Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection

Low-Cost Electronic Nose for the Determination of Urinary Infections

Currently, urine samples for bacterial or fungal infections require a long diagnostic period (48 h). In the present work, a point-of-care device known as an electronic nose (eNose) has been designed based on the "smell print" of infections, since each one emits various volatile organic compounds (VOC) that can be registered by the electronic systems of the device and recognized in a very short time. Urine samples were analyzed in parallel using urine culture and eNose technology. A total of 203 urine samples were analyzed, of which 106 were infected and 97 were not infected. A principal component analysis (PCA) was performed using these data. The algorithm was initially capable of correctly classifying 49% of the total samples. By using SVM-based models, it is possible to improve the accuracy of the classification up to 74% when randomly using 85% of the data for training and 15% for validation. The model is evaluated as having a correct classification rate of 74%. In conclusion, the diagnostic accuracy of the eNose in urine samples is high, promising and amenable for further improvement, and the eNose has the potential to become a feasible, reproducible, low-cost and high-precision device to be applied in clinical practice for the diagnosis of urinary tract infections.

Urinary tract infections: a review of the current diagnostics landscape

Urinary tract infections are the most common bacterial infections worldwide. Infections can range from mild, recurrent (rUTI) to complicated (cUTIs), and are predominantly caused by uropathogenic Escherichia coli (UPEC). Antibiotic therapy is important to tackle infection; however, with the continued emergence of antibiotic resistance there is an urgent need to monitor the use of effective antibiotics through better stewardship measures. Currently, clinical diagnosis of UTIs relies on empiric methods supported by laboratory testing including cellular analysis (of both human and bacterial cells), dipstick analysis and phenotypic culture. Therefore, development of novel, sensitive and specific diagnostics is an important means to rationalise antibiotic therapy in patients. This review discusses the current diagnostic landscape and highlights promising novel diagnostic technologies in development that could aid in treatment and management of antibiotic-resistant UTIs.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

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.

A semi-supervised decision support system to facilitate antibiotic stewardship for urinary tract infections

Urinary Tract Infections (UTIs) are among the most frequently occurring infections in the hospital. Urinalysis and urine culture are the main tools used for diagnosis. Whereas urinalysis is sufficiently sensitive for detecting UTI, it has a relatively low specificity, leading to unnecessary treatment with antibiotics and the risk of increasing antibiotic resistance. We performed an evaluation of the current diagnostic process with an expert-based label for UTI as outcome, retrospectively established using data from the Electronic Health Records. We found that the combination of urinalysis results with the Gram stain and other readily available parameters can be used effectively for predicting UTI. Based on the obtained information, we engineered a clinical decision support system (CDSS) using the reliable semi-supervised ensemble learning (RESSEL) method, and found it to be more accurate than urinalysis or the urine culture for prediction of UTI. The CDSS provides clinicians with this prediction within hours of ordering a culture and thereby enables them to hold off on prematurely prescribing antibiotics for UTI while awaiting the culture results.

Electronic Nose and Tongue for Assessing Human Microbiota

The technological developments of recent times have allowed the use of innovative approaches to support the diagnosis of various diseases. Many of such clinical conditions are often associated with metabolic unbalance, in turn producing an alteration of the gut microbiota even during asymptomatic stages. As such, studies regarding the microbiota composition in biological fluids obtained by humans are continuously growing, and the methodologies for their investigation are rapidly changing, making it less invasive and more affordable. To this extent, Electronic Nose and Electronic Tongue tools are gaining importance in the relevant field, making them a useful alternative—or support—to traditional analytical methods. In light of this, the present manuscript seeks to investigate the development and use of such tools in the gut microbiota assessment according to the current literature. Significant gaps are still present, particularly concerning the Electronic Tongue systems, however the current evidence highlights the strong potential such tools own to enter the daily clinical practice, with significant advancement concerning the patients’ acceptability and cost saving for healthcare providers.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Experimental realization of visible gas sensing technology based on spatial heterodyne spectroscopy

