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Gas Sensors for Health Care and Medical Applications
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Gas Sensors for Health Care and Medical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 100137

Special Issue Editors

Prof. Dr. Woosuck Shin

E-Mail
Guest Editor
National Institute of Advanced Industrial Science and Technology (AIST) Sakurazaka, Moriyama-ku, Nagoya 463-8560, Japan
Interests: microsensors; VOC sensors; health care and medical application
Special Issues, Collections and Topics in MDPI journals
Dr. Toshio Itoh

E-Mail Website
Guest Editor
Inorganic Functional Materials Research Institute, AIST, Nagoya, Japan
Interests: semiconductor oxide sensors; PCA analysis; room air monitoring

Special Issue Information

Dear Colleagues,

This Special Issue welcomes both reviews and original research articles on the current progress of new sensor technology, human gas specimen collection methods, and interpretation of breath analyses. The recent advancement of breath, using breath-gas monitoring, highly sensitive volatile organic compounds (VOC) detection by chemical or gas sensors, and progress of array or microsensors combined with system technology, enables new applications of human monitoring and parts per billion (ppb) level detection of breath VOC. This state-of-the-art of detection technology accelerates the generation of commercially available sensor systems for health care applications, with significantly-enhanced detection capabilities and minimal size, weight, and power consumption. In this Special Issue, the current state-of-the-art of VOC detection and analysis for health care and medical applications will be examined, and the standardization and methodology for specimen collection, patient preparation, and data analysis for assessing human health or disease will be discussed. This session intends to bridge the gap between the recent achievements of chemical sensors and systems and their applications to the new field of human health monitoring. As such, breath analysis can have an important impact on the global challenges that are faced by medical communities and sensor-technology users.

Prof. Dr. Woosuck Shin
Dr. Toshio Itoh
Guest Editors

Keywords

  • Metal oxide sensors
  • Sensor arrays
  • Electronic nose or e-nose
  • principal component (PC) analysis
  • Food monitoring using gas sensors
  • Room air monitoring
  • Breath analysis
  • Machine learning
  • Breath analysis for asthma, chronic obstructive pulmonary disease (COPD) or lung cancer
  • Sensors for halitosis management

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Published Papers (10 papers)

