Use Of Electronic Nose Research Articles

Factors affecting scum formation and the use of electronic nose to identify fundamental odor properties in urban estuaries

Factors affecting scum formation and the use of electronic nose to identify fundamental odor properties in urban estuaries

Use of Electronic Nose to Identify Levels of Cooking Cookies

Use of Electronic Nose to Identify Levels of Cooking Cookies

Diagnostic performance of eNose technology in COVID-19 patients after hospitalization

BackgroundVolatile organic compounds (VOCs) produced by human cells reflect metabolic and pathophysiological processes which can be detected with the use of electronic nose (eNose) technology. Analysis of exhaled breath may potentially play an important role in diagnosing COVID-19 and stratification of patients based on pulmonary function or chest CT.MethodsBreath profiles of COVID-19 patients were collected with an eNose device (SpiroNose) 3 months after discharge from the Leiden University Medical Centre and matched with breath profiles from healthy individuals for analysis. Principal component analysis was performed with leave-one-out cross validation and visualised with receiver operating characteristics. COVID-19 patients were stratified in subgroups with a normal pulmonary diffusion capacity versus patients with an impaired pulmonary diffusion capacity (DLCOc < 80% of predicted) and in subgroups with a normal chest CT versus patients with COVID-19 related chest CT abnormalities.ResultsThe breath profiles of 135 COVID-19 patients were analysed and matched with 174 healthy controls. The SpiroNose differentiated between COVID-19 after hospitalization and healthy controls with an AUC of 0.893 (95-CI, 0.851–0.934). There was no difference in VOCs patterns in subgroups of COVID-19 patients based on diffusion capacity or chest CT.ConclusionsCOVID-19 patients have a breath profile distinguishable from healthy individuals shortly after hospitalization which can be detected using eNose technology. This may suggest ongoing inflammation or a common repair mechanism. The eNose could not differentiate between subgroups of COVID-19 patients based on pulmonary diffusion capacity or chest CT.

Diagnosis of pathological conditions through electronic nose analysis of urine samples: a systematic review and meta-analysis.

Currently available urinalysis methods are often applied for screening and monitoring of several pathologies. However, traditionally analyzed biomarkers in urinalysis still lack sensitivity and specificity to accurately diagnose some diseases. Several studies have proposed the use of electronic noses (eNoses) for the analysis of volatile organic compounds in urine samples that may, directly or indirectly, correlate with certain pathologies. Hence, the aim of this study was to perform a systematic review and meta-analysis of studies concerning the use of portable electronic noses for diagnosis or monitoring of pathologies through analysis of urine samples. A systematic review of the literature was held according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Twenty-four articles met the inclusion criteria and were included in the analysis. The results of the revised studies showed that there are various volatile organic compound profiles, identified through eNose analysis, that may be applied for diagnosis or monitoring of several diseases, such as diabetes, urinary tract infection, inflammatory bowel disease, and kidney disease. A meta-analysis was conducted taking into consideration the data of 10 of the initial 24 articles. The pooled sensitivity, specificity, and diagnostic odds ratio were 84% (95% CI, 0.72-0.92), 85% (95% CI, 0.75-0.91), and 24.17 (95% CI: 7.85-74.41), respectively. The area under the receiver operating characteristic curve was 0.897. These results suggest that eNose technology has adequate diagnostic accuracy for several pathologies and could be a promising screening tool for clinical settings. However, more studies are needed to reduce heterogeneity between results.

Classification of Three Volatiles Using a Single-Type eNose with Detailed Class-Map Visualization.

The use of electronic noses (eNoses) as analysis tools are growing in popularity; however, the lack of a comprehensive, visual representation of how the different classes are organized and distributed largely complicates the interpretation of the classification results, thus reducing their practicality. The new contributions of this paper are the assessment of the multivariate classification performance of a custom, low-cost eNose composed of 16 single-type (identical) MOX gas sensors for the classification of three volatiles, along with a proposal to improve the visual interpretation of the classification results by means of generating a detailed 2D class-map representation based on the inverse of the orthogonal linear transformation obtained from a PCA and LDA analysis. The results showed that this single-type eNose implementation was able to perform multivariate classification, while the class-map visualization summarized the learned features and how these features may affect the performance of the classification, simplifying the interpretation and understanding of the eNose results.

Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath

There has been a growing interest in the use of electronic noses (e-noses) in detecting volatile organic compounds in exhaled breath for the diagnosis of cancer. However, no systematic evaluation has been performed of the overall diagnostic accuracy and methodologic challenges of using e-noses for cancer detection in exhaled breath. To provide an overview of the diagnostic accuracy and methodologic challenges of using e-noses for the detection of cancer. An electronic search was performed in the PubMed and Embase databases (January 1, 2000, to July 1, 2021). Inclusion criteria were the following: (1) use of e-nose technology, (2) detection of cancer, and (3) analysis of exhaled breath. Exclusion criteria were (1) studies published before 2000; (2) studies not performed in humans; (3) studies not performed in adults; (4) studies that only analyzed biofluids; and (5) studies that exclusively used gas chromatography-mass spectrometry to analyze exhaled breath samples. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies 2. Generalized mixed-effects bivariate meta-analysis was performed. Main outcomes were sensitivity, specificity, and mean area under the receiver operating characteristic curve. This review identified 52 articles with a total of 3677 patients with cancer. All studies were feasibility studies. The sensitivity of e-noses ranged from 48.3% to 95.8% and the specificity from 10.0% to 100.0%. Pooled analysis resulted in a mean (SE) area under the receiver operating characteristic curve of 94% (95% CI, 92%-96%), a sensitivity of 90% (95% CI, 88%-92%), and a specificity of 87% (95% CI, 81%-92%). Considerable heterogeneity existed among the studies because of differences in the selection of patients, endogenous and exogenous factors, and collection of exhaled breath. Results of this review indicate that e-noses have a high diagnostic accuracy for the detection of cancer in exhaled breath. However, most studies were feasibility studies with small sample sizes, a lack of standardization, and a high risk of bias. The lack of standardization and reproducibility of e-nose research should be addressed in future research.

The Use of Electronic Nose for the Classification of Blended and Single Malt Scotch Whisky

As with any profitable industry, the whisky market is subject to fraudulent activity, including adulteration. An expert can identify the differences between whiskies, but it is difficult for the majority of consumers to differentiate fraudulent beverages. Complex chemical and analytical analyses have been able to detect the differences between whiskies; however, this type of analysis is time-consuming, complex, requires trained professionals, and can only be conducted in the laboratory. A rapid and real-time assessment of whisky quality could prove beneficial to wholesalers and consumers. The odour of whiskies can be used to identify their brands, regions and styles, as thus has the potential for quality assessment and fraudulent detection. One type of technology used for real-time odour analysis is an electronic nose (e-nose). This study investigates the capability of a new e-nose prototype (called NOS.E) developed by our team to identify the differences between six whiskies with respect to their brand names, regions, and styles. This study investigates the capability of a new e-nose prototype (called NOS.E) developed by our team in identifying the differences among whiskies. Ensemble of several classifiers is adopted to improve the classification accuracy of the system. The proposed e-nose solution was verified by a field testing displayed at the CEBIT Australia 2019 trade show, by reaching an accuracy of 96.15%, 100%, and 92.31% in brand name, region, and style classification, respectively. Confirmation of the NOS.E findings was further carried out using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> GC-TOFMS).

An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients.

Background: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. Methods: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. Results: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). Conclusions: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.

The Use of Electronic Nose in the Quality Evaluation and Adulteration Identification of Beijing-You Chicken.

