Editorial
Electronic-nose Devices – Potential for Noninvasive Early Disease-Detection Applications
Alphus Dan Wilson*
Department of Pathology, USDA Forest Service, Southern Research Station, USA
*Corresponding author: Alphus Dan Wilson, Department of Pathology, USDA Forest Service,Southern Research Station, Stoneville, MS 38776, USA
Published: 14 Jul, 2017
Cite this article as: Wilson AD. Electronic-nose Devices
– Potential for Noninvasive Early
Disease-Detection Applications. Ann
Clin Case Rep. 2017; 2: 1401.
Abstract
Significant progress in the development of portable electronic devices is showing considerable
promise to facilitate clinical diagnostic processes. The increasing global trend of shifts in healthcare
policies and priorities toward shortening and improving the effectiveness of diagnostic procedures
by utilizing non-invasive methods should provide multiple benefits of increasing treatment efficiency
and lowering healthcare costs while accelerating the speed and accuracy of diagnoses. The results
of this improved approach will lead to earlier treatments that ultimately result in more favorable
prognoses, more rapid patient recovery, and shorter hospital stays. Electronic-nose (e-nose) and
similar devices have been at the forefront of recent clinical research focused on the development
of new potential diagnostic tools to aid in disease detection and etiology for point-of-care testing
(POCT). E-noses are artificial gas-sensing systems, usually containing chemical cross-reactive
multi-sensor arrays capable of characterizing the aroma patterns of volatile organic compounds
(VOCs), which utilize pattern-recognition algorithms for aroma classification [1]. The most useful e-nose devices are lightweight, portable handheld tools that provide real-time data. These low-cost
instruments have the potential to revolutionize many diagnostic, clinical procedures used to detect
a multitude of human diseases with diverse etiologies. Numerous types of e-nose devices have been
developed, based on different operational mechanisms, offering varying capabilities, sensitivities,
response times, and advantages for different clinical applications [2]. Currently available e-nose technologies most commonly used for clinical practice include carbon nanofiber (CNF), conducting
polymer (CP), metal oxide semiconductor (MOS), polymer carbon black composite (PCBC), quartz
crystal microbalance (QCM), and surface acoustic wave (SAW) devices.
Many conventional diagnostic tests involve invasive or painful procedures that often discourage
patients from seeking preemptive early disease-detection screenings. Other tests require timeconsuming
or expensive and sophisticated laboratory chemical tests that often delay the availability
of results for diagnostic evaluation and preclude early disease treatments, potentially putting
patients at greater risk with less favorable prognoses [3-5]. This is particularly true for acute infectious diseases, caused by systemic infections (sepsis) by virulent morbific pathogens with short
incubation periods which produce rapid onset of tissue damage, organ degeneration, and critical
life-threatening conditions.
E-nose devices are particularly useful for noninvasive early detection of diseases by sensing
specific mixtures of VOCs that serve as effective chemical biomarkers of disease. Multi-sensor arrays
of portable e-nose instruments provide unique VOC-metabolite signatures for specific diseases,
allowing simpler diagnoses than by nuclear magnetic resonance (NMR) metabolomics, selected
ion flow tube-mass spectrometry (SIFT-MS), proton transfer reaction-mass spectrometry (PTRMS)
or gas chromatography-mass spectrometry (GC-MS) methods that often require extensive
data manipulations, intensive analyses and complex modeling before diagnostic interpretations are
possible. E-noses often yield faster results and provide earlier detections of human diseases, allowing
earlier, more effective treatments and consequently more rapid patient recovery [6].
Electronic-nose devices offer many additional advantages over conventional diagnostic tools
such as low operating costs, ease of operation without extensive training required, rapid results
and response times, good precision, greater portability and flexibility for clinical or field use,
and high adaptability for specialized applications [7,8]. These versatile gas-sensing devices also
provide a means for rapidly checking patient identity and confirming physiological status prior to
administering drugs, invasive surgical procedures, or other irreversible treatments [2].
E-nose analysis of headspace volatiles from diagnostic air samples, including expired breath,
body fluids, and skin, provides effective means for detecting many diseases noninvasively [7,9].
Through e-nose breath analysis, the overall health of a patient may
be monitored continuously to determine changes in physiological
(healthy or diseased) states based on the presence of unique breath
VOC-mixtures, corresponding to diagnostic e-nose smellprintsignatures
(in reference libraries), previously associated with specific
diseases or conditions. Breath monitoring of patients by e-nose
analyses following treatments also may provide indications of the
effectiveness of treatments, recovery from disease conditions, wound
and graft healing, and indications of prognoses going forward.
The range of types and categories of diseases detectable with
e-nose instruments include various respiratory diseases, numerous
types of cancers, metabolic disorders, urinary- and intestinal-tract
infections, and related physiological monitoring of diseases (progress
and recovery) [7,10]. Some more recent advances include e-nose
detection of patient’s drug use, exposure to hazardous gases and toxins,
organ dysfunctions and failures, and physiological abnormalities [2].
E-nose devices also have been used in forensic medicine to determine
postmortem cause and time of death [11].
Metabolic fingerprinting of many major diseases has been
accomplished through the identification of key VOC biomarker
metabolites. A large number of different specific types and classes of
chemical disease biomarkers have been identified. Different categories
of chemical biomarkers, associated with e-nose smellprint signatures,
provide indicators of different types of diseases. Disease biomarker
signatures may be categorized as indicators of abiotic diseases
(including genetic and metabolic diseases), infectious diseases, and
non-infectious diseases. Additional disease biomarker signatures
have been classified into subcategories such as disease-predisposition
biomarkers, pathogen-specific biomarkers, and location or organspecific
biomarkers (e.g. gut-microflora) that may be the basis of
application-specific or disease-specific e-nose detection [12-16].