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2949 KiB  
Article
A Novel Medical E-Nose Signal Analysis System
by Lu Kou, David Zhang and Dongxu Liu
Sensors 2017, 17(4), 402; https://doi.org/10.3390/s17040402 - 5 Apr 2017
Cited by 86 | Viewed by 7963
Abstract
It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, [...] Read more.
It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>Global working flow of the system. BGL: blood glucose level.</p> Full article ">Figure 2
<p>The frame of the e-nose system with five modules: the gas route, the sensor arrays, the signal processing circuitry, the controlling circuitry and the host computer.</p> Full article ">Figure 3
<p>Snapshot of the device (<b>a</b>) and gas chamber (<b>b</b>). Sensors are embedded on its wall. Samples are injected to the chamber from the inlet hole at one end and pumped out through the outlet end.</p> Full article ">Figure 4
<p>The four stages of measurement procedure.</p> Full article ">Figure 5
<p>Forward selection result of six binary-classification tasks. For each graph, the horizontal axis is the number of features used and the vertical axis is the classification accuracy.</p> Full article ">Figure 5 Cont.
<p>Forward selection result of six binary-classification tasks. For each graph, the horizontal axis is the number of features used and the vertical axis is the classification accuracy.</p> Full article ">
2528 KiB  
Article
Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm
by Yuichi Sakumura, Yutaro Koyama, Hiroaki Tokutake, Toyoaki Hida, Kazuo Sato, Toshio Itoh, Takafumi Akamatsu and Woosuck Shin
Sensors 2017, 17(2), 287; https://doi.org/10.3390/s17020287 - 4 Feb 2017
Cited by 98 | Viewed by 11791
Abstract
Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed [...] Read more.
Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls) using gas chromatography/mass spectrometry (GC/MS), and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM) algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH3CN, isoprene, 1-propanol) is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>Breath sampling and gas analysis by GC/MS.</p> Full article ">Figure 2
<p>Comparison of VOC concentration distributions from lung cancer (red, <span class="html-italic">n</span> = 107) and healthy (green, <span class="html-italic">n</span> = 29) controls’ breath; (<b>a</b>) CH<sub>3</sub>CN; (<b>b</b>) CHCl<sub>3</sub>; (<b>c</b>) methanol; (<b>d</b>) CHN; (<b>e</b>) ethanol; (<b>f</b>) 1-propanol; (<b>g</b>) isoprene; (<b>h</b>) C<sub>2</sub>H<sub>3</sub>CN; and (<b>i</b>) limonene. The VOCs in (<b>a</b>–<b>e</b>) show significant differences between samples, while those in (<b>f</b>–<b>i</b>) do not show significant differences (<a href="#sensors-17-00287-t001" class="html-table">Table 1</a>). The distributions of the remaining 11 VOCs are shown in the <a href="#app1-sensors-17-00287" class="html-app">Supplementary Information (Figure S1)</a>.</p> Full article ">Figure 3
<p>Schematic illustrating the oversampling technique to obtain the same number of healthy control samples to that of the lung cancer patients. After one sample (<b>red</b>) is randomly chosen, two samples (<b>blue</b>) are randomly interpolated on the lines between the chosen sample and the two nearest samples (<b>yellow</b>).</p> Full article ">Figure 4
<p>Schematic illustrating nonlinear support vector machine (SVM). (<b>a</b>) The two-class data set is composed of two VOCs (VOC 1 and VOC 2; left panel), which are transformed into a different coordinate space (right panel) where the dataset can be classified by a flat boundary; (<b>b</b>) The SVM boundary (thick line) is determined using data points called support vectors (thick circles). The number of support vectors should be small to avoid overfitting the data points.</p> Full article ">Figure 5
<p>Schematic illustrating the leave-one-out cross-validation (LOOCV) procedure. A data point is repeatedly exchanged to categorize the training and testing data set.</p> Full article ">Figure 6
<p>Dependency of the performance of SVM diagnosis on the number of trained VOCs of the data set (lung cancer patients, <span class="html-italic">n</span> = 107; healthy individuals, <span class="html-italic">n</span> = 29, oversampling healthy samples, <span class="html-italic">n</span> = 78). (<b>a</b>) Best accuracy (ACC, blue line) with the corresponding true positive rate (TPR, solid red line) and true negative rate (TNR, solid green line) within all combinations of each number of trained VOCs (from 1 to 10). The dashed red and green lines represent the best TPR and TNR, respectively; (<b>b</b>) The number of support vectors that are used in the classifier in (<b>a</b>) for the best ACC (blue), TPR (red), and TNR (green). Left and right y-axes represent the actual number of data points and fraction of all data points, respectively.</p> Full article ">Figure 7
