Sustainable innovations in e-nose sensor arrays for meat and seafood preservation: Advances in volatile compound detection

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.

Early prediction of cancer is crucial for effective treatment decisions. Stomach cancer is one of the worst malignancies in the world because it does not reveal the growth in symptoms. In recent years, non-invasive diagnostic methods, particularly exhaled breath analysis, have attracted interest in detecting stomach cancer. This review discusses invasive and non-invasive diagnostic methods for stomach cancer, with a special emphasis on breath analysis and electronic nose (e-nose) technology. Various analytical methods have been used to analyze volatile organic compounds (VOCs) associated with stomach cancer. Gas chromatography-mass Spectrometry is one of the most widely used techniques. These techniques enable the detection and analysis of VOCs, offering a promising route for early stomach cancer diagnosis. The e-nose system has been introduced as a cost-effective and portable alternative for VOC detection in stomach cancer to overcome the challenges associated with conventional methods. This review discusses the advantages and disadvantages of the e-nose system. This review recommends that e-nose sensors, combined with advanced pattern recognition techniques, be utilized to enable rapid and reliable diagnosis of stomach cancer.

An electronic nose (E-nose) system equipped with a sensitive sensor array was developed for fast diagnosis of aphid infestation on greenhouse tomato plants at early stages. Volatile organic compounds (VOCs) emitted by tomato plants with and without aphid attacks were detected using both the developed E-nose system and gas chromatography mass spectrometry (GC-MS), respectively. Sensor performance, with fast sensor responses and high sensitivity, were observed using the E-nose system. A principle component analysis (PCA) indicated accurate diagnosis of aphid-stressed plants compared to healthy ones, with the first two PCs accounting for 86.7% of the classification. The changes in VOCs profiles of the healthy and infested tomato plants were quantitatively determined by GC-MS. Results indicated that a group of new VOCs biomarkers (linalool, carveol, and nonane (2,2,4,4,6,8,8-heptamethyl-)) played a role in providing information on the infestation on the tomato plants. More importantly, the variation in the concentration of sesquiterpene VOCs (e.g., caryophyllene) and new terpene alcohol compounds was closely associated with the sensor responses during E-nose testing, which verified the reliability and accuracy of the developed E-nose system. Tomato plants growing in spring had similar VOCs profiles as those of winter plants, except several terpenes released from spring plants that had a slightly higher intensity.

In this study, we proposed a portable electronic nose (e-nose) system based on a microcontroller (MSP430-FG439) combined with a tin oxide ( SnO2 ) gas sensor, which was heated by a cyclic heating method, to recognize the volatile organic compounds (VOCs). We had demonstrated that this e-nose system can classify and quantify VOCs, such as methanol and ethanol. The sensitivity of the e-nose system had good linearity in the concentration range of 10–40 ppm of these two VOCs, respectively. This portable e-nose system was implemented with a microcontroller acted as CPU, an LCD for displaying information of gases in real time, a wireless communication system, ZigBee, and a warning system.

. A portable electronic nose (E-nose) system equipped with a sensitive sensor array was successfully developed to detect insect-stressed tomato plants, which were infested by aphids and whiteflies for 2 to 3 days, by taking advantages of their unique volatile organic compounds (VOCs) profiles. With showing fast sensor responses, an accurate diagnosis of aphids-stressed, whiteflies-stressed tomato plants from healthy groups were verified with PCA results accounting for 86% classification, which confirmed the promising capability of E-nose system providing a fast and reliable detection of infested tomato plants at early stage. For comparison, the changes of VOCs profiles were quantitatively determined by gas chromatography-mass spectrometry (GC-MS), with results showing that a group of new VOCs biomarkers (methyl salicylate and several terpenes) played info-chemical roles in the tomato-aphids interaction and tomato-whiteflies interaction, respectively. Moreover, the variation of the concentration of VOCs compounds explained the sensors behaviors during E-nose test, which confirmed the reliability and accuracy of the developed E-nose system. The satisfactory diagnosis among unstressed and insect stressed tomato plants as well as samples between, demonstrated the E-nose system had promising potential for a smart insect control at early stages in greenhouse.

The integration of sensor technology and artificial intelligence (AI) is transforming agriculture, particularly in post-harvest management. This study focuses on utilizing an electronic nose (e-nose) system in conjunction with machine learning (ML) models to monitor and detect potato spoilage in storage environments. The e-nose system, equipped with sensitive gas sensors, detects volatile organic compounds (VOCs) emitted by potatoes during different spoilage stages. By analyzing these emissions, the system can identify early signs of spoilage, offering a valuable solution for mitigating post-harvest losses, which remain a significant challenge in the agricultural sector. Through a series of controlled experiments, VOCs were captured and analyzed using a neural network model, classifying the potatoes into three categories: fresh, mildly spoiled, and fully spoiled. The neural network was trained on data from multisensory gas analysis, achieving a high level of classification accuracy. This study demonstrates that the integration of e-nose technology and ML algorithms can effectively monitor potato quality in storage, providing real-time insights to optimize storage conditions, extend shelf life, and reduce wastage.