The rapidity and accuracy of diagnoses based e-nose detection
of disease biomarker mixtures are greatly improved by the use
of application-specific e-nose databases, containing breathprints
or smellprints (VOC biomarker disease-signatures) for specific
sets of closely related diseases. This approach greatly reduces the
possibility of false positive, false negative and unknown diagnostic
determinations. The e-nose can be programmed to utilize a wide
range of application-specific reference databases, depending on the
diagnostic applications required.
The improved universal standardization of portable e-nose
instruments and protocols for different disease-detection applications
should speed up the development and implementation of these
devices for routine clinical diagnostic use. The eventual increased
use of e-nose instruments for clinical diagnostic applications will
not necessarily preclude the use or replacement of conventional
diagnostic methods, but they will likely provide additional tools for
improving efficiency and earlier indications of probable diagnoses.
New-generation dual-technology e-nose instruments are now
being developed that provide headspace aroma signatures from an
e-nose sensor array as well as chemical analysis data for component
identifications [2]. These new advanced e-noses may be used in
combination with other physiological sensor devices to aid in disease
diagnosis and classification [17].
Conclusion
Current clinical research with e-nose devices is achieving significant progress in the development of new biomedical applications that should help accelerate many clinical operations and procedures. Emerging electronic-nose technologies offer great potential for numerous POCT diagnostic applications in early disease detection with many advantages over conventional invasive, painful and time-consuming tests that discourage patients from seeking preemptive disease-screening procedures. Ongoing research and clinical trials are providing application-specific protocols and efficacy data that should eventually allow e-nose devices to be integrated as effective tools to enhance disease-diagnostic procedures in routine clinical practice. The greater potential cost-effectiveness of e-nose based early clinical disease diagnosis, by means of real-time detection of VOC-disease signatures, offers new alternative approaches to cutting diagnostic costs and facilitate clinical decision-making. E-nose devices ultimately may play a role in offering a more personalized approach to disease detection and therapy in the future when used in combination with other early disease-screening procedures for patients with predispositions to specific diseases.
References
- Santini G, Mores N, Penas A, Capuano R, Mondino C, Trové A, et al. Electronic nose and exhaled breath NMR-based metabolomics applications in airways disease. Curr Top Med Chem. 2016; 16: 1610-1630.
- Wilson AD. Recent progress in the design and clinical development of electronic-nose technologies. Nanobiosens Dis Diagn. 2016; 5: 15-27.
- Veronika Ruzsanyi, Lukas Fischer, Jens Herbig, Clemes Ager, Anton Amann. Multi-capillary-column proton-transfer-reaction time-of-flight mass spectrometry. J Chromatogr. A 2013; 1316: 112–118.
- Spaněl P, Smith D. Progress in SIFT-MS: breath analysis and other applications. Mass Spectrom Rev. 2011; 30: 236-67.
- Wang C, Sahay P. Breath analysis using laser spectroscopic techniques: Breath biomarkers, spectral fingerprints, and detection limits. Sensors. 2009; 9: 8230–8262.
- Wilson AD. Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath. Metabolites. 2015; 5: 140–163.
- Wilson AD, Baietto M. Advances in electronic-nose technologies developed for biomedical applications. Sensors (Basel). 2011; 11: 1105-1176.
- Wilson AD, Baietto M. Applications and advances in electronic-nose technologies. Sensors (Basel). 2009; 9: 5099-5148.
- Capelli L, Taverna G, Bellini A, Eusebio L, Buffi N, Lazzeri M, et al. Application and uses of electronic noses for clinical diagnosis on urine samples: a review. Sensors. 2016; 16: 1708.
- Kahn N, Lavie O, Paz M, Segev Y, Haick H. Dynamic nanoparticle-based flexible sensors: diagnosis of ovarian carcinoma from exhaled breath. Nano Lett. 2015; 15: 7023-7028.
- Wilson AD. Electronic-nose applications in forensic science and for analysis of volatile biomarkers in the human breath. J Forens Sci Criminol. 2014; 1: 1-21.
- Wilson AD. Biomarker metabolite signatures pave the way for electronic-nose applications in early clinical disease diagnoses. Curr Metabolom. 2017; 5: 90-101.
- Bos LD, Sterk PJ, Schultz MJ. Volatile metabolites of pathogens: a systematic review. PLoS Pathog. 2013; 9: e1003311.
- Emwas AM, Salek RM. NMR-based metabolomics in human disease diagnosis: applications, limitations, and recommendations. Metabolomics. 2013; 9: 1048-1072.
- Paff T, van der Schee MP, Daniels JMA, Pals G, Postmus PE, Sterk PJ, et al. Exhaled molecular profiles in the assessment of cystic fibrosis and primary ciliary dyskinesia. J Cystic Fibrosis. 2013; 12: 454-460.
- Garcia RA, Morales V, Martín S, Vilches E, Toledano A. Volatile organic compounds analysis in breath air in healthy volunteers and patients suffering epidermoid laryngeal carcinomas. Chromatographia. 2014; 77: 501-509.
- Begum S, Barua S, Ahmed MU. Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning. Sensors. 2014; 14: 11770–11785.