[PDF]
http://dx.doi.org/10.3952/lithjphys.45409
Open access article / Atviros prieigos straipsnis
Lith. J. Phys. 45, 297–305 (2005)
PARAMETERS OF SEMICONDUCTING GAS
SENSORS ACCEPTABLE FOR NON-INVASIVE EARLY DETECTION OF WOUND
INFECTIONS ∗
A. Galdikasa, Ž. Kanclerisa, A. Olekasa,
D. Senulienėa, V. Strazdienėa, A. Šetkusa,
R. Bagdonasb, and R. Rimdeikab
aSemiconductor Physics Institute, A. Goštauto 11,
LT-01108 Vilnius, Lithuania
E-mail: setkus@pfi.lt
bKaunas Medical University Hospital, Eiveinių 2,
LT-50009 Kaunas, Lithuania
Received 27 June 2005
Method of multiexponential decays is used for
approximation of the resistive sensor response to steep change in
gas composition in the atmosphere. The response of tin and indium
oxide thin film sensors is described by a set of the parameters
that are included into the output database of the sensor array.
The dependence of these parameters on the rates of the surface
chemical processes is discussed. The output database is visualised
using an original method of a two-dimensional graphical
representation that is introduced as a “portrait” of smell.
Suitability of these graphical images for identification of the
infected substances and infection type is studied. Based on visual
inspection of the images, the contamination of chicken meat with
bacteria is detectable within several hours after the intentional
infection. Capabilities to distinguish between the clinical
infections of wounds and to diagnose the infection by smell are
studied.
Keywords: metal oxide, electrical properties, odour
detection, diagnostics of infection
PACS: 73.25+i, 73.90+f, 81.05.Rm, 81.15.Cd
∗ The report presented at the 36th Lithuanian National
Physics Conference, 16–18 June 2005, Vilnius, Lithuania
PUSLAIDININKINIŲ DUJŲ JUTIKLIŲ
PARAMETRAI, TINKAMI ANKSTYVAJAM NEINVAZINIAM ŽAIZDŲ INFEKCIJOS
APIBŪDINIMUI
A. Galdikasa, Ž. Kanclerisa, A. Olekasa,
D. Senulienėa, V. Strazdienėa, A. Šetkusa,
R. Bagdonasb, R. Rimdeikab
aPuslaidininkių fizikos institutas, Vilnius, Lietuva
bKauno medicinos universiteto klinikos, Kaunas,
Lietuva
Tirtos galimybės identifikuoti bakterinį
užkrėtimą pagal varžinių dujų jutiklių atsaką į lakiuosius
produktus, išsiskiriančius iš užkrėstosios terpės. Tuo tikslu
plonasluoksnių alavo ir indžio oksido jutiklių aplinkoje yra
staigiai pakeičiama atmosferos sudėtis. Atsako į šį pokytį laikinė
priklausomybė aprašyta daugiaeksponenčio aproksimavimo metodu ir
surasti būdingieji parametrai. Dujų jutiklių komplekto atsakas
apibūdintas rastųjų eksponenčių parametrų rinkiniu, iš kurio
sudaroma šio atsako duomenų bazė. Aptarta tų parametrų
priklausomybė nuo cheminių vyksmų, vykstančių jautriojo sluoksnio
paviršiuje, spartos. Taikant originalų metodą, iš atsako duomenų
bazės sudarytas dvimatis grafikas, kuris pavadintas „kvapo
atvaizdu“. Nagrinėta, kaip šie grafiniai atvaizdai tinka organinių
medžiagų infekuotumui ir infekcijos rūšiai nustatyti. Parodyta,
kad pagal grafinius atvaizdus vištienos užterštumas bakterijomis
nustatomas praėjus kelioms valandoms nuo jos infekavimo. Nagrinėta
galimybė pagal kvapą diagnozuoti žaizdos infekciją ir infekcijos
tipą klinikinėmis sąlygomis.
References / Nuorodos
[1] E. Llobet, J. Brezmes, X. Vilanova, and J. Sueiras, Qualitative
and quantitative anlysis of volatile organic compounds using
transient and steady-state responses of tick-film tin oxide gas
sensor array, Sens. Actuators B 41, 13–21 (1997),
http://dx.doi.org/10.1016/S0925-4005(97)80272-9
[2] D. Baranauskas, A. Galdikas, A. Mironas, A. Šetkus, and D.
