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Within-day and between-day repeatability of measurements with an electronic nose in patients

with COPD

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IOP PUBLISHING JOURNAL OF BREATH RESEARCH

J. Breath Res. 7 (2013) 017103 (8pp) doi:10.1088/1752-7155/7/1/017103

Within-day and between-day repeatabilityof measurements with an electronic nosein patients with COPDMaria Bofan1, Nadia Mores2, Marco Baron1, Malgorzata Dabrowska2,Salvatore Valente3, Maurizio Schmid1, Andrea Trové4, Silvia Conforto1,Gina Zini5, Paola Cattani6, Leonello Fuso3, Antonella Mautone3,Chiara Mondino7, Gabriella Pagliari3, Tommaso D’Alessio 1

and Paolo Montuschi3,8

1 Department of Applied Electronics, Faculty of Engineering, University of Roma 3, Rome, Italy2 Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Rome,

Italy3 Department of Internal Medicine and Geriatrics, Faculty of Medicine, Catholic University of the Sacred

Heart, Rome, Italy4 Department of Medicine, ‘San Carlo di Nancy’ Hospital, Istituto Dermopatico dell’Immacolata, IDI,

Rome, Italy5 Department of Hematology, Faculty of Medicine, Catholic University of the Sacred Heart, Rome, Italy6 Department of Microbiology, Faculty of Medicine, Catholic University of the Sacred Heart, Rome, Italy7 Department of Immunodermatology, Istituto Dermopatico dell’Immacolata, IDI, Rome, Italy

E-mail: [email protected]

Received 14 September 2012

Accepted for publication 28 November 2012

Published 27 February 2013

Online at stacks.iop.org/JBR/7/017103

Abstract

Electronic noses (e-noses), artificial sensor systems generally consisting of chemical sensor

arrays for the detection of volatile compound profiles, have potential applications in

respiratory medicine. We assessed within-day and between-day repeatability of an e-nose

made from 32 sensors in patients with stable chronic obstructive pulmonary disease (COPD).

We also compared between-day repeatability of an e-nose, fraction of exhaled nitric oxide

(FENO) and pulmonary function testing. Within-day and between-day repeatability for the

e-nose was assessed in two breath samples collected 30 min and seven days apart, respectively.

Repeatability was expressed as an intraclass correlation coefficient (ICC). All sensors had ICC

above 0.5, a value that is considered acceptable for repeatability. Regarding within-day

repeatability, ICC ranged from 0.75 to 0.84 (mean = 0.80 ± 0.004). Sensors 6 and 19 were

the most reproducible sensors (both, ICC = 0.84). Regarding between-day repeatability, ICCranged from 0.57 to 0.76 (mean = 0.68 ± 0.01). Sensor 19 was the most reproducible sensor

(ICC = 0.76). Within-day e-nose repeatability was greater than between-day repeatability

( P < 0.0001). Between-day repeatability of FENO (ICC = 0.91) and spirometry (ICC range =

0.94–0.98) was greater than that of e-nose (mean ICC = 0.68). In patients with stable COPD,

the e-nose used in this study has acceptable within-day and between-day repeatability which

varies between different sensors.

(Some figures may appear in colour only in the online journal)

8 Author to whom any correspondence should be addressed.

1752-7155/13/017103+08$33.00 1 © 2013 IOP Publishing Ltd Printed in the UK & the USA

http://dx.doi.org/10.1088/1752-7155/7/1/017103mailto:[email protected]://stacks.iop.org/JBR/7/017103http://stacks.iop.org/JBR/7/017103mailto:[email protected]://dx.doi.org/10.1088/1752-7155/7/1/017103


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J. Breath Res. 7 (2013) 017103 M Bofan et al

Introduction

Several volatile organic compounds (VOCs), including

isoprene, 1,2-pentadiene, acetone, ethanol, pentane and

ethane, have been identified in exhaled breath in

healthy subjects and patients with respiratory disease by

gas-chromatography/mass spectrometry (GC/MS) [1, 2].Identification of selective patterns of VOCs in exhaled breath

could be used as a biomarker of respiratory diseases [3–7].

An electronic nose (e-nose) is an artificial sensor system

that generally consists of an array of chemical sensors for

detection of volatile compound profiles (breathprints) and

an algorithm for pattern recognition [8–11]. E-noses can be

handheld, portable devices that provide immediate results and

have potential applications in respiratory medicine [12, 13].