The increasing use of pesticides and fertilizers in agricultural practices poses substantial consumer risks. This study explores the utilization of an E-Nose (electronic nose) system augmented with machine learning (ML) to identify fertilizers and pesticides in plants. E-Nose technology effectively analyzes the volatile organic compounds (VOCs) emitted by plants at different conditions. The principle behind E-Nose technology involves using a gas sensor array to record and monitor VOC emissions from plant samples. These emissions are then analyzed using ML models to identify patterns related to pesticide and fertilizer treatments. One significant advantage of this technology is its capability to detect chemical residues without direct contact with the plants, making it a safer and more efficient alternative to traditional chemical analysis. The scope of application of E-nose technology extends to various agricultural monitoring requirements, particularly in identifying the fertilizer and pesticide applications in plants. In this study, the spinach plants were classified into three categories-organic, lightly fertilized, and heavily fertilized. Similarly, the cauliflower plants were exposed to different concentrations of a pesticide mixture containing 40% profenofos and 4% cypermethrin. VOC readings were taken after specific growth periods to capture the effects of these treatments. The generated data was classified using ML models, SVM (Support Vector Machine) and random forest, achieving accuracies of 90.9% for pesticide classification and 99.5% in distinguishing plants treated with fertilizers of different concentrations. The E-Nose technology offers a rapid and cost-effective solution for real-time agricultural monitoring.

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

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

Authenticating gelatin sources is essential for consumers, particularly those with dietary restrictions or religious concerns regarding pork-derived ingredients. Porcine gelatin, widely used in food and pharmaceutical products, poses considerable challenges for authentication due to its prevalence and the difficulty of detecting it, especially in processed products. In this study, we developed and evaluated an integrated electronic nose (e-nose) system with a Recurrent Neural Network (RNN) to detect and classify gelatin type based on their sources. The e-nose system utilized an array of gas sensors to capture the unique volatile organic compounds (VOCs) associated with each gelatin type, which was subsequently classified by the RNN. The classification performance of the integrated 7-module e-nose system showed promising results based on time points after sample preparation, with accuracy, sensitivity, and AUC of 96.3%, 96.6%, and 98.2% at the 0-hour point, respectively, rising to 99.1% for all three metrics at 2-hour point. The sensitivity of the system also showed an increase over time for single gelatin samples, from 100%, 97.8%, and 91.9% to 98.6%, 99.3%, and 99.3% for pig-derived, cow-derived, and fish gelatin, respectively. For mixed gelatin samples, the system maintained high accuracy, sensitivity, and AUC at 98.2%, 97.9%, and 98.1%, respectively. In conclusion, the integrated e-nose system demonstrates the potential for robust performance in gelatin authentication, paving the way for more efficient and reliable methods of halal food authentication.

Rationale: Analysis of exhaled volatile organic compounds (VOCs) by sensor-driven electronic nose (eNose) technology is a widely suggested measure for non-invasive monitoring of chronic airway diseases. While this technology allows probabilistic (clinical) advise, it is incapable of providing details regarding involved metabolites. Modelling sensor deflections by matching Gas Chromatography-Mass Spectrometry (GC-MS) samples could help to ascertain which VOCs induce a sensor response. Objective: To determine the association between eNose sensor deflections and exhaled VOCs measured by GC-MS. Methods: Paired samples of breath from asthma patients (n=22) were collected on sorbent tubes. One tube was analysed by four different eNoses and the second one by GC-MS. Pooling of resulting datasets consisted of 1) Partial Least Square Regression (PLSR) analysis and 2) clustered heat-mapping of PLSR loadings. Results: Matching data was available for 158 eNose sensors and 1025 GC-MS features, whereby PLSR resulted in R2‘s from 0.25 to 0.72. Figure 1 shows clustered heat-mapping outcomes. Conclusion: This explorative analysis revealed distinctive and associated patterns of exhaled VOCs between eNose and GC-MS. This data matching could help to identify which VOCs are responsible for clinically relevant results obtained by eNose and will facilitate valorisation of breathomics into clinical tests.

