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As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

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Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

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Infectious diseases that spread through the bloodstream, known as bloodstream infections (BSIs), are a major global health problem. Positive outcomes for patients with sepsis are typically the result of prompt treatment started after an early diagnosis of BSIs. In this study, we evaluated the capabilities of a portable electronic nose (E-Nose) to detect BSIs with two commonly isolated Gram-negative bacterial species, E. coli and K. pneumonia. One hundred and five blood samples were randomly collected for blood culture examinations using BACTEC and VITEK 2 system, and headspace analysis by an E-Nose from June to December 2021. Classification accuracy of E. coli, K. pneumonia, and negative controls was measured using principal component analysis, area under the receiver operating characteristic curve, sensitivity, and specificity analysis. After incubation for 24 h, cluster plots generated using principal component analysis demonstrated that E-Nose could accurately diagnose the presence of E. coli and K. pneumonia in BACTEC blood culture bottles with a sensitivity and specificity of 100% in just 120 s. The E-Nose method has been shown to be an immediate, precise, and cost-effective alternative to automated blood culture BACTEC and VITEK 2 systems for the fast detection of the causative bacterial pathogens of BSIs in clinical practice. Thus, patients with such Gram-negative bacteremia can have guided empirical antimicrobial therapy on the same day of BSIs diagnosis, which can be lifesaving.

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Detecting volatile organic compounds is a fundamental step in water quality analysis. Methylisoborneol (MIB) provides a lousy odor to water, whereas geosmin (GEO) is responsible for its sour taste. A widely-used technique for their detection is gas-phase chromatography. On the other hand, an electronic nose from organic thin-film transistors is a cheaper and faster alternative. Poly(2,5-bis(3-tetradecyl-thiophen-2-yl)thieno[3,2-b]thiophene) (PBTTT-C14) features semiconducting properties suitable for organic electronics. However, in order to expose the active layer in a bottom-gate transistor structure with photolithographically patterned electrodes, a cross-linked dielectric such as poly(4-vinyl phenol) (PVP) is necessary. In this work, the cross-linking was demonstrated using FTIR and Raman spectroscopies, as well as high-k capacitors with a dielectric constant of 5.3. The presence of enhanced crystallinity with terrace formation in the semiconducting film was confirmed with UV-visible spectrophotometry, atomic force microscopy, and X-ray diffraction. Finally, for the first time, a PBTTT-C14 transistor on cross-linked PVP was shown to respond to isoborneol with a sensitivity of up to 6% change in mobility per ppm. Due to its similarity to MIB, a system comprising these sensors must be investigated in the future as a tool for sanitation companies in real-time water quality monitoring.

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Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin". For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation-extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.

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The objective of this work was to evaluate the use of an electronic nose and chemometric analysis to discriminate global patterns of volatile organic compounds (VOCs) in breath of postCOVID syndrome patients with pulmonary sequelae. A cross-sectional study was performed in two groups, the group 1 were subjects recovered from COVID-19 without lung damage and the group 2 were subjects recovered from COVID-19 with impaired lung function. The VOCs analysis was executed using a Cyranose 320 electronic nose with 32 sensors, applying principal component analysis (PCA), Partial Least Square-Discriminant Analysis, random forest, canonical discriminant analysis (CAP) and the diagnostic power of the test was evaluated using the ROC (Receiver Operating Characteristic) curve. A total of 228 participants were obtained, for the postCOVID group there are 157 and 71 for the control group, the chemometric analysis results indicate in the PCA an 84% explanation of the variability between the groups, the PLS-DA indicates an observable separation between the groups and 10 sensors related to this separation, by random forest, a classification error was obtained for the control group of 0.090 and for the postCOVID group of 0.088 correct classification. The CAP model showed 83.8% of correct classification and the external validation of the model showed 80.1% of correct classification. Sensitivity and specificity reached 88.9% (73.9%-96.9%) and 96.9% (83.7%-99.9%) respectively. It is considered that this technology can be used to establish the starting point in the evaluation of lung damage in postCOVID patients with pulmonary sequelae.

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The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

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The increasing demand for food production has necessitated the development of sensitive and reliable methods of analysis, which allow for the optimization of storage and distribution while ensuring food safety. Methods to quantify and monitor volatile and biogenic amines are key to minimizing the waste of high-protein foods and to enable the safe consumption of fresh products. Novel materials and device designs have allowed the development of portable and reliable sensors that make use of different transduction methods for amine detection and food quality monitoring. Herein, we review the past decade's advances in volatile amine sensors for food quality monitoring. First, the role of volatile and biogenic amines as a food-quality index is presented. Moreover, a comprehensive overview of the distinct amine gas sensors is provided according to the transduction method, operation strategies, and distinct materials (e.g., metal oxide semiconductors, conjugated polymers, carbon nanotubes, graphene and its derivatives, transition metal dichalcogenides, metal organic frameworks, MXenes, quantum dots, and dyes, among others) employed in each case. These include chemoresistive, fluorometric, colorimetric, and microgravimetric sensors. Emphasis is also given to sensor arrays that record the food quality fingerprints and wireless devices that operate as radiofrequency identification (RFID) tags. Finally, challenges and future opportunities on the development of new amine sensors are presented aiming to encourage further research and technological development of reliable, integrated, and remotely accessible devices for food-quality monitoring.