<p>VOC distributions on a 3D representation for the list of top accuracy combinations in <a href="#sensors-17-00287-t005" class="html-table">Table 5</a>. CHN, isoprene, 1-propanol (<b>a</b>); CHN, methanol, 1-propanol (<b>b</b>); CHN, methanol, isoprene (<b>c</b>); isoprene, methanol, 1-propanol (<b>d</b>); CH<sub>3</sub>CN, methanol, isoprene (<b>e</b>); and isoprene, CH<sub>3</sub>CN, 1-propanol (<b>f</b>). The red and green circles represent lung cancer patients and healthy controls, respectively, and the blue circles indicate the oversampling data. The oversampling data are more widely spread than the original healthy samples in this range because some of the healthy samples exist outside of the axis range.</p> Full article ">Figure 8
<p>(<b>a</b>) Schematic illustration of the hypothesis that the cancer stage correlates with distance from the SVM boundary in the transformed coordinates space; (<b>b</b>) The <span class="html-italic">y</span>-axis indicates the distance from the SVM boundary. The learning VOC combination of the best TPR in <a href="#sensors-17-00287-t003" class="html-table">Table 3</a> (butane, ethanol, acetone, C<sub>2</sub>H<sub>3</sub>CN, and toluene) was used for computing the test sample distance.</p> Full article ">
823 KiB  
Communication
An Electrochemical Gas Biosensor Based on Enzymes Immobilized on Chromatography Paper for Ethanol Vapor Detection
by Tatsumi Kuretake, Shogo Kawahara, Masanobu Motooka and Shigeyasu Uno
Sensors 2017, 17(2), 281; https://doi.org/10.3390/s17020281 - 1 Feb 2017
Cited by 47 | Viewed by 6570
Abstract
This paper presents a novel method of fabricating an enzymatic biosensor for breath analysis using chromatography paper as enzyme supporting layer and a liquid phase layer on top of screen printed carbon electrodes. We evaluated the performance with ethanol vapor being one of [...] Read more.
This paper presents a novel method of fabricating an enzymatic biosensor for breath analysis using chromatography paper as enzyme supporting layer and a liquid phase layer on top of screen printed carbon electrodes. We evaluated the performance with ethanol vapor being one of the breathing ingredients. The experimental results show that our sensor is able to measure the concentration of ethanol vapor within the range of 50 to 500 ppm. These results suggest the ability of detecting breath ethanol, and it can possibly be applied as a generic vapor biosensor to a wide range of diseases. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>Enzymatic reaction at the surface of ChrSPCEs in ethanol detection.</p> Full article ">Figure 2
<p>Enzyme supporting layer is a lamination of enzyme layer and mediator layer. (<b>a</b>) ChrPrs dipped into the solutions containing AOD and HRP or Ferro; (<b>b</b>) ChrPrs left to dry in a refrigerator at 4 °C for 12 h, respectively; (<b>c</b>) Modified ChrPr enzyme electrodes (ChrSPCEs) placed onto the screen-printed electrodes; (<b>d</b>) Measurement setup of ethanol gaseous analysis by the ChrSPCEs.</p> Full article ">Figure 3
<p>Typical current responses of modified chromatography paper enzyme electrodes for several ethanol gaseous concentrations. (<b>a</b>) The chronoamperometory at V<sub>0</sub> = −0.2 V with 0, 50, 100, 200 and 500 ppm; (<b>b</b>) The calibration curve of the reduction current taken at t = 200 s by enzymatic catalyst on the concentration of ethanol gas.</p> Full article ">
1123 KiB  
Article
Photoacoustic Spectroscopy for the Determination of Lung Cancer Biomarkers—A Preliminary Investigation
by Yannick Saalberg, Henry Bruhns and Marcus Wolff
Sensors 2017, 17(1), 210; https://doi.org/10.3390/s17010210 - 21 Jan 2017
Cited by 23 | Viewed by 9219
Abstract
With 1.6 million deaths per year, lung cancer is one of the leading causes of death worldwide. One reason for this high number is the absence of a preventive medical examination method. Many diagnoses occur in a late cancer stage with a low [...] Read more.
With 1.6 million deaths per year, lung cancer is one of the leading causes of death worldwide. One reason for this high number is the absence of a preventive medical examination method. Many diagnoses occur in a late cancer stage with a low survival rate. An early detection could significantly decrease the mortality. In recent decades, certain substances in human breath have been linked to certain diseases. Different studies show that it is possible to distinguish between lung cancer patients and a healthy control group by analyzing the volatile organic compounds (VOCs) in their breath. We developed a sensor based on photoacoustic spectroscopy for six of the most relevant VOCs linked to lung cancer. As a radiation source, the sensor uses an optical-parametric oscillator (OPO) in a wavelength region from 3.2 µm to 3.5 µm. The limits of detection for a single substance range between 5 ppb and 142 ppb. We also measured high resolution absorption spectra of the biomarkers compared to the data currently available from the National Institute of Standards and Technology (NIST) database, which is the basis of any selective spectroscopic detection. Future lung cancer screening devices could be based on the further development of this sensor. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>Experimental setup of the photoacoustic sensor.</p> Full article ">Figure 2