The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify different volatile components leading to the diagnosis of lung cancer at an early stage. In this preliminary study, a commercialized E-nose consists of an array of 32 conducting polymer sensors (Cyranose 320) was used to detect and discriminate the VOCs emitted from cancer cells which is A549 (lung cancer cell line) between MCF7 (breast cancer cell line). Blank medium was used to obtain controlled value. The VOC profiles of each sample were characterized using a classification algorithm called k-Nearest Neighbors (KNN) to test and benchmark the performance of Enose in identifying VOCs of lung cancer from different cancer cell lines. The E-nose with KNN classifier was able to classify the VOCs of lung cancer cell with over 90% successful accuracy in 30 seconds. This study can conclude that e-nose is capable to rapidly discriminate volatile organic compounds of cancerous cells which generated during cell growth.

Electronic Nose (ENose) technology has emerged as a transformative tool in medical diagnostics, leveraging sensor arrays that mimic the human olfactory system to detect odors and volatile organic compounds (VOCs) in various biological samples. ENose systems utilize a range of sensor types, such as metal oxide semiconductors and conducting polymers, to generate unique “smell fingerprints” through pattern recognition algorithms. These systems have shown promise in diagnosing various medical conditions, including respiratory diseases, infectious diseases, metabolic disorders, and neurological conditions. Notably, ENose technology holds significant promise in cancer diagnostics, offering a non-invasive, cost-effective, and rapid approach to early detection and monitoring. It has demonstrated impressive accuracy (85%-95%) in detecting cancers and monitoring complications. However, challenges remain, including issues with standardization, sensor sensitivity, and data interpretation. Despite these hurdles, ENose technology’s market growth is fueled by the increasing prevalence of chronic diseases. Recent developments in Artificial Intelligence (AI), particularly machine learning techniques like deep learning, have enhanced the diagnostic accuracy and robustness of ENose devices. This paper explores the evolution, core principles, applications, challenges, and future potential of ENose technology, with particular emphasis on integrating recent advancements in AI for enhanced detection and interpretation. Future research and collaboration across sectors are essential to overcome existing challenges and integrate ENose into mainstream healthcare.

VOCs (volatile organic compounds) can be used as a biomarker of placebo phenomenon, such as stress, panic disorder, health conditions, and many others. VOCs from the exhaled breath have different concentration levels that are also related to many health diseases. However, the use of VOCs as biomarkers in exhaled breath are very limited. Hence, this study aims to develop a novel e-nose (electric nose) system based on a VOC measurement system to identify placebo phenomenon in emission areas. For this purpose, a digital semiconductor VOC sensor and a microcontroller were used to detect VOC level. The developed system was tested inside a chamber for the initial calibration and comparation steps using fresh air and a comparator device. After calibration, the system was used to measure the VOC concentrations of 20 exhaled breath samples in the emission sampling areas (control and emission sources). In other sides, the VOC levels surrounding the emission areas were also measured using the comparator device. The placebo levels (PLS) of the exhaled breath samples were divided into PLS(-) or placebo negative and PLS(+) or placebo positive related to the placebo conditions. The sampling areas were divided into indoor and outdoor areas to identify the different placebo percentages and the dependence related to the emission levels. The results show that the emission levels of the emission sources are about 504-528 ppb, meanwhile, the control area (clean area) has <10 ppb of VOC levels. A higher VOC concentration, a higher PLS(+) percentage. The exhaled breath of PLS(+) samples contain >78 ppb of VOC levels, while PLS(-) samples has < 78 ppb of VOC levels (p < 0.05). It can be concluded that VOC concentrations in the emission sources has a potential to influence the placebo quantification in human psycological health. The developed e-nose system can be used to identify VOC levels as a biomarker of a placebo phenomenon.

Strawberries are susceptible to mechanical damage. The detection of damaged strawberries by their volatile organic compounds (VOCs) can avoid the deficiencies of manual observation and spectral imaging technologies that cannot detect packaged fruits. In the present study, the detection of strawberries with impact damage is investigated using electronic nose (e-nose) technology. The results show that the e-nose technology can be used to detect strawberries that have suffered impact damage. The best model for detecting the extent of impact damage had a residual predictive deviation (RPD) value of 2.730, and the correct rate of the best model for identifying the damaged strawberries was 97.5%. However, the accuracy of the prediction of the occurrence time of impact was poor, and the RPD value of the best model was only 1.969. In addition, the gas chromatography–mass spectrophotometry analysis further shows that the VOCs of the strawberries changed after suffering impact damage, which was the reason why the e-nose technology could detect the damaged fruit. The above results show that the mechanical force of impact caused changes in the VOCs of strawberries and that it is possible to detect strawberries that have suffered impact damage using e-nose technology.