However, e-nose data analysis generally requires complex

multivariate statistical methods [14]. E-nose is potentially

useful for discriminating between asthmatic patients and

healthy subjects [3, 15], between patients with asthma of

different severity [15], between patients with lung cancer and

chronic obstructive pulmonary disease (COPD) [16], between

patients with asthma and COPD [4, 17], and between patients

with lung cancer and healthy subjects and/or patients with

non-cancer lung disease [5, 6, 18–20]. E-nose breathprinting

is associated with airway inflammation activity and has been

proposed as a new non-invasive tool for assessing lung

inflammation in patients with respiratory disease, including

COPD [21]. Assessing e-nose repeatability in patients with

COPD, in whom it is likely to be applied as a new diagnostic

technique, is important as (1) good within-day repeatability

for a chemical sensor array in healthy subjects was reported

in a previous study [4], but no quantitative data on between-day repeatability of e-noses are available; (2) chronic airway

inflammation, the pathophysiological hallmark of COPD, is

present in patients with COPD, but not in healthy subjects.

Being a dynamic process, chronic airway inflammation might

affect between-day repeatability of e-nose measurement to a

greater extent than that in healthy subjects; (3) VOC patterns

detected by e-nose are related to the type of inflammation

[21]. In patients with COPD, airway inflammation generates

a pattern of volatile compounds which is different not only

from that observed in healthy subjects [4], but also from

that observed in patients with asthma [4, 17] and lung cancer

[16]. As a result, sensor response and repeatability in patientswith COPD, other respiratory diseases and healthy subjects

might be different; (4) in patients with COPD, the persistent

airway obstruction which characterizes COPD might affect

breath sampling and e-nose analysis. As forced vital capacity

(FVC) in patients with COPD is generally lower than that in

healthy subjects, breath sample volumes collected in patients

with COPD are usually lower than those collected in healthy

subjects. This might affect filling and volatile distribution in

the bags used for collecting breath samples and, consequently,

e-nose analysis.

In this study, the primary objective was to assess within-

day and between-day repeatability of e-nose measurements

in patients with stable COPD. The secondary objective wasto compare between-day repeatability of e-nose, fraction of

Table 1. Subjects’ characteristicsa.

n 24Age, years 68 ± 1.7Sex, females/males 5/19Smoking habitCurrent smokers/ex-smokers/never smokers 0/24/0Smoking history, pack years 39.5 (24.2–63.3)

Atopy, yes/no 0/24GOLD stage 1/2/3 7/11/6Inhaled corticosteroids, yes/no 0/24Oral corticosteroids, yes/no 0/24

a Data are numbers, mean ± SEM, or medians and interquartileranges (25th and 75th percentiles).

exhaled nitric oxide (FENO) and pulmonary function testing

and within-day repeatability of e-nose and FENO.

Methods

Study subjects

Twenty-four patients with COPD were included (table 1).

Diagnosis and classification of COPD was based on GOLD

guidelines [22]. Diagnosis of COPD was based on the

history of smoking, symptoms of dyspnea, cough and sputum

production, and the presence of post-bronchodilator forced

expiratory volume in 1 s (FEV1)/FVC


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Figure 1. Setup for breath sampling.

interval. Before breath sampling for e-nose analysis, FENOmeasurement was performed. Considering this study design,within-day repeatability of spirometry was not assessed as

the short time interval between measures (30 min) madeit unlikely for any changes to occur in pulmonary functiontesting. Interventions were performed in the following order:FENO measurement and breath sampling for e-nose analysis.

Before breath sampling, subjects were asked to refrainfrom eating and drinking (except water) for at least 12 h andto stop short-acting bronchodilators for at least 12 h and long-acting bronchodilators for at least 24 h. Breath sampling ande-nose analysis were performed in a windowless conferencefacility in the Clinical Pharmacology Unit, University HospitalAgostino Gemelli, Catholic University of the Sacred Heart,Rome, Italy, controlled by a central ventilation system andwithout disinfectant dispensers or person traffic. Skin prick

testing was performed at visit 1.Informed consent was obtained from patients. The study

was approved by the Ethics Committee of the UniversityHospital Agostino Gemelli, Catholic University of the SacredHeart, Rome, Italy.

Collection of exhaled breath

Exhaled breath was collected through mixed expiratorysampling in which total breath, including dead space air, iscollected. An equilibration phase (wash-in) with VOC-filteredroom air was performed before breath sampling to reducethe interference of ambient VOCs [15]. Subjects were asked

to breathe tidally VOC-filtered air for 5 min, while wearinga nose clip, into a two-way non-rebreathing valve with aninspiratory VOC filter and an expiratory silica reservoir toreduce sample water vapor as this could affect sensor response[8, 14] (figure 1). Then, subjects were asked to inhale tomaximal inspiration and perform a FVC maneuver into aTedlar bag against an expiratory resistance of 20 cm H2Oto close the soft palate and obtain an expiratory flow of 0.1–0.2 pL/S [4, 15]. This breath sampling procedure minimizesthe effect of ambient VOCs on e-nose analysis.