<p>Number of occurrences of optical–parametric oscillator wavelength step sizes between 3.2 µm and 3.5 µm.</p> Full article ">Figure 3
<p>Number of occurrences of the optical–parametric oscillator output power between 3.2 µm and 3.5 µm.</p> Full article ">Figure 4
<p>Biomarker spectra (<b>blue</b>: Measurement; <b>red</b>: NIST; <b>yellow</b>: PNNL) measured at a concentration of 100 ppm in nitrogen at atmospheric conditions (294 K, 1024 hPa).</p> Full article ">
9881 KiB  
Article
Novel Isoprene Sensor for a Flu Virus Breath Monitor
by Pelagia-Irene Gouma, Lisheng Wang, Sanford R. Simon and Milutin Stanacevic
Sensors 2017, 17(1), 199; https://doi.org/10.3390/s17010199 - 20 Jan 2017
Cited by 35 | Viewed by 22321
Abstract
A common feature of the inflammatory response in patients who have actually contracted influenza is the generation of a number of volatile products of the alveolar and airway epithelium. These products include a number of volatile organic compounds (VOCs) and nitric oxide (NO). [...] Read more.
A common feature of the inflammatory response in patients who have actually contracted influenza is the generation of a number of volatile products of the alveolar and airway epithelium. These products include a number of volatile organic compounds (VOCs) and nitric oxide (NO). These may be used as biomarkers to detect the disease. A portable 3-sensor array microsystem-based tool that can potentially detect flu infection biomarkers is described here. Whether used in connection with in-vitro cell culture studies or as a single exhale breathalyzer, this device may be used to provide a rapid and non-invasive screening method for flu and other virus-based epidemics. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>Morphology and structure of <span class="html-italic">h</span>-WO<sub>3</sub> powders: (<b>a</b>) TEM image; (<b>b</b>) HRTEM image (inset: SAED) of nanoparticles; (<b>c</b>) TEM image; (<b>d</b>) HRTEM image (inset: SAED) of nanorods.</p> Full article ">Figure 2
<p>Resistance change of <span class="html-italic">h</span>-WO<sub>3</sub> with exposure to NO, NO<sub>2</sub>, methanol, and isoprene at 350 °C.</p> Full article ">Figure 3
<p>(<b>a</b>) A single sensor readout circuit with Bluetooth module; (<b>b</b>) A three-sensor system with integrated readout and heater control circuit as a step toward wireless handheld multi-sensor breathalyzer.</p> Full article ">
2793 KiB  
Article
Mixed-Potential Gas Sensors Using an Electrolyte Consisting of Zinc Phosphate Glass and Benzimidazole
by Takafumi Akamatsu, Toshio Itoh and Woosuck Shin
Sensors 2017, 17(1), 97; https://doi.org/10.3390/s17010097 - 5 Jan 2017
Cited by 7 | Viewed by 5111
Abstract
Mixed-potential gas sensors with a proton conductor consisting of zinc metaphosphate glass and benzimidazole were fabricated for the detection of hydrogen produced by intestinal bacteria in dry and humid air. The gas sensor consisting of an alumina substrate with platinum and gold electrodes [...] Read more.
Mixed-potential gas sensors with a proton conductor consisting of zinc metaphosphate glass and benzimidazole were fabricated for the detection of hydrogen produced by intestinal bacteria in dry and humid air. The gas sensor consisting of an alumina substrate with platinum and gold electrodes showed good response to different hydrogen concentrations from 250 parts per million (ppm) to 25,000 ppm in dry and humid air at 100–130 °C. The sensor response varied linearly with the hydrogen and carbon monoxide concentrations due to mass transport limitations. The sensor responses to hydrogen gas (e.g., ?0.613 mV to 1000 ppm H2) was higher than those to carbon monoxide gas (e.g., ?0.128 mV to 1000 ppm CO) at 120 °C under atmosphere with the same level of humidity as expired air. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>The structure of the sensor: (<b>a</b>) schematic illustration; and (<b>b</b>) optical image.</p> Full article ">Figure 2
<p>Sensor responses to 25,000 ppm H<sub>2</sub> in humid and dry air at (<b>a</b>) 80 °C; (<b>b</b>) 100 °C; (<b>c</b>) 120 °C; and (<b>d</b>) 130 °C.</p> Full article ">Figure 2 Cont.
<p>Sensor responses to 25,000 ppm H<sub>2</sub> in humid and dry air at (<b>a</b>) 80 °C; (<b>b</b>) 100 °C; (<b>c</b>) 120 °C; and (<b>d</b>) 130 °C.</p> Full article ">Figure 3
<p>Sensor response to different concentrations of H<sub>2</sub> in humid air at 120 °C.</p> Full article ">Figure 4
<p>The relationship between sensor response and the log of the H<sub>2</sub> concentration in humid air at 120 °C. The solid line shows the least-squares linear fit.</p> Full article ">Figure 5
<p>The relationship between the sensor response and the H<sub>2</sub> concentration in humid air at 120 °C. The solid line shows the least-squares linear fit.</p> Full article ">Figure 6
<p>Sensor responses to different concentrations of CO in humid air at 120 °C.</p> Full article ">Figure 7