The objective of this study was to reveal the secrets of the unique meat characteristics of Beijing-you chicken (BJY) and to compare the difference of quality and flavor with Luhua chicken (LH) and Arbor Acres broiler (AA) at their typical market ages. The results showed the meat of BJY was richer in essential amino acids, arachidonic acid contents, inosine monophosphate (IMP), and guanosine monophosphate (GMP). The total fatty acid and unsaturated fatty acid contents of BJY chicken and LH chicken were lower than that of AA broilers, whereas the ratios of unsaturated fatty acids/saturated fatty acids (2.31) and polyunsaturated fatty acids/monounsaturated fatty acids (1.52) of BJY chicken were the highest. The electronic nose and SPME-GC/MS analysis confirmed the significant differences among these three chickens, and the variety and relative content of aldehydes might contribute to a richer flavor of BJY chicken. The meat characteristics of BJY were fully investigated and showed that BJY chicken might be favored among these three chicken breeds with the best flavor properties and the highest nutritional value. This study also provides an alternative way to identify BJY chicken from other chickens.

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.

Electronic Nose, Tongue and Eye: Their Usefulness for the Food Industry

Background: The electronic nose, tongue, and eye are futuristic technologies that have been used for many years; they have been gaining market in different types of industries and can increasingly be found in the food area; their function is to determine sensory characteristics (smell, aroma, and flavor) and objective visuals, without the subjectivity that can be represented by sensory analysis by people (the study that can complement the analysis of machines, without being exclusive). Objectives: Find the main generalities of these mechanisms, their sensors, software, mechanism of action, and applications within the food industry. Methods: A search was carried out in the main databases of indexed articles, with terms that allowed collecting the necessary information, and 89 articles were used that met different inclusion criteria. Results: The main outcomes were to understand the operation of each of these technologies, what their main components are, and how they can be linked in the beer, wine, oil, fruit, vegetable, dairy, etc. industry to determine their quality, safety, and fraud. Conclusions: The use of electronic nose, tongue, and eye is found in more food industries every day. Its technology continues to evolve; the future of sensory analysis will undoubtedly apply these mechanisms due to the reliability, speed, and reproducibility of the results.

A Concentration-Based Drift Calibration Transfer Learning Method for Gas Sensor Array Data

The practical use of electronic nose (e-nose) systems suffers from drift issue, which alters data distribution and reduces the accuracy of classification. This letter proposes a transfer learning method called concentration-based drift calibration (CDC) for calibrating the sensor drift. Based on the sensor characteristic that sensor response is correspondent to gas concentration, transfer samples were collected in the target domain for certain gas concentrations and then used for calibration with a designed concentration-based model and CDC transfer process. This method was evaluated on a complex time-varying drift dataset. The experimental results show that the proposed method for drift calibration is effective and can be used for real-world applications. Moreover, the CDC transfer process can be applied over time with data that has been previously collected to yield a more generalized model.

Electronic nose to distinguish bladder cancer by urinary odour feature: A pilot study.

No study has yet investigated the use of electronic nose (eNose) technology to reveal pattern recognition of urological diseases, including bladder cancer. We sought to determine the diagnostic performance of the eNose in recognizing urinary odour in patients with bladder cancer. The eNose is a commercially available model equipped with two sensors. The angle of the two sensors (θ) depends on the kinds of chemical substances, thus defining θ as the feature of odour. Quantity of odour is the number of θ detected during a measurement. Urine samples were from 36 untreated patients with bladder cancer, 29 with urolithiasis, 10 with urinary tract infection (UTI), and 27 healthy volunteers. Based on ROC analysis of the quantity in patients with bladder cancer, an optimal cut-off value for θ of 49, 48, and 55 was applied to compare with samples from the healthy volunteer, urolithiasis and UTI groups, respectively. There were significantly differences between bladder cancer and the other conditions using these specific points (p< 0.0001, respectively). The resulting diagnostic sensitivity was 61.4%, 45.6%, and 60.8%, and specificity was 52.8%, 68.4%, and 90.2%, respectively. The AUC for bladder cancer was 0.565, 0.548, and 0.909, respectively. The eNose is a small, portable, rapid, low cost, and noninvasive instrument for distinguishing bladder cancer from other benign conditions.

