Sensors and Actuators B 146 (2010) 477482

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Sensors and Actuators B: Chemical

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A. Perera