<p>(<b>a</b>) The relationship between the sensor response and the logarithm of the CO concentration and (<b>b</b>) the relationship between the sensor response and the CO concentration in humid air at 120 °C. The solid lines show the least-squares linear fit.</p> Full article ">
3224 KiB  
Article
A Novel Wireless Wearable Volatile Organic Compound (VOC) Monitoring Device with Disposable Sensors
by Yue Deng, Cheng Chen, Xiaojun Xian, Francis Tsow, Gaurav Verma, Rob McConnell, Scott Fruin, Nongjian Tao and Erica S. Forzani
Sensors 2016, 16(12), 2060; https://doi.org/10.3390/s16122060 - 3 Dec 2016
Cited by 19 | Viewed by 9601
Abstract
A novel portable wireless volatile organic compound (VOC) monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its [...] Read more.
A novel portable wireless volatile organic compound (VOC) monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its sensors. The device was compared to a commercial photo-ionization detector, gas chromatography-mass spectrometry, and carbon monoxide detector. In addition, environmental operational conditions, such as barometric change, temperature change and wind conditions were also tested to evaluate the device performance. The multiple comparisons and tests indicate that the proposed VOC device is adequate to characterize personal exposure in many real-world scenarios and is applicable for personal daily use. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>(<b>a</b>) Device dimension and weight; (<b>b</b>) Pictures of sensor components and user interface on a smart phone.</p> Full article ">Figure 2
<p>Sensor calibration under different concentrations of <span class="html-italic">o</span>-xylene.</p> Full article ">Figure 3
<p>(<b>a</b>) Scanning sensor QR code; (<b>b</b>) Replacement of new sensor cartridge; (<b>c</b>) Real-time fitness and VOCs exposure device data during personal exercise.</p> Full article ">Figure 4
<p>Comparison of the response between the new VOC (TVOC) device with a MIP-QTF sensor and RAE Photo-Ionization Detector (PID) for levels assessed during a trip on Los Angeles Highway 101. NOTE: The response of TVOC was calculated in ppmC using a calibration factor for “outdoor environment with motor vehicle exhaust” (see text for more details), while the response of PID was calculated in ppm using the calibration procedure described in the instrument manual.</p> Full article ">Figure 5
<p>Selectivity validation test to H<sub>2</sub>S with artificial gas sample (<b>a</b>) and gas sample from Mammoth Spring, Yellowstone National Park. Real-time test was done on the new VOC device (<b>b</b>) and gas sample was collected for GC-MS analysis in the lab (<b>c</b>,<b>d</b>). The single peak in the chromatogram confirmed non-significant concentrations of other VOCs, and a presence of H<sub>2</sub>S.</p> Full article ">Figure 6
<p>Outdoor testing of traffic markers as a function of time: (<b>a</b>) Concentration of Carbon Monoxide; (<b>b</b>) Corresponding concentration of total hydrocarbon (HC).</p> Full article ">Figure 7
<p>The new VOC device’s performance to a rapid elevation increase. Starting point and end point of the trip are indicated using dashed-dotted lines.</p> Full article ">Figure 8
<p>(<b>a</b>) VOC device’s real-time hydrocarbon concentration test at point A; (<b>b</b>) Corresponding delta temperature between points A and B. Height between point A and B was 14 ft.</p> Full article ">Figure 9
<p>Wind speed and direction effect on VOC device performance. (<b>a</b>) HC concentration under lower wind speed and wind direction from areas with higher expected concentration (direction A showing in (<b>d</b>), (<b>b</b>) HC concentration under higher wind speed and wind direction from areas with lower expected concentration (direction B showing in (<b>d</b>); (<b>c</b>) peak HC values comparison; (<b>d</b>) a map showing the testing location and wind directions.</p> Full article ">
1185 KiB  
Article
Effects of Sampling Conditions and Environmental Factors on Fecal Volatile Organic Compound Analysis by an Electronic Nose Device
by Daniel J. C. Berkhout, Marc A. Benninga, Ruby M. Van Stein, Paul Brinkman, Hendrik J. Niemarkt, Nanne K. H. De Boer and Tim G. J. De Meij
Sensors 2016, 16(11), 1967; https://doi.org/10.3390/s16111967 - 23 Nov 2016
Cited by 30 | Viewed by 6451
Abstract
Prior to implementation of volatile organic compound (VOC) analysis in clinical practice, substantial challenges, including methodological, biological and analytical difficulties are faced. The aim of this study was to evaluate the influence of several sampling conditions and environmental factors on fecal VOC profiles, [...] Read more.