Based on the characteristics of optical absorption gas sensing technology (OA-GST) and spatial heterodyne spectroscopy (SHS), a novel type of visual gas sensing technology (V-GST) can present the invisible gas information in the form of two-dimensional visual fingerprint, which has attracted people's attention. In this paper, we have realized the NO₂ detection of V-GST in the laboratory environment for the first time. Experimental results show that: V-GST not only has different interferogram response to different spectra, but also has good response to different concentrations of NO₂, which lays a foundation for the application of this technology in gas sensing. And the average classification recognition rate of the system for different band NO₂ response data is over 80%, which verifies the effectiveness of the V-GST in gas detection.

Application of leukocyte esterase strip test in the screening of periprosthetic joint infections and prospects of high-precision strips

Periprosthetic joint infection (PJI) represents one of the most challenging complications after total joint arthroplasty (TJA). Despite the availability of a variety of diagnostic techniques, the diagnosis of PJI remains a challenge due to the lack of well-established diagnostic criteria. The leucocyte esterase (LE) strips test has been proved to be a valuable diagnostic tool for PJI, and its weight in PJI diagnostic criteria has gradually increased. Characterized by its convenience, speed and immediacy, leucocyte esterase strips test has a prospect of broad application in PJI diagnosis. Admittedly, the leucocyte esterase strips test has some limitations, such as imprecision and liability to interference. Thanks to the application of new technologies, such as machine reading, quantitative detection and artificial intelligence, the LE strips test is expected to overcome the limitations and improve its accuracy.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

BACKGROUND

Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems.

AIMS

This narrative review aims to explore the current use of ML In clinical microbiology.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019.

CONTENT

We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n = 71, 73%) but a significant number used data from low- and middle-income countries (n = 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes.

IMPLICATIONS

In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

Electronic Noses in Medical Diagnostics

BACKGROUND

Electronic nose technology is being developed in order to analyse complex mixtures of volatiles in a way parallel to biologic olfaction. When applied in the field of medicine, the use of such devices should enable the identification and discrimination between different diseases. In this review, a comprehensive summary of research in medical diagnostics using electronic noses is presented. A special attention has been paid to the application of these devices and sensor technologies, in response to current trends in medicine.

METHODS

Peer-reviewed research literature pertaining to the subject matter was identified based on a search of bibliographic databases. The quality and relevance of retrieved papers was assessed using standard tools. Their content was critically reviewed and certain information contained therein was compiled in tabularized form.

RESULTS

The majority of reviewed studies show promising results, often surpassing the accuracy and sensitivity of established diagnostic methods. However, only a relatively small number of devices have been field tested. The methods used for sample collection and data processing in various studies were listed in a table, together with electronic nose models used in these investigations.

CONCLUSION

Despite the fact that devices equipped with arrays of chemical sensors are not routinely used in everyday medical practice, their prospective use would solve some established issues in medical diagnostics, as well as lead to developments in prophylactics by facilitating a widespread use of non-invasive screening tests.

Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor

A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising.

Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Synergy between nanomaterials and volatile organic compounds for non-invasive medical evaluation

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.

Innovative Diagnostic Methods for Early Prostate Cancer Detection through Urine Analysis: A Review

Prostate cancer is the second most common cause of cancer death among men. It is an asymptomatic and slow growing tumour, which starts occurring in young men, but can be detected only around the age of 40–50. Although its long latency period and potential curability make prostate cancer a perfect candidate for screening programs, the current procedure lacks in specificity. Researchers are rising to the challenge of developing innovative tools able of detecting the disease during its early stage that is the most curable. In recent years, the interest in characterisation of biological fluids aimed at the identification of tumour-specific compounds has increased significantly, since cell neoplastic transformation causes metabolic alterations leading to volatile organic compounds release. In the scientific literature, different approaches have been proposed. Many studies focus on the identification of a cancer-characteristic “odour fingerprint” emanated from biological samples through the application of sensorial or senso-instrumental analyses, others suggest a chemical characterisation of biological fluids with the aim of identifying prostate cancer (PCa)-specific biomarkers. This paper focuses on the review of literary studies in the field of prostate cancer diagnosis, in order to provide an overview of innovative methods based on the analysis of urine, thereby comparing them with the traditional diagnostic procedures.