Electronic nose analysis

The setup for e-nose analysis used in this study consists of ane-nosewith onboard softwarefor data analysis,a collecting bag

Figure 2. Setup for the breath analysis with an e-nose consisting of 32 chemical sensors made from composites of an inorganicconductor (carbon black) and insulating organic polymers(Cyranose 320 R, Smiths Detection, Pasadena).

containing VOC-filtered ambient air for baseline measurement

and a collecting bag containing the breath sample (figure 2).

A commercially available e-nose (Cyranose 320 R, Smiths

Detection, Pasadena, USA, currently produced by IOS,

Baldwin Park, USA) was used for breath analysis (figure 2).

This e-nose consists of an array of 32 chemical sensors made

from composites of an inorganic conductor (carbon black) and

insulatingorganic polymers [23]. Themeasurement is based on

a resistance variation in each chemical sensor when exposed

to breath VOCs. A typical breathprint is shown in figure 3.

E-nose analysis was performed immediately after breath

sample collection. Each breath sample was analyzed fivetimes with the same e-nose. Data from the first measure were

discarded as suggested by the manufacturer and reported in

previous studies [4, 15]. E-nose responses for each sensor

(changes in resistance) were stored in the e-nose in-built

database. Due to the limited data storage capacity of Cyranose

320, data were copied into a Matlab file and analyzed offline

with a pattern recognition algorithm.

Pulmonary function

Spirometry was performed with a Pony FX spirometer

(Cosmed, Rome, Italy) and the best of at least three acceptable

FVC maneuvers chosen [24]. Acceptable repeatability is

achieved when both the two largest FVC and FEV1 values,

respectively, are within 0.15 L of each other (0.1 L for subjects

with a FVC 1.0 L) [24].

Exhaled nitric oxide measurement

FENO was measured with the NIOX system (Aerocrine,

Stockholm, Sweden) with a single breath on-line method at

a constant flow of 50 mL s−1 according to American Thoracic

Society guidelines [25, 26]. Exhalations were repeated after

a 1 min relaxation period until the performance of three

FENO values varied less than 10%. FENO measurements wereobtained before spirometry.

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Figure 3. A typical breathprint from a patient with COPD obtained with an e-nose (Cyranose 320). Upward deflections represent the sensorresponses to patient breath sample expressed as changes in sensor resistance ( R). The actual response ( R) for individual sensors iscalculated by subtracting response to VOC-free air (baseline) from patient breath sample response (sampling). A wash-out phase is

performed between baseline and sampling phases.

Skin testing

Atopy was assessed by skin prick tests for common

aeroallergens (Stallergenes, Antony, France).

Statistical analysis

Data distribution (normal versus non-parametric) was assessed

with Lillifor test for normality. The Lillifor test for normality,

an adaptation of the Kolmogorov–Smirnov test, is used to

test the null hypothesis that data come from a normally

distributed population, when the null hypothesis does not

specify the expected value and variance of the distribution

[27]. Normally distributed values (age, spirometry, sensor

resistance changes) were expressed as mean ± SEM. Non-

parametric values (FENO) were expressed as medians and

interquartile ranges (25th and 75th percentiles). For each

of the 32 e-nose sensors, the mean response (change in

sensor resistance) of four consecutive measures from the

same breath sample was considered for the analysis. Within-

day repeatability and between-day repeatability were assessed

by calculating intraclass correlation coefficients (ICCs) using

one-way analysis of variance. ICC provides a scalar measure

of agreement by assessing sensor repeatability [28, 29]. Valuesrange from 0 (no agreement) to 1 (perfect agreement).

There are no universally accepted standards for

interpreting repeatability [30]. In our study, the degree of

concordance between measures was rated on the basis of the

following standards for ICC:0.5 for acceptable repeatability, which is higher than that (>

0.4) proposed by Fleiss [32] and Landis and Koch [33] for

Cohen’s kappa coefficient, a measure of inter-rater agreement

for categorical items.

The intra-rater and inter-rater analysis was calculated asfollows [29]:

ICC= true score variance/(true score variance+ variance

because of raters + error score variance).

Thetrue score is definedas the mean acrossmany repeated

ratings on each target (object of measurement) [29].

The ICC (3,k) model for multiple raters was adopted in

which each target was measured by the same rater and

this rater was the only rater of interest [29].