Prior to implementation of volatile organic compound (VOC) analysis in clinical practice, substantial challenges, including methodological, biological and analytical difficulties are faced. The aim of this study was to evaluate the influence of several sampling conditions and environmental factors on fecal VOC profiles, analyzed by an electronic nose (eNose). Effects of fecal sample mass, water content, duration of storage at room temperature, fecal sample temperature, number of freeze–thaw cycles and effect of sampling method (rectal swabs vs. fecal samples) on VOC profiles were assessed by analysis of totally 725 fecal samples by means of an eNose (Cyranose320®). Furthermore, fecal VOC profiles of totally 1285 fecal samples from 71 infants born at three different hospitals were compared to assess the influence of center of origin on VOC outcome. We observed that all analyzed variables significantly influenced fecal VOC composition. It was feasible to capture a VOC profile using rectal swabs, although this differed significantly from fecal VOC profiles of similar subjects. In addition, 1285 fecal VOC-profiles could significantly be discriminated based on center of birth. In conclusion, standardization of methodology is necessary before fecal VOC analysis can live up to its potential as diagnostic tool in clinical practice. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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<p>(<b>a</b>–<b>g</b>) Scatterplot for the discrimination by electronic nose based on difference in several variables, including: (<b>a</b>) sample mass; (<b>b</b>) number of freeze–thaw cycles; (<b>c</b>) sample temperature; (<b>d</b>) water content; (<b>e</b>) duration of storage at room temperature; (<b>f</b>) rectal swabbing; and (<b>g</b>) center of origin. Axes depicted are orthogonal linear recombinations of the raw sensor data by means of principle component analysis. Illustrated axes solely comprise principle components demonstrated to be statistically significant different for the variable concerned. Individual VOC profiles are illustrated as marked points. The intersection of the lines deriving from the individual profiles demonstrates the mean VOC profile of this specific variable. All evaluated sampling conditions have a significant influence on detected fecal VOC profile, only dilution from 1:2 to 1:5 did not affect outcome. Abbreviations: AMC = Academic medical center; MMC = Maxima Medical Center; VUmc = Vrije Universiteit medical center.</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>–<b>g</b>) Scatterplot for the discrimination by electronic nose based on difference in several variables, including: (<b>a</b>) sample mass; (<b>b</b>) number of freeze–thaw cycles; (<b>c</b>) sample temperature; (<b>d</b>) water content; (<b>e</b>) duration of storage at room temperature; (<b>f</b>) rectal swabbing; and (<b>g</b>) center of origin. Axes depicted are orthogonal linear recombinations of the raw sensor data by means of principle component analysis. Illustrated axes solely comprise principle components demonstrated to be statistically significant different for the variable concerned. Individual VOC profiles are illustrated as marked points. The intersection of the lines deriving from the individual profiles demonstrates the mean VOC profile of this specific variable. All evaluated sampling conditions have a significant influence on detected fecal VOC profile, only dilution from 1:2 to 1:5 did not affect outcome. Abbreviations: AMC = Academic medical center; MMC = Maxima Medical Center; VUmc = Vrije Universiteit medical center.</p> Full article ">
3292 KiB  
Article
Development of an Exhaled Breath Monitoring System with Semiconductive Gas Sensors, a Gas Condenser Unit, and Gas Chromatograph Columns
by Toshio Itoh, Toshio Miwa, Akihiro Tsuruta, Takafumi Akamatsu, Noriya Izu, Woosuck Shin, Jangchul Park, Toyoaki Hida, Takeshi Eda and Yasuhiro Setoguchi
Sensors 2016, 16(11), 1891; https://doi.org/10.3390/s16111891 - 10 Nov 2016
Cited by 61 | Viewed by 7332
Abstract
Various volatile organic compounds (VOCs) in breath exhaled by patients with lung cancer, healthy controls, and patients with lung cancer who underwent surgery for resection of cancer were analyzed by gas condenser-equipped gas chromatography-mass spectrometry (GC/MS) for development of an exhaled breath monitoring [...] Read more.
Various volatile organic compounds (VOCs) in breath exhaled by patients with lung cancer, healthy controls, and patients with lung cancer who underwent surgery for resection of cancer were analyzed by gas condenser-equipped gas chromatography-mass spectrometry (GC/MS) for development of an exhaled breath monitoring prototype system involving metal oxide gas sensors, a gas condenser, and gas chromatography columns. The gas condenser-GC/MS analysis identified concentrations of 56 VOCs in the breath exhaled by the test population of 136 volunteers (107 patients with lung cancer and 29 controls), and selected four target VOCs, nonanal, acetoin, acetic acid, and propanoic acid, for use with the condenser, GC, and sensor-type prototype system. The prototype system analyzed exhaled breath samples from 101 volunteers (74 patients with lung cancer and 27 controls). The prototype system exhibited a level of performance similar to that of the gas condenser-GC/MS system for breath analysis. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
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Figure 1

