Portable E-nose Research Articles

Sensor array optimization for the electronic nose via different deep learning methods

A sensor array is a key component of an electronic nose (E-nose). However, the practical applications of the E-nose are often inhibited by its size and energy consumption arising from the number of gas sensors. Achieving a high-performance E-nose with a minimum number of sensors is key and challenging for its practical applications. In this study, different machine learning models have been studied and compared to optimize the performance of the E-nose. The results show that the convolutional neural network (CNN) is the best-performing model, which has an accuracy of 0.986 for classification, and an R-square score of 0.979 for concentration prediction, outperforming the gated recurrent unit, long short-term memory, multi-layer perceptron neural network, and support vector machine. The performance of the E-nose experiences only a minor decrease when the number of sensors that participated in pattern recognition is reduced from eight to four, where the CNN model can yield an accuracy of 0.905 for classification and an R-square of 0.972 for concentration prediction. To further quantify the pros and cons of array optimization, a cost-effective metric is designed to reveal the suitable array size under different scenarios. This work can provide valuable guidance in the design of portable E-nose devices with a smaller size and optimal performance.

Emerging trends in metal oxide-based electronic noses for healthcare applications: a review.

An electronic nose (E-nose) is a technology fundamentally inspired by the human nose, designed to detect, recognize, and differentiate specific odors or volatile components in complex and chaotic environments. Comprising an array of sensors with meticulously designed nanostructured architectures, E-noses translate the chemical information captured by these sensors into useful metrics using complex pattern recognition algorithms. E-noses can significantly enhance the quality of life by offering preventive point-of-care devices for medical diagnostics through breath analysis, and by monitoring and tracking hazardous and toxic gases in the environment. They are increasingly being used in defense and surveillance, medical diagnostics, agriculture, environmental monitoring, and product validation and authentication. The major challenge in developing a reliable E-nose involves miniaturization and low power consumption. Various sensing materials are employed to address these issues. This review presents the key advancements over the last decade in E-nose technology, specifically focusing on chemiresistive metal oxide sensing materials. It discusses their sensing mechanisms, integration into portable E-noses, and various data analysis techniques. Additionally, we review the primary metal oxide-based E-noses for disease detection through breath analysis. Finally, we address the major challenges and issues in developing and implementing a portable metal oxide-based E-nose.

CRISPR/Cas9-mediated lipoxygenase gene-editing in yellow pea leads to major changes in fatty acid and flavor profiles.

Although pulses are nutritious foods containing high amounts of protein, fiber and phytochemicals, their consumption and use in the food industry have been limited due to the formation of unappealing flavors/aromas described as beany, green, and grassy. Lipoxygenase (LOX) enzymes are prevalent among pulse seeds, and their activity can lead to the formation of specific volatile organic compounds (VOCs) from certain polyunsaturated fatty acids (PUFAs). As a widespread issue in legumes, including soybean, these VOCs have been linked to certain unappealing taste perception of foods containing processed pulse seeds. To address this problem in pea and as proof of principle to promote the wider use of pulses, a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) construct was designed to create null alleles (knockouts) of PsLOX2 which had been implicated in the generation of VOCs in peas. Successful CRISPR/Cas9-mediated LOX gene editing of stable transgenic pea lines (TGP) was confirmed by DNA sequencing of the wild type (WT) and TGP pslox2 mutant lines. These lines were also assessed for LOX activity, PUFA levels, and VOCs. Compared to WT peas, the TGP lines showed a significant reduction (p < 0.05) in LOX activity and in the concentration of key VOCs, including hexanal, 2-hexenal, heptanal, (E)-2-heptenal, (E,E)-2,4-heptadienal, 1-octen-3-ol, octanal, (E)-2-octenal (E,E)-2,4-nonadienal and furan-2-pentyl. The content of two essential PUFAs, linoleic and α-linolenic acids, the known substrates of LOX in plants, was higher in TGP flours, indicating the efficacy of the CRISPR-mediated gene editing in minimizing their oxidation and the further modification of PUFAs and their products. The collection of VOCs from the headspace of ground pea seeds, using a portable eNose also distinguished the TGP and WT lines. Multiple regression analysis showed that LOX activity correlated with the two VOCs, heptanal and (E,E)-2,4-heptadienal in pea flours. Partial Least Squares Regression (PLS-R) plot for selected PUFAs, VOCs, and sensor responses in WT and TGP lines showed distinct clusters for WT and TGP lines. Together this data demonstrates the utility of CRISPR mediated mutagenesis of PsLOX2 to quickly improve aroma and fatty acid (FA) profiles of pea seeds of an elite Canadian variety.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap, nondestructive, and easy to use, gas sensors play important roles in the food industry. However, most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing. Also, an ideal electronic nose (E-nose) in a cold chain should be stable to its surroundings and remain highly accurate and portable. In this work, a portable film bulk acoustic resonator (FBAR)-based E-nose was built for real-time measurement of banana shelf time. The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone, and by introducing an air-tight FBAR as a reference, the E-nose can avoid most of the drift caused by surroundings. With the help of porous layer by layer (LBL) coating of the FBAR, the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate, while the detection range is large enough to cover a relative humidity of 0.8. In this regard, the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state, thereby showing the possibility of real-time shelf time detection. This portable FBAR-based E-nose has a large testing scale, high sensitivity, good humidity tolerance, and low frequency drift to its surroundings, thereby meeting the needs of cold-chain usage.