Detection of Water Stress in Khasi Mandarin Orange Plants from Volatile Organic Compound Emission Profile Implementing Electronic Nose

Plants in the absence of an innate immune system like animals and being immobile are regularly exposed to a host of stresses, ranging from biotic to abiotic stresses. In response to these, plants have developed a complicated response system like reprogramming gene expressions and emission of secondary metabolites as volatile organic compounds (VOCs) by its various tissues like roots, stems, leaves etc. These VOCs can be used as biomarkers for inspecting plants’ in situ health status. This paper address the usefulness of electronic nose (e-nose) system to sense the VOCs emitted by plants’ leaves to detect the stresses in it. Standard commercial electronic nose (e-nose) system Alfa Mos Fox 3000 has been used here to identify the stressed and non-stressed plants. Fifteen Mandarin orange plants were considered for the study and were subdivided into three categories. Each one was subjected to a different level of water stress. Leaf samples were collected for e-nose analyses from each plant of all three categories on the 15th day and 30th day of induction of water stresses. Dimensionality reduction techniques like kernel Principal Component Analysis (kPCA), Linear Discriminant Analysis (LDA) and classification algorithms like Support Vector Machines (SVC) and Multi-Layer Perceptron Classifier (MLPC) have been used to classify the three categories of plants. The scores obtained from these analyses reveals the feasibility of using an e nose system in discriminating plants based on the status of water stress in them. This paper analyses the applicability of e nose system in stress diagnosis of agricultural and horticultural crops, which would significantly help in controlling the irrigation regime.

Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank.

Background: There is an increasing interest in employing electronic nose technology in the diagnosis and monitoring of lung diseases. Interstitial lung diseases (ILD) are challenging in regard to setting an accurate diagnosis in a timely manner. Thus, there is a high unmet need in non-invasive diagnostic tests. This single-center explorative study aimed to evaluate the usefulness of electronic nose (Aeonose®) in the diagnosis of ILDs. Methods: Exhaled volatile organic compound (VOC) signatures were obtained by Aeonose® in 174 ILD patients, 23 patients with chronic obstructive pulmonary disease (COPD), and 33 healthy controls (HC). Results: By dichotomous comparison of VOC’s between ILD, COPD, and HC, a discriminating algorithm was established. In addition, direct analyses between the ILD subgroups, e.g., cryptogenic organizing pneumonia (COP, n = 28), idiopathic pulmonary fibrosis (IPF, n = 51), and connective tissue disease-associated ILD (CTD-ILD, n = 25) were performed. Area under the Curve (AUC) and Matthews’s correlation coefficient (MCC) were used to interpret the data. In direct comparison of the different ILD subgroups to HC, the algorithms developed on the basis of the Aeonose® signatures allowed safe separation between IPF vs. HC (AUC of 0.95, MCC of 0.73), COP vs. HC (AUC 0.89, MCC 0.67), and CTD-ILD vs. HC (AUC 0.90, MCC 0.69). Additionally, to a case-control study design, the breath patterns of ILD subgroups were compared to each other. Following this approach, the sensitivity and specificity showed a relevant drop, which results in a poorer performance of the algorithm to separate the different ILD subgroups (IPF vs. COP with MCC 0.49, IPF vs. CTD-ILD with MCC 0.55, and COP vs. CT-ILD with MCC 0.40). Conclusions: The Aeonose® showed some potential in separating ILD subgroups from HC. Unfortunately, when applying the algorithm to distinguish ILD subgroups from each other, the device showed low specificity. We suggest that artificial intelligence or principle compound analysis-based studies of a much broader data set of patients with ILDs may be much better suited to train these devices.

Use of Electronic Noses for Diagnosis of Digestive and Respiratory Diseases through the Breath.