Figure 1
<p>Average concentrations and confidence intervals (CIs) of 11 VOCs in exhaled air from LC, HC, and LC-S groups. For these 11 VOCs, the <span class="html-italic">p</span> value was less than 0.05 for differences between LC and HC groups and/or between LC and LC-S groups.</p> Full article ">Figure 2
<p>Pt, Pd, and Au/SnO<sub>2</sub> sensor element: (<b>a</b>) front (Pt, Pd, and Au/SnO<sub>2</sub> thick film with platinum comb-type electrode); and (<b>b</b>) back (platinum heater). The substrate size was 4 × 4 mm<sup>2</sup>.</p> Full article ">Figure 3
<p>(<b>a</b>) Prototype system; and (<b>b</b>) flow stream of the prototype system.</p> Full article ">Figure 4
<p>GC/MS spectra of the air of the consultation room in Aichi Cancer Center obtained using: an Analytic Barrier bag (<b>a</b>); and cleaned Tedlar bag (<b>b</b>).</p> Full article ">Figure 5
<p>GC/MS spectra of exhaled breaths from: (<b>a</b>) a patient with lung cancer (IA, Ad); (<b>b</b>) another patient with lung cancer (IIIA, Ad); (<b>c</b>) a healthy control; and (<b>d</b>) the patient from (<b>b</b>) after undergoing surgery.</p> Full article ">Figure 5 Cont.
<p>GC/MS spectra of exhaled breaths from: (<b>a</b>) a patient with lung cancer (IA, Ad); (<b>b</b>) another patient with lung cancer (IIIA, Ad); (<b>c</b>) a healthy control; and (<b>d</b>) the patient from (<b>b</b>) after undergoing surgery.</p> Full article ">Figure 6
<p>GC spectra from (<b>A</b>) column 1 and (<b>B</b>) column 2 of the prototype system used for analysis of several concentrations of: (<b>a</b>) nonanal; (<b>b</b>) acetoin; (<b>c</b>) acetic acid; and (<b>d</b>) propanoic acid. The condensed amounts of two milliliters of 1, 5, 10, and 30 ppm were almost the same as that of 200 mL (exhaled breath analysis) of 10, 50, 100, and 300 ppb VOCs, respectively.</p> Full article ">Figure 7
<p>GC spectra from: (<b>A</b>) column 1; and (<b>B</b>) column 2 of the prototype system of exhaled breath samples from a patient with lung cancer (LC) and a healthy control (HC).</p> Full article ">Figure 8
<p>Percentages of samples having higher concentrations of: (<b>a</b>) nonanal; (<b>b</b>) acetoin; (<b>c</b>) acetic acid; and (<b>d</b>) propanoic acid on the prototype system. Open-rhombus plots from healthy controls were excluded from the evaluation of the <span class="html-italic">C</span><sub>cutoff</sub> because of abnormally high concentrations. LC: patients with lung cancer; HC: healthy controls.</p> Full article ">