Portable electronic nose system with elastic architecture and fault tolerance based on edge computing, ensemble learning, and sensor swarm

The portable electronic nose (E-nose) systems are suffering from the limited computing ability of microcontrollers and can only adopt simple pattern recognition algorithms. The heavy coupling between the sensors greatly limits the anti-fault ability of the system. Herein, a novel ensemble learning framework based on independent artificial neural networks (ANN) is proposed in a portable E-nose system to recognize volatile organic compounds (VOCs). Edge computing is applied to complete data processing and analysis on an ARM Cortex-M3 based microcontroller in an E-nose system. Each sensing unit can recognize VOCs by training independent ANN models. Then an ensemble learning method further organizes the diverse ANN models into an onboard sensor swarm with a significantly enhanced performance. The accuracy of the type classification can reach 81.1%, which is improved by more than 20% compared with the best individual classifier. The R2 score of concentration prediction can reach 84.1%, which is improved by more than 25% compared with the best regressor in the ensemble group. For both type identification and concentration prediction, the tolerance to the sensor faults of swarm-based E-nose is 10 and 18 times greater than that of the conventional array-based sensors, respectively. Our work has designed an elastic architecture based on sensor swarms, providing a novel avenue for the development of E-nose systems with high fault tolerance.

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.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Seafood freshness: e-nose data for classification purposes

The aim of this study was to develop an electronic nose system useful for the inspection of different seafood products and specifically designed to be applied as an auxiliary device for the management of seafood in the large-scale distribution chain. Since the freshness is a critical issue in determining the quality of fish it is of great importance to have a simple and rapid analytical method representing a useful tool for assessing the acceptability of the seafood products in the distribution centers or in the supermarkets. In this work a portable e-nose, composed by four selected metal oxide semiconductor sensors, a photoionization detector and two electrochemical cells, was applied to test three different seafood products - sole (Solea senegalensis) fillets, red mullet (Mullus barbatus) fillets and cuttlefish (Seppia officinalis) - during their shelf life from the day of arrival and packaging at the distribution center until beyond the expiration date. The K-means partitional clustering method was applied to group samples into three freshness classes, then confirmed by microbial analysis; two classification models based on k-nearest neighbors (K-NN) and partial least square-discriminant analysis were developed to classify the freshness of the seafood regardless the species and in particular, the KNN model provided 100% overall sensitivity, specificity and precision in prediction thus confirming the applicability of the system to inform retailers on product safety and quality.

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.

Aquaphotomic, E-Nose and Electrolyte Leakage to Monitor Quality Changes during the Storage of Ready-to-Eat Rocket.

The consumption of ready-to-eat (RTE) leafy vegetables has increased rapidly due to changes in consumer diet. RTE products are perceived as fresh, high-quality, and health-promoting. The monitoring of the RTE quality is crucial in relation to safety issues. This study aimed to evaluate the maintenance of RTE rocket salad freshness packed under modified atmospheres. A portable E-nose, the electrolyte leakage test (which measures the index of leaf damage—ILD), and NIR spectroscopy and Aquaphotomics were employed. Two trials were carried out, using the following gas mixtures: (A) atmospheric air (21% O2, 78% N2); (B) 30% O2, 70% N2; (C) 10% CO2, 5% O2, 85% N2. Samples were stored at 4 °C and analyzed at 0, 1, 4, 7, 11, and 13 days. ANOVA, PCA, PLS were applied for data processing. E-nose and ILD results identified the B atmosphere as the best for maintaining product freshness. NIR spectroscopy was able to group the samples according to the storage time. Aquaphotomics proved to be able to detect changes in the water structure during storage. These preliminary data showed a good agreement NIR/ILD suggesting the use of NIR for non-destructive monitoring of the damage to the plant membranes of RTE rocket salad.

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts.