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The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC1 = 95.9%, PC2 = 1.0%, PC3 = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%-99.9%) and 100% (88.4%-100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects.

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Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

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BACKGROUND: We identified a global chemical pattern of volatile organic compounds in exhaled breath capable of discriminating between COVID-19 patients and controls (without infection) using an electronic nose. METHODS: The study focused on 42 SARS-CoV-2 RT-qPCR positive subjects as well as 42 negative subjects. Principal component analysis indicated a separation of the study groups and provides a cumulative percentage of explanation of the variation of 98.3%. RESULTS: The canonical analysis of principal coordinates model shows a separation by the first canonical axis CAP1 (r2 = 0.939 and 95.23% of correct classification rate), the cut-off point of 0.0089; 100% sensitivity (CI 95%:91.5-100%) and 97.6% specificity (CI 95%:87.4-99.9%). The predictive model usefulness was tested on 30 open population subjects without prior knowledge of SARS-CoV-2 RT-qPCR status. Of these 3 subjects exhibited COVID-19 suggestive breath profiles, all asymptomatic at the time, two of which were later shown to be SARS-CoV-2 RT-qPCR positive. An additional subject had a borderline breath profile and SARS-CoV-2 RT-qPCR positive. The remaining 27 subjects exhibited healthy breath profiles as well as SARS-CoV-2 RT-qPCR test results. CONCLUSIONS: In all, the use of olfactory technologies in communities with high transmission rates as well as in resource-limited settings where targeted sampling is not viable represents a practical COVID-19 screening approach capable of promptly identifying COVID-19 suspect patients and providing useful epidemiological information to guide community health strategies in the context of COVID-19.

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This paper presents a comprehensive review of the research studies in volatolomics performed on animals so far. At first, the procedures proposed for the collection, preconcentration, and storing of the volatile organic compounds emitted by various biological samples of different animals are presented and discussed. Next, the results obtained in the analysis of the collected volatile samples with analytical equipment are shown. The possible volatile biomarkers identified for various diseases are highlighted for different types of diseases, animal species, and biological samples analyzed. The chemical classes of these compounds, as well as the biomarkers found in a higher number of animal diseases, are indicated, and their possible origin is analyzed. The studies that dealt with the diagnosis of various diseases from sample measurement with electronic nose systems are also presented and discussed. The paper ends with a final remark regarding the necessity of optimization and standardization of sample collection and analysis procedures for obtaining meaningful results.

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INTRODUCTION: To end the tuberculosis (TB) epidemic, efficient diagnostic tools are needed. In a previous calibration study, a portable 'point of care' electronic nose device (AeonoseTM ) proved to be a promising tool in a hospital setting. We evaluated this technology to detect TB in an indigenous population in Paraguay. METHODS: A total of 131 participants were enrolled. eNose results were compared with anamnesis, physical examinations, chest radiography and mycobacterial cultures in individuals with signs and symptoms compatible with TB. The eNose analysis was performed in two stages: first, the training with a combination of a previous study population plus 47 participants from the new cohort (total n = 153), and second, the 'blind prediction' of 84 participants. RESULTS: 21% of all participants (n = 131) showed symptoms and/or chest radiography abnormalities suspicious of TB. No sputum samples resulted culture positive for Mycobacterium tuberculosis complex. Only one patient had a positive smell print analysis. In the training model, the specificity was 92% (95% confidence interval (CI): 85%-96%) and the negative predictive value (NPV) was 95%. In the blind prediction model, the specificity and the NPV were 99% (95% CI: 93%-99%) and 100%, respectively. Although the sensitivity and positive predictive value of the eNose could not be assessed in this cohort due to the small sample size, no active TB cases were found during a one year of follow-up period. CONCLUSION: The eNose showed promising specificity and negative predictive value and might therefore be developed as a rule-out test for TB in vulnerable populations.

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BACKGROUND: Analysis of volatile organic compounds (VOCs) in exhaled breath has been proposed as a screening method that discriminates between disease and healthy subjects, few studies evaluate whether these chemical fingerprints are specific when compared between diseases. We evaluated global VOCs and their discrimination capacity in chronic obstructive pulmonary disease (COPD), lung cancer, breast cancer and healthy subjects by chemoresistive sensors and chemometric analysis. METHODS: A cross-sectional study was conducted with the participation of 30 patients with lung cancer, 50 with breast cancer, 50 with COPD and 50 control subjects. Each participant's exhaled breath was analyzed with the electronic nose. A multivariate analysis was carried: principal component analysis (PCA) and, canonical analysis of principal coordinates (CAP). Twenty single-blind samples from the 4 study groups were evaluated by CAP. RESULTS: A separation between the groups of patients to the controls was achieved through PCA with explanations of >90% of the data and with a correct classification of 100%. In the CAP of the 4 study groups, discrimination between the diseases was obtained with 2 canonical axes with a correct general classification of 91.35%. This model was used for the prediction of the single-blind samples resulting in correct classification of 100%. CONCLUSIONS: The application of chemoresistive gas sensors and chemometric analysis can be used as a useful tool for a screening test for lung cancer, breast cancer and COPD since this equipment detects the set of VOCs present in the exhaled breath to generate a characteristic chemical fingerprint of each disease.