Review

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Review
Understanding the Potential of WO3 Based Sensors for Breath Analysis
by Anna Staerz, Udo Weimar and Nicolae Barsan
Sensors 2016, 16(11), 1815; https://doi.org/10.3390/s16111815 - 29 Oct 2016
Cited by 86 | Viewed by 10453
Abstract
Tungsten trioxide is the second most commonly used semiconducting metal oxide in gas sensors. Semiconducting metal oxide (SMOX)-based sensors are small, robust, inexpensive and sensitive, making them highly attractive for handheld portable medical diagnostic detectors. WO3 is reported to show high sensor [...] Read more.
Tungsten trioxide is the second most commonly used semiconducting metal oxide in gas sensors. Semiconducting metal oxide (SMOX)-based sensors are small, robust, inexpensive and sensitive, making them highly attractive for handheld portable medical diagnostic detectors. WO3 is reported to show high sensor responses to several biomarkers found in breath, e.g., acetone, ammonia, carbon monoxide, hydrogen sulfide, toluene, and nitric oxide. Modern material science allows WO3 samples to be tailored to address certain sensing needs. Utilizing recent advances in breath sampling it will be possible in the future to test WO3-based sensors in application conditions and to compare the sensing results to those obtained using more expensive analytical methods. Full article
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Basic picture of the respiratory system (Wikicommons: Respiratory).</p> Full article ">Figure 2
<p>(<b>A</b>) The response of WO<sub>3</sub>-based sensors at 400 °C to 600 ppb of acetone are depicted with respect to Si-content and relative background humidity. A sensor based on 10% mol Si-doped WO3 is optimal. (<b>B</b>) Using this sensor it was possible to detect ultralow concentrations of acetone (20–90 ppb) in 90% RH [<a href="#B16-sensors-16-01815" class="html-bibr">16</a>]. The figure is reprinted from [<a href="#B16-sensors-16-01815" class="html-bibr">16</a>]. Copyright 2010 American Chemical Society.</p> Full article ">Figure 3
<p>STEM and HRTEM of the WO<sub>3−x</sub> nanoneedles functionalized with Fe<sub>2</sub>O<sub>3</sub>. The figure is reprinted with permission from [<a href="#B27-sensors-16-01815" class="html-bibr">27</a>]. Copyright 2015 American Chemical Society.</p> Full article ">Figure 4
<p>Gouma et al. has found that the gas selectivity is strongly dependent on the crystal phase. (<b>A</b>) A raman spectrum taken of the monoclinic γ-WO<sub>3</sub> sample; (<b>B</b>) A XRD spectrum taken of the monoclinic γ-WO<sub>3</sub> sample; (<b>C</b>) Gas sensing response of the monoclinic γ-WO<sub>3</sub> sample to 10 ppm NO, 10 ppm acetone, 10 ppm isoprene, 50 ppm ethanol, 50 ppm methanol, and 50 ppm CO in synthetic air; (<b>D</b>) Gas sensing response of the monoclinic γ-WO<sub>3</sub> sample to 1 ppm, 500 ppb, and 300 ppb NO in synthetic air. Reprinted from [<a href="#B30-sensors-16-01815" class="html-bibr">30</a>] with the permission of AIP Publishing.</p> Full article ">Figure 5
<p>The graphite (0.1 wt%)-WO<sub>3</sub> (<b>A</b>) functionalized hemitubes showed high sensor signals to 2 ppm H<sub>2</sub>S than the graphene oxide (0.1 wt%)-WO<sub>3</sub> (<b>B</b>) functionalized hemitubes. All test gases were measured at 2 ppm and a background humidity of 85%–95%. The figure is reprinted with permission from [<a href="#B35-sensors-16-01815" class="html-bibr">35</a>].</p> Full article ">
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