Zelenin, Odour identification system based on transient response of
one semiconductor gas sensor, Lithuanian J. Phys. 37,
147–154 (1997)
[3] R. Gutierrez-Osuna, H. Troy Nagle, and S.S. Schiffman, Transient
response analysis of an electronic nose using multi-exponential
models, Sens. Actuators B 61, 170–182(1999),
http://dx.doi.org/10.1016/S0925-4005(99)00290-7
[4] T. Eklöv, P. Mårtensson, and I. Lundström, Selection of
variables for interpreting multivariate gas sensor data, Anal. Chim.
Acta 381, 221–232 (1999),
http://dx.doi.org/10.1016/S0003-2670(98)00739-9
[5] A. Galdikas, A. Mironas, D. Senulienė, and A. Šetkus, Specific
set of the time constants for characterisation of organic volatile
compounds in the output of metal oxide sensors, Sens. Actuators B 68,
335–343 (2000),
http://dx.doi.org/10.1016/S0925-4005(00)00454-8
[6] A. Galdikas, A. Mironas, D. Senuliene, V. Strazdiene, A. Setkus,
and D. Zelenin, Response time based output metal oxide gas sensors
applied to evaluation of meat freshness with neural signal analysis,
Sens. Actuators B 69, 258–265 (2000),
http://dx.doi.org/10.1016/S0925-4005(00)00505-0
[7] C. Distante, M. Leo, P. Siciliano, and K.C. Persaud, On the
study of feature extraction methods for an electronic nose, Sens.
Actuators B 87, 274–288 (2002),
http://dx.doi.org/10.1016/S0925-4005(02)00247-2
[8] A. Galdikas, Ž. Kancleris, D. Senulienė, and A. Šetkus,
Influence of heterogeneous reaction rate on response kinetics of
metal oxide gas sensors: Application to the recognition of an odour,
Sens. Actuators B 95, 244–251 (2003),
http://dx.doi.org/10.1016/S0925-4005(03)00434-9
[9] J.W. Gardner, Hyun Woo Shin, and E.L. Hines, An electronic nose
system to diagnose illness, Sens. Actuators B 70, 19–24
(2000),
http://dx.doi.org/10.1016/S0925-4005(00)00548-7
[10] C.M. McEntegart, W.R. Penrose, S. Strathmann, and J.R. Stetter,
Detection and discrimination of coliform bacteria with gas sensor
arrays, Sens. Actuators B 70, 170–176 (2000),
http://dx.doi.org/10.1016/S0925-4005(00)00561-X
[11] N. Magan, A. Pavlou, and I. Chrysanthakis, Milk-sense: A
volatile sensing system recognises spoilage bacteria and yeasts in
milk, Sens. Actuators B 72, 28–34 (2001),
http://dx.doi.org/10.1016/S0925-4005(00)00621-3
[12] M. Holmberg, F. Gustafsson, E.G. Hornsten, F. Winquist, L.E.
Nilsson, L. Ljung, and I. Lundstrom, Bacteria classification based
on feature extraction from sensor data, Biotechnol. Tech. 12,
319–324 (1998),
http://dx.doi.org/10.1023/A:1008862617082
[13] A. Šetkus, Heterogeneous reaction rate based description of the
response kinetics in metal oxide gas sensors, Sens. Actuators B 87,
348–359 (2002),
http://dx.doi.org/10.1016/S0925-4005(02)00269-1
[14] K. Tittelbach-Helmrich, An integration method for the analysis
of multiexponential transient signals, Meas. Sci. Technol. 4,
1323–1329 (1993),
http://dx.doi.org/10.1088/0957-0233/4/12/003
[15] A.A. Istratov and O.F. Vyvenko, Exponential analysis in
physical phenomena, Rev. Sci. Instrum. 70, 1233–1257 (1999),
http://dx.doi.org/10.1063/1.1149581
[16] W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery,
Numerical Recipes in C (Cambridge University Press,
Cambridge, 1994) p. 120
[17] T. Maekawa, K. Suzuki, T. Takada, T. Kobayashi, and M.
Egashira, Odor identification using SnO2-based sensor array, Sens.
Actuators B 80, 51–58 (2001),
http://dx.doi.org/10.1016/S0925-4005(01)00885-1
[18] J. Mizsei and S. Ress, Chemical images by artificial olfactory
epithelia, Sens. Actuators B 83, 164–168 (2002),
http://dx.doi.org/10.1016/S0925-4005(01)01035-8
[19] A. Šetkus, C. Baratto, E. Comini, G. Faglia, A. Galdikas, Ž.
Kancleris, G. Sberveglieri, and D. Senulienė, Influence of metallic
impurities on response kinetics in metal oxide thin film gas
sensors, Sens. Actuators B 103, 448–456 (2004),
http://dx.doi.org/10.1016/j.snb.2004.05.004