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This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis. The database consists of 4 groups of measurements which were acquired through the E-nose system: 10 control samples (healthy people), 20 samples of people with COPD, 4 samples of smokers and 10 air samples, where in each group two samples of exhaled breath per person were acquired giving a total of 78 samples (40 from COPD, 20 from control, 8 from smokers and 10 from the air).

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Waste cooking oil (WCO) is a valuable feedstock for the synthesis of biodiesel but the product exhibits poor oxidative stability. Techniques available for assessing this parameter are generally expensive and time-consuming, hence the purpose of this study was to develop and validate a rapid and reliable predictive system based on signals from the sensors of a commercial hand-held e-nose instrument. Biodiesels were synthesized from soybean oil and six samples of WCO, and their physicochemical characteristics and oxidative stabilities determined before and after storage in different types of containers for 30 or 60 days at room temperature or 43 °C. Linear regression models were constructed based on principal component analysis of the signals generated by all 32 e-nose sensors and stochastic modeling of signal profiles from individual sensors. The regression model with principal components as predictors was unable to explain the oxidative stability of biodiesels, while the regression model with stochastic parameters (combining signals from 11 sensors) as predictors showed an excellent goodness of fit (R2 = 0.91) with a 45-sample training set and a good quality of prediction (R2 = 0.84) with a 18-sample validation set. The proposed e-nose system was shown to be accurate and efficient and could be used to advantage by producers/distributors of biodiesel in the assessment fuel quality.

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Ionogel are versatile materials, as they present the electrical properties of ionic liquids and also dimensional stability, since they are trapped in a solid matrix, allowing application in electronic devices such as gas sensors and electronic noses. In this work, ionogels were designed to act as a sensitive layer for the detection of volatiles in a custom-made electronic nose. Ionogels composed of gelatin and a single imidazolium ionic liquid were doped with bare and functionalized iron oxide nanoparticles, producing ionogels with adjustable target selectivity. After exposing an array of four ionogels to 12 distinct volatile organic compounds, the collected signals were analyzed by principal component analysis (PCA) and by several supervised classification methods, in order to assess the ability of the electronic nose to distinguish different volatiles, which showed accuracy above 98%.

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Academic stress is an emotion that students experience during their time at the university, sometimes causing physical and mental health effects. Because of the COVID-19 pandemic, universities worldwide have left the classroom to provide the method of teaching virtually, generating challenges, adaptations, and more stress in students. In this pilot study, a methodology for academic stress detection in engineering students at the University of Pamplona (Colombia) is proposed by developing and implementing an artificial electronic nose system and the galvanic skin response. For the study, the student's stress state and characteristics were taken into account to make the data analysis where a set of measurements were acquired when the students were presenting a virtual exam. Likewise, for the non-stress state, a set of measurements were obtained in a relaxation state after the exam date. To carry out the pre-processing and data processing from the measurements obtained previously by both systems, a set of algorithms developed in Python software were used to perform the data analysis. Linear Discriminant Analysis (LDA), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM) classification methods were applied for the data classification, where a 96 % success rate of classification was obtained with the E-nose, and 100 % classification was achieved by using the Galvanic Skin Response.

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Companies develop strategies to describe where they want to go and how they will reach their destination. Business strategies are useful but may not be sufficiently detailed for areas of high importance, such as technology and innovation. In this paper we examined the effort of building a technology roadmap with an early growth stage company located in the state of São Paulo, Brazil. Roadmaps are easy to design yet flexible tools that can allow decision makers to explore a myriad of possible strategies. However, the challenges ahead for new companies facing uncertain growth scenarios demand that framework conditions be adequately addressed, and that innovation culture and technology management tools are integrated with the technology roadmapping strategy. Based on the empirical evidence collected from the startup studied, along with the literature and interviews with key stakeholders, this paper developed a pathway to support technology and innovation plans for startups going through similar growth stages and provides directions for future research in the area, given the scarcity of evidence available of new high-tech companies' efforts in planning and developing new products.(AU)

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Companies develop strategies to describe where they want to go and how they will reach their destination. Business strategies are useful but may not be sufficiently detailed for areas of high importance, such as technology and innovation. In this paper we examined the effort of building a technology roadmap with an early growth stage company located in the state of São Paulo, Brazil. Roadmaps are easy to design yet flexible tools that can allow decision makers to explore a myriad of possible strategies. However, the challenges ahead for new companies facing uncertain growth scenarios demand that framework conditions be adequately addressed, and that innovation culture and technology management tools are integrated with the technology roadmapping strategy. Based on the empirical evidence collected from the startup studied, along with the literature and interviews with key stakeholders, this paper developed a pathway to support technology and innovation plans for startups going through similar growth stages and provides directions for future research in the area, given the scarcity of evidence available of new high-tech companies' efforts in planning and developing new products.

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