Research on a Visual Electronic Nose System Based on Spatial Heterodyne Spectrometer

Light absorption gas sensing technology has the characteristics of massive parallelism, cross-sensitivity and extensive responsiveness, which make it suitable for the sensing task of an electronic nose (e-nose). With the performance of hyperspectral resolution, spatial heterodyne spectrometer (SHS) can present absorption spectra of the gas in the form of a two dimensional (2D) interferogram which facilitates the analysis of gases with mature image processing techniques. Therefore, a visual e-nose system based on SHS was proposed. Firstly, a theoretical model of the visual e-nose system was constructed and its visual maps were obtained by an experiment. Then the local binary pattern (LBP) and Gray-Level Co-occurrence Matrix (GLCM) were used for feature extraction. Finally, classification algorithms based on distance similarity (Correlation coefficient (CC); Euclidean distance to centroids (EDC)) were chosen to carry on pattern recognition analysis to verify the feasibility of the visual e-nose system.

Detection of Volatile Organic Compounds in a Drop of Urine by Ultrasonic Nebulization Extraction Proton Transfer Reaction Mass Spectrometry

Detection of volatile organic compounds (VOCs) in human urine has potential application value in screening for disease and toxin exposure. However, the current technologies are too slow to detect the concentration of VOCs in fresh urine. In this study, we developed a novel ultrasonic nebulization extraction proton transfer reaction mass spectrometry (UNE-PTR-MS) technology. The urinary VOCs can be rapidly extracted to gaseous VOCs using the UNE system and then delivered using a carrier gas to the PTR-MS instrument for rapid detection. The carrier gas flow and sample size were optimized to 100 mL/min and 100 μL, respectively. The limits of detection (LODs) and response time of the UNE-PTR-MS were evaluated by detecting three VOCs that are common in human urine: methanol, acetaldehyde, and acetone. The LODs determined for methanol (4.47 μg/L), acetaldehyde (1.98 μg/L), and acetone (3.47 μg/L) are 2-3 orders of magnitude lower than the mean concentrations of that in healthy human urine. The response time of the UNE-PTR-MS is 34 s and only 0.66 mL of urine is required for a full scan. The repeatability of this UNE-PTR-MS was evaluated, and the relative standard deviations of 5 independent determinations were between 4.62% and 5.21%. Lastly, the UNE-PTR-MS was applied for detection of methanol, acetaldehyde, and acetone in real human urine to test matrix effects, yielding relative recoveries of between 88.39% and 94.54%. These results indicate the UNE-PTR-MS can be used for the rapid detection of VOCs in a drop of urine and has practical potential for diagnosing disease or toxin exposure.

Application and Uses of Electronic Noses for Clinical Diagnosis on Urine Samples: A Review

The electronic nose is able to provide useful information through the analysis of the volatile organic compounds in body fluids, such as exhaled breath, urine and blood. This paper focuses on the review of electronic nose studies and applications in the specific field of medical diagnostics based on the analysis of the gaseous headspace of human urine, in order to provide a broad overview of the state of the art and thus enhance future developments in this field. The research in this field is rather recent and still in progress, and there are several aspects that need to be investigated more into depth, not only to develop and improve specific electronic noses for different diseases, but also with the aim to discover and analyse the connections between specific diseases and the body fluids odour. Further research is needed to improve the results obtained up to now; the development of new sensors and data processing methods should lead to greater diagnostic accuracy thus making the electronic nose an effective tool for early detection of different kinds of diseases, ranging from infections to tumours or exposure to toxic agents.

