospheric packaged as well as stored at 4 °C for 15 d. The results showed that US treatment inhibited the growth of total number of colonies, mold and yeast in modified atmospheric packaged fresh-cut cucumber during storage. US treatment for 10 min effectively reduced the loss of weight, firmness, total soluble solids and total color change (ΔE), the increase of MDA content and the degradation of ascorbic acid content and flavor, as well as decreased water mobility and maintained cell wall integrity in fresh-cut cucumber during storage. Therefore, these results demonstrated that US treatment was helpful for inhibiting microorganisms and improving storage quality, and could be an effective method to enhance the preservative effect of modified atmospheric packaged fresh-cut cucumber.