Data were a matrix where rows represent subjects (n = 24)

and columns represent times of two breath samplings (raters),

performed 30 min (within-day repeatability) or seven daysapart (visits 1 and 2, between-day repeatability).

4


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E-noses are generally used for classification purposes

[9, 12, 13]. In this case, the differential responses across the

sensor array (resistance shifts) are composed in patterns and

analyzed by pattern recognition algorithms [14]. In this study,

the pattern recognition analysis was not required as we aimed

at assessing the repeatability of an e-nose in patients with

COPD and not its ability to discriminate between differentsubject groups. To increase accuracy of measurement, five

consecutive measures were taken from each breath sample

and the mean values of four measures (the first measure was

discarded as suggested by the manufacturer) were included in

the analysis in analogy with a previous study [5]. The mean

values of each of the 32 sensors, representing the mean change

in their resistance (sensor response), were obtained at two time

points (within-day repeatability: 0 and 30 min; between-day

repeatability: day 1 and day 7) and used for calculating ICCs

as described above. FENO measurement and spirometry were

performed at the same time points.

Mean ICC values expressing within-day repeatability and

mean ICC values expressing between-day repeatability of e-nose sensors were compared with the unpaired t -test.

A between-group comparison was performed by the

paired t -test or Wilcoxon matched pairs signed rank test based

on normal or non-parametric data distribution, respectively

[34]. P value


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(A)

(B )

Figure 4. Within-day repeatability ( A) and between-day repeatability ( B) of an e-nose. On the x -axis, sensor numbers are shown. On the y-axis, ICC values used for assessing repeatability are shown. A threshold value for acceptable repeatability was set at ICC > 0.5. Sensorshaving acceptable repeatability are indicated by an asterisk.

Table 3. Between-day repeatability of FENO measurement andpulmonary function testing in patients with COPD (n = 24).

Measure 1 Measure 2 P value ICC

Sensor 19 0.77 ± 0.04 0.80 ± 0.05 0.37 0.76delta R, a

FENO, ppb 22.2 (11.6–40.8) 18.8 (16.3–37.6) 0.99 0.90FEV1, L 1.93 ± 0.14 1.95 ± 0.15 0.77 0.98FEV1,% 69.8 ± 3.5 69.3 ± 3.9 0.70 0.97

predicted valueFVC, L 3.04 ± 0.19 3.02 ± 0.19 0.65 0.98FVC,% 84.5 ± 3.4 84.2 ± 3.6 0.80 0.96predicted valueFEV1/FVC,% 64.7 ± 2.0 64.1 ± 1.9 0.47 0.94

Values are mean ± SEM or medians and interquartile ranges(25th–75th) depending on normal or non-parametric datadistribution, respectively.a Values are mean ± SEM of mean values of four consecutivemeasures taken from each breath sample (see the text). Delta R =sensor change in resistance.

than 0.5. Twelve out of 32 sensors had excellent to perfect

within-day repeatability (ICC range = 0.81–0.84), whereassubstantial repeatability was observed with the other sensors

(ICC range = 0.75–0.80). All but sensor 6 (ICC = 0.75) had

ICCs greater than 0.75, a value which is considered excellent

by Fleiss [32]. Sensors 6 and 19 (both, ICC = 0.84) were

the best performing sensors. ICC values ranged from 0.75 to

0.84 (mean ICC = 0.80). These values are similar (ICC =

0.65–0.91, mean 0.80) to those reported in a previous study

in which breath analysis was performed in healthy subjects

with the same type of e-nose used in our study under similar

experimental conditions [4].

Regarding between-day repeatability of e-nose, 29 sensors

had substantial repeatability (ICC range = 0.62–0.76),

whereas 3 sensors had moderate repeatability (ICC range =0.57–0.60). Sensor 19, the best performing sensor, had an

ICC value of 0.76 which is above the threshold for excellent

repeatability [32]. ICC values ranged from 0.57 to 0.76 (mean

ICC = 0.68). In a previous study, no breathprint differences

were reported with the same e-nose used in our study when

two breath samples, taken 1 to 48 days apart from healthy

smoker and non-smoker subjects, were analyzed [4]. However,

no quantitative data on between-day repeatability of this

e-nose are available. Likewise, there are no data on between-

day repeatability of other e-noses applied to breath analysis.In our study, when all sensors were considered, within-

day repeatability was significantly greater than between-day

repeatability ( P < 0.0001). This might be due to day-to-day

biological variability in endogenous metabolism as reflected

by breath VOCs rather than e-nose variability (imprecision) in

view of thefact that theagreement between twomeasurestaken

30 min apart was excellent. Changes in breathing patterns and

degree of airflow limitationmightaffect e-nose repeatability. In

our study, breathing pattern variations are unlikely to decrease

between-day e-nose repeatability as patients with COPD were

clinically and functionally stable at one week distance as

reflected by no symptom changes and between-day ICC valuesfor lung function tests ranging from 0.94 to 0.98. Moreover, in

our study, breath sampling was performed by asking subjects

to breath tidally and regularly for a certain period of time

(5 min) as suggested by Miekisch et al [12]. This procedure

minimizes possible variability due to single breath differences

and breathing patterns [12].