Diagnosing gastrointestinal illnesses using fecal headspace volatile organic compounds

Volatile organic compounds (VOCs) emitted from stool are the components of the smell of stool representing the end products of microbial activity and metabolism that can be used to diagnose disease. Despite the abundance of hydrogen, carbon dioxide, and methane that have already been identified in human flatus, the small portion of trace gases making up the VOCs emitted from stool include organic acids, alcohols, esters, heterocyclic compounds, aldehydes, ketones, and alkanes, among others. These are the gases that vary among individuals in sickness and in health, in dietary changes, and in gut microbial activity. Electronic nose devices are analytical and pattern recognition platforms that can utilize mass spectrometry or electrochemical sensors to detect these VOCs in gas samples. When paired with machine-learning and pattern recognition algorithms, this can identify patterns of VOCs, and thus patterns of smell, that can be used to identify disease states. In this review, we provide a clinical background of VOC identification, electronic nose development, and review gastroenterology applications toward diagnosing disease by the volatile headspace analysis of stool.

The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva

Breath analysis is a young field of research with its roots in antiquity. Antoine Lavoisier discovered carbon dioxide in exhaled breath during the period 1777-1783, Wilhelm (Vilém) Petters discovered acetone in breath in 1857 and Johannes Müller reported the first quantitative measurements of acetone in 1898. A recent review reported 1765 volatile compounds appearing in exhaled breath, skin emanations, urine, saliva, human breast milk, blood and feces. For a large number of compounds, real-time analysis of exhaled breath or skin emanations has been performed, e.g., during exertion of effort on a stationary bicycle or during sleep. Volatile compounds in exhaled breath, which record historical exposure, are called the 'exposome'. Changes in biogenic volatile organic compound concentrations can be used to mirror metabolic or (patho)physiological processes in the whole body or blood concentrations of drugs (e.g. propofol) in clinical settings-even during artificial ventilation or during surgery. Also compounds released by bacterial strains like Pseudomonas aeruginosa or Streptococcus pneumonia could be very interesting. Methyl methacrylate (CAS 80-62-6), for example, was observed in the headspace of Streptococcus pneumonia in concentrations up to 1420 ppb. Fecal volatiles have been implicated in differentiating certain infectious bowel diseases such as Clostridium difficile, Campylobacter, Salmonella and Cholera. They have also been used to differentiate other non-infectious conditions such as irritable bowel syndrome and inflammatory bowel disease. In addition, alterations in urine volatiles have been used to detect urinary tract infections, bladder, prostate and other cancers. Peroxidation of lipids and other biomolecules by reactive oxygen species produce volatile compounds like ethane and 1-pentane. Noninvasive detection and therapeutic monitoring of oxidative stress would be highly desirable in autoimmunological, neurological, inflammatory diseases and cancer, but also during surgery and in intensive care units. The investigation of cell cultures opens up new possibilities for elucidation of the biochemical background of volatile compounds. In future studies, combined investigations of a particular compound with regard to human matrices such as breath, urine, saliva and cell culture investigations will lead to novel scientific progress in the field.

The need for external validation in machine olfaction: emphasis on health-related applications

Over the last two decades, electronic nose research has produced thousands of research works. Many of them were describing the ability of the e-nose technology to solve diverse applications in domains ranging from food technology to safety, security, or health. It is, in fact, in the biomedical field where e-nose technology is finding a research niche in the last years. Although few success stories exist, most described applications never found the road to industrial or clinical exploitation. Most described methodologies were not reliable and were plagued by numerous problems that prevented practical application beyond the lab. This work emphasizes the need of external validation in machine olfaction. I describe some statistical and methodological pitfalls of the e-nose practice and I give some best practice recommendations for researchers in the field.