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

Design and construction of a portable e-nose system for human exhaled breath VOC analysis

This paper describes the design and fabrication of a system that can analyze the human exhaled breath volatile organic compounds (VOCs). The expelled breath contains several VOCs in addition to nitrogen, oxygen, carbon dioxide, and water vapor. Some of these VOCs are biomarkers for certain lung diseases like lung cancer, cystic fibrosis, chronic obstructive pulmonary disease (COPD), etc, and they can even identify smokers and non-smokers by variations of VOCs. The development of the electronic nose (e-nose) with five metal oxide semiconductor (MOS) gas sensors, control circuit, and processing circuit is explained in this paper. The system is tested and verified in the detection of breath VOCs. The clinical feasibility of the system is checked in a small pilot study involving 4 lung cancer patients, 4 chronic obstructive pulmonary disease patients, 4 asthma patients, and 4 healthy controls including smokers and non-smokers. The results of the study are also included, along with the conclusions drawn from the work. The e-nose system developed with TGS gas sensors is economical and uses a non-invasive method of breath sampling, and surely it will be a diagnostic tool for the detection of lung cancer, COPD, and other major respiratory diseases in near future.

A Portable E-Nose Endowed With Subjective Evaluation Function of Air Quality in Vehicles

In-vehicle air pollution, especially the volatile organic compounds (VOCs) pollutants, is an important source of passenger discomforts and consumer complaints. In this work, we designed a portable instrument based on an electronic nose (e-nose) to address the task of subjective assessment of in-vehicle air quality (IAQ). Our instrument consists of three modules: 1) the preconcentrating module that concentrates the VOCs to detectable level through adsorption and desorption; 2) the sensing module that measures the odor fingerprints of the target gas with an array of metal oxide semiconductor sensors; 3) the processing module that classifies the measured responses into the corresponding subjective grades via a stacking-based ensemble learning method. A dataset for subjective IAQ assessment was built to train and test the e-nose, which contains gas sample responses measured by the instrument and labels obtained by jury tests. Grading experiment results validated the practical performance of the proposed instrument as a direct case of narrowing gaps between objective assessments and subjective perceptions based on instrumentation.

Electronic nose versus quadrupole mass spectrometry for identifying viral hepatitis C patients.

Hepatitis C is a leading cause of liver disease and transplantation and is a significant burden on public health worldwide. This study aimed to apply the Electronic Nose (E-Nose) and quadrupole Mass Spectrometry (MS/MS) technologies for screening blood samples from hepatitis C patients and healthy controls. We analysed volatile organic compounds (VOCs) in the headspace over blood samples to identify those VOCs characteristic for diagnosing hepatitis C patients. The study comprised 150 acute hepatitis C patients with age range: 24-59years, and mean age ±SD: 41.5±12.8years and 150 age-matched healthy controls (age range: 24-51 and mean age: 40.11±4.89years) from the Hospital of the Medical Research Institute, Alexandria University, Alexandria, Egypt. Collected blood samples were analysed qualitatively and quantitatively using the E-Nose and MS/MS techniques, respectively. Principal component analysis of the E-Nose 10-sensor responses accurately classified blood samples from hepatitis C patients and healthy controls. The first two principal components explained over 98.35% of the variance in signals with no false-positive (healthy controls) or false-negative (hepatitis C patients) results. MS/MS showed two fragmentation ions at m/z of 104 and 151Da with the positive electrospray ionization mode (ESI+) in blood samples for hepatitis C patients, but not for healthy controls or background water samples. We identified the two specific fragmentation ions at m/z 104 and m/z 151 Da as malonic acid (MF: C3 H4 O4 ; MW: 104.06g/mol) and monosaccharide pentose (MF: C5 H10 O5 ; MW: 150.13g/mol) in VOCs of the headspace over blood samples for hepatitis C patients. This provides a rationale for developing diagnostic tests for hepatitis C virus based on altered trace VOCs concentrations using the relatively inexpensive, easy-to-use, portable and non-invasive E-Nose technology.

Development of Portable E-Nose System for Fast Diagnosis of Whitefly Infestation in Tomato Plant in Greenhouse

An electronic nose (E-nose) system equipped with a gas sensor array and real-time control panel was developed for a fast diagnosis of whitefly infestation in tomato plants. Profile changes of volatile organic compounds (VOCs) released from tomato plants under different treatments (i.e., whitefly infestation, mechanical damage, and no treatment) were successfully determined by the developed E-nose system. A rapid sensor response with high sensitivity towards whitefly-infested tomato plants was observed in the E-nose system. Results of principal component analysis (PCA) and hierarchical clustering analysis (HCA) indicated that the E-nose system was able to provide accurate distinguishment between whitefly-infested plants and healthy plants, with the first three principal components (PCs) accounting for 87.4% of the classification. To reveal the mechanism of whitefly infestation in tomato plants, VOC profiles of whitefly-infested plants and mechanically damaged plants were investigated by using the E-nose system and GC-MS. VOCs of 2-nonanol, oxime-, methoxy-phenyl, and n-hexadecanoic acid were only detected in whitefly-infested plants, while compounds of dodecane and 4,6-dimethyl were only found in mechanically damaged plant samples. Those unique VOC profiles of different tomato plant groups could be considered as bio-markers for diagnosing different damages. Moreover, the E-nose system was demonstrated to have the capability to differentiate whitefly-infested plants and mechanically damaged plants. The relationship between sensor performance and VOC profiles confirmed that the developed E-nose system could be used as a fast and smart device to detect whitefly infestation in greenhouse cultivation.