On the other hand, an effect of airflow limitation severity

on e-nose repeatability in patients with COPD seems to

be excluded by the fact that within-day and between-day

repeatability in the subgroups of subjects with FEV1 lower

or higher than 50% of the predicted value was similar. Our

findings are in line with a previous study which showed that

e-nose breathprints in patients with asthma are not affected byacute changes in airway caliber [35].

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Taken together, these data indicate that the chemical

sensor array used in this study has excellent within-

day repeatability and acceptable between-day repeatability

in patients with stable COPD. Individual sensors behave

differently in terms of repeatability, but the implications of

these findings are currently unknown. Focusing on selected

sensors (e.g., the most reproducible) might be required.However, the best performing sensors in terms of repeatability

might not necessarily be the most informative in terms of

classification. In this study, the e-nose was not used for

classification as we aimed at assessing sensor between-day

repeatability and within-day repeatability and comparing e-

nose repeatability with FENO measurement and lung function

tests. Addressing the issue of possible sensor redundancy

[8], that is the presence of sensors that do not significantly

contribute to e-nose classification ability, was beyond the scope

of our study. However, this relevant issue has to be formally

addressed in future classification studies.

Measurement of FENO is a technique which has been

approved for non-invasive assessment and monitoring of

airway inflammation in the clinical setting in patients with

asthma [36], but its utility in patients with COPD is uncertain.

In this study, the FENO measurement showed excellent within-

day and between-day repeatability (ICC values = 0.99 and

0.91, respectively), which was greater than that observed with

e-nose. These findings might be explained, at least partially, by

the differences in working principles between FENO analyzers

and e-noses. The former are based on a selective sensor for a

single molecule (NO). By contrast, e-noses generally consist

of an array of chemical sensors which are globally selective for

a complex mixture of VOCs in the exhaled breath, that is each

sensor binds selected volatiles and each volatile is selectivelybound by more sensors. Alternatively, or in addition to that,

the lower repeatability of e-nose compared to FENO might

reflect the higher capacity of e-nose to detect subtle airway

inflammatory changes.

In comparison with FENO measurement and e-nose, we

observed that pulmonary function parameters were the most

reproducible when spirometry was repeated after seven days

(ICC values ranging from 0.94 to 0.99). These findings are in

line with the clinical stability of patients with COPD included

in this study.

When assessing a dynamic biological phenomenon

such as respiratory inflammation, almost perfect day-to-dayrepeatability, as that observed with pulmonary function testing

in our study, does not necessarily translate into an advantage.

Pulmonary function testing is a routine technique for clinical

assessment of patients with respiratory disease, but may be

insufficiently sensitive to detect changes in lung inflammation

that may precede symptom and functional changes. In a

previous study, e-nose breathprints were associated with

airway inflammation activity as reflected by sputum eosinophil

cationic protein and myeloperoxidase in patients with mild

COPD [21]. E-noses might provide a non-invasive and

sensitive tool for assessing and monitoring of respiratory

inflammation, the pathophysiological hallmark of COPD.

In combination with GC/MS [21] and NMR spectroscopy[37, 38] or liquid chromatography/MS [39], which are

suitable for identification and quantification of volatile and

semivolatile/non-volatile compounds in the gaseous and

liquid phase (exhaled breath condensate) of the exhaled

breath, respectively, e-noses might provide new insights into

the pathophysiology of pulmonary disease. This integrated

approach known as breathomics, the molecular analysis of

the exhaled breath, might also be useful for identifyingthe inflammatory subphenotype and activity in patients with

COPD [21].

In conclusion, the e-nose that was used in our

study in patients with stable COPD has excellent within-

day repeatability and acceptable between-day repeatability.

Sensors have different repeatability. Similar studies aimed

at testing the repeatability of different e-noses in patients

with different respiratory diseases or COPD exacerbations

are warranted. Large controlled studies are required for

establishing the utility of e-noses for assessing and monitoring

the response to pharmacological treatment in patients with

respiratory disease, including COPD.

Acknowledgments

This work was supported by PRIN 2007 and Catholic

University of the Sacred Heart, Fondi di Ateneo 2009–2011.

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