A low-power electronic nose signal-processing chip for a portable artificial olfaction system

The bulkiness of current electronic nose (E-Nose) systems severely limits their portability. This study designed and fabricated an E-Nose signal-processing chip by using TSMC 0.18-μ m 1P6M complementary metal-oxide semiconductor technology to overcome the need to connect the device to a personal computer, which has traditionally been a major stumbling block in reducing the size of E-Nose systems. The proposed chip is based on a conductive polymer sensor array chip composed of multiwalled carbon nanotubes. The signal-processing chip comprises an interface circuit, an analog-to-digital converter, a memory module, and a microprocessor embedded with a pattern-recognition algorithm. Experimental results have verified the functionality of the proposed system, in which the E-Nose signal-processing chip successfully classified three odors, carbon tetrachloride (CCl4), chloroform (CHCl3), and 2-Butanone (MEK), demonstrating its potential for portable applications. The power consumption of this signal-processing chip was maintained at a very low 2.81 mW using a 1.8-V power supply, making it highly suitable for integration as an electronic nose system-on-chip.

Advances in electronic-nose technologies developed for biomedical applications

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry.

Different volatile signals emitted by human ovarian carcinoma and healthy tissue

Many cancers are detected at a late stage resulting in high mortality rates. Thus, it is essential to develop inexpensive and simple methods for early diagnosis. Detection of different malignancies using canine scent, as well as other technical methods, has been reported in peer-reviewed journals, indicating that this may represent a new diagnostic tool for malignancies. Aim: This study aims to test the detection of different volatile organic compound signals emitted by ovarian carcinoma and normal tissues. Materials & methods: A previously tested electronic nose is used in the pilot study to analyze human grade 3 seropapillary ovarian carcinoma samples. The recorded signals were compared with healthy human Fallopian tube specimens. A variety of algorithms were tested and confusion matrices compared. In parallel, an external validation study was performed using the same type and grade of human ovarian carcinomas with healthy myometrium (first part) and postmenopausal ovarium (second part) specimens as controls. Both sample types were obtained from individuals who did not participate in the pilot study. Results: Method sensitivity was 100% (15 of 15) in the pilot study. The first part of the validation study demonstrated that 84.8% of cancer tissues (sensitivity: 84.8%) and 88.6% of the control samples (specificity: 88.6%) were correctly classified. In the second part the JRip algorithm correctly classified 75% of cancer tissues (sensitivity: 75%) and 80% of the control ovarian tissues (specificity: 80%). Collating results gives a sensitivity of 84.4%, whereas overall specificity was 86.8%. Conclusion: Although based on a limited number of samples, our results strongly suggest that specific volatile organic compound signals emitted by ovarian carcinomas may be used for early diagnosis of the disease.

Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer

A potential strategy for diagnosing lung cancer, the leading cause of cancer-related death, is to identify metabolic signatures (biomarkers) of the disease. Although data supports the hypothesis that volatile compounds can be detected in the breath of lung cancer patients by the sense of smell or through bioanalytical techniques, analysis of breath samples is cumbersome and technically challenging, thus limiting its applicability. The hypothesis explored here is that variations in small molecular weight volatile organic compounds (“odorants”) in urine could be used as biomarkers for lung cancer. To demonstrate the presence and chemical structures of volatile biomarkers, we studied mouse olfactory-guided behavior and metabolomics of volatile constituents of urine. Sensor mice could be trained to discriminate between odors of mice with and without experimental tumors demonstrating that volatile odorants are sufficient to identify tumor-bearing mice. Consistent with this result, chemical analyses of urinary volatiles demonstrated that the amounts of several compounds were dramatically different between tumor and control mice. Using principal component analysis and supervised machine-learning, we accurately discriminated between tumor and control groups, a result that was cross validated with novel test groups. Although there were shared differences between experimental and control animals in the two tumor models, we also found chemical differences between these models, demonstrating tumor-based specificity. The success of these studies provides a novel proof-of-principle demonstration of lung tumor diagnosis through urinary volatile odorants. This work should provide an impetus for similar searches for volatile diagnostic biomarkers in the urine of human lung cancer patients.