Predicting chilling requirement of peach floral buds using electronic nose

Chilling requirement is an important agronomic trait of peach and other deciduous fruit trees. It is a quantitative indicator of natural dormancy and its release in a low temperature environment. It is directly related to the distribution area of fruit trees and the success of cultivation. Therefore, chilling requirement evaluation is very important for cultivar breeding and plant introduction. The existing evaluation method has obvious disadvantages such as harsh conditions, complex operation and time-consuming, which greatly prolong the evaluation process. Chilling requirement of peach floral buds could be quickly estimated by a portable E-nose (PEN3.5). The ratio R of the actual accumulated chilling hours during any period of the dormancy and release process to the chilling requirement was used to indicate the stage of chilling accumulation. 5 stages were set as the R value was 0 (StageⅠ), 0.4 (Stage Ⅱ), 0.8 (Stage Ⅲ), 1.0 (Stage Ⅳ) and 1.2 (Stage Ⅴ). Volatile compounds from the floral buds of 9 cultivars with known chilling requirements at the five stages were tested, and the signals of 10 types of sensors were different. Broad-methane gradually decreased during the dormancy process, and reached the bottom at dormancy release, with a significant difference in each stage (p<0.05), while it increased significantly after dormancy release (p<0.05). The sulphur-organic compound showed a significant decrease in the early and mid-term of dormancy (p<0.05), while stayed in relatively high content during and after the release (p<0.05). Methane-aliph slightly increased during the dormancy and reached the highest content at dormancy release, which was significantly different from that in the initial period of dormancy (p<0.05), and significantly decreased after the dormancy was released (p<0.05). So the three types of compounds could be used as the metabolic markers for different stages of dormancy and release. PCA and LDA analysis indicated that the 5 stages could be clearly distinguished. PLSR analysis was performed on the R value and volatile compounds content indicating by the response of sensors, and the quantitative model was established. Cultivars with different chilling requirements were used to validate the model, and it was found that there was no significant difference between predicted chilling accumulation and chilling requirement estimated by traditional method (p>0.05). It proved that the model established in this paper can be used to evaluate the chilling requirement of peach floral buds. This is the first time that E-nose detection and PLSR quantitative analysis model are combined to be used in the estimation of chilling requirements and have achieved efficient and convenient evaluation. It has a high potential to be applied in the evaluation of peach chilling requirements in the future. And for other deciduous fruit trees or plants, the innovative method is of great reference significance.

Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade.

Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument

The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%.

An electronic nose supported by an artificial neural network for the rapid detection of aflatoxin B1 and fumonisins in maize

Mycotoxins pose a significant threat to the safety of food and its products. A rapid, reliable, and cheap method of testing for the most important regulated mycotoxins would be useful and time saving. This study aimed to evaluate the potential use of an electronic nose (e-nose) for rapid identification of mycotoxin contamination above legal limits in maize samples. A total of 316 maize samples were collect from a commercial field in Northern Italy from 2014 to 2018 and analyzed for contamination with aflatoxin B1 (AFB1) and fumonisins (FBs), both using a conventional method (HPLC-MS) and a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany) equipped with a 10-metal oxide sensor array. Artificial neural network (ANN), logistic regression (LR), and discriminant analysis (DA) were used to investigate whether the e-nose was capable of separating samples contaminated at levels above or below the legal limits, either for AFB1 or FBs. All the methodologies used showed high accuracy (≥70%) in distinguishing maize grain contamination above or below the legal limit. Notably, ANN performed better than the other methods, with 78% and 77% accuracy for AFB1 and FBs, respectively. This was the first time that five years of data and three different statistical approaches have been adopted to check e-nose performance. Results suggest that the e-nose supported by ANN could be a rapid and reliable tool for the detection of AFB1 and FBs in maize.

Implementation of a Portable E-Nose System to Identify the Water Stresses of Khasi Mandarin Orange Plants

Implementation of a Portable E-Nose System to Identify the Water Stresses of Khasi Mandarin Orange Plants