The increased occurrence of chronic diseases related to lifestyle or environmental conditions may have a detrimental effect on long-term health if not diagnosed and controlled in time. For this reason, it is important to develop new noninvasive early diagnosis equipment that allows improvement of the current diagnostic methods. This, in turn, has led to an exponential development of technology applied to the medical sector, such as the electronic nose. In addition, the appearance of this type of technology has allowed the possibility of studying diseases from another point of view, such as through breath analysis. This paper presents a bibliographic review of past and recent studies, selecting those investigations in which a patient population was studied with electronic nose technology, in order to identify potential applications of this technology in the detection of respiratory and digestive diseases through the analysis of volatile organic compounds present in the breath.

Electronic nose – an instrument for odour nuisances monitoring

An increasingly frequent problem of people living in urban agglomerations is the occurrence of odour nuisance. Although the source of these nuisances is different, their common feature is that they are a complex mixture of odour compounds with different odour thresholds. However, from a practical point of view, the most valuable would be a direct link between the odour intensity and the results of on-line analytical air monitoring. Such a possibility is created by the use of electronic noses (devices that are supposed to imitate the human sense of smell) to measure odours. The paper presents the use of an electronic nose combined with multiple liear regression model (MLR) to determine the odour intensity of the two-component mixture samples of commonly known odour compounds: trimethylamine (TMA) and triethylamine (TEA) in concentration range 50–200 ppm v/v. The obtained results were compared with the theoretical values determined using Zwaardemaker and euclidean additivity (EA) models. For high concentrations of substances in the mixtures (&gt; 150 ppm v/v), the masking effect was observed.

Use of Electronic Noses in Seawater Quality Monitoring: A Systematic Review.

Electronic nose (eNose) systems are particularly appreciated for their portability, usability, relative low cost, and real-time or near real-time response. Their application finds space in several domains, including environmental monitoring. Within this field, marine monitoring is of particular scientific relevance due to the fragility of this specific environment, daily threatened by human activities that can potentially bring to catastrophic and irreversible consequences on marine wildlife. Under such considerations, a systematic review, complying with the PRISMA guidelines, was conducted covering the period up to 15 October 2018, in PubMed, ScienceDirect, and Google Scholar. Despite the relatively low number of articles published on this specific topic and the heterogeneity of the technological approaches employed, the results obtained by the various groups highlight the positive contribution eNose has given and can provide in near future for the monitoring and safeguarding of this delicate environment.

Rice Odours’ Readings Investigation Using Principal Component Analysis

The use of electronic nose (e-nose) devices plus principal component analysis can help the process of categorizing the 16 different rice into its type. Generally, the physical feature of an e-nose own more than one hole to capture the odour of rice. For example, the portable e-nose so-called Insniff does have 10 holes (or variables). In this situations, we will have a dataset that consist high-dimension dataset where lead to the presence of interdependencies between all variables under study. Therefore, this study is presented to investigate the odour of rice for identifying the most important variables contributing to the rice odour readings. The principal component analysis (PCA) is implemented to determine the component that best represent the all 10 variables in order to eliminate the interdependency problem, and (2) to identify which variable is considered as important and influential to the newly-formed principle component (PC). The results from PCA suggested that the first two principle components is chosen. It is based on three assessments which are Kaiser’s criterion larger than 1, cumulative proportion of total variance, and scree plot. These two principle components explained 89% of total variance. Results showed that sensor 1 (0.931) and sensor 2 (0.966) are the two important variables that highly contribute to PC1. On the other hand, for PC2, the highest contribution is from sensor 8 (0.828). This study demonstrate that PCA is effective for investigating rice odour readings.

Use of electronic nose to discriminate meats from bulls fed diet with or without flaxseed inclusion and subjected to different aging periods

A metal-oxide sensors array electronic nose (e-nose) was used to discriminate beef loins (Longissimus thoracis) obtained from Piemontese bulls fed without or with flaxseed and subjected to 3 different aging periods (2, 7, 10 days) at 4°C. At 7 days of aging, samples were also assessed for flavor intensity by panelists. A comparison between e-nose and panel assessments was performed subjecting a 7 days e-nose reading on cooked meat to partial least square regression for flavor prediction. The e-nose could not discriminate populations in meat samples, however it could represent a valuable tool in supporting flavor scoring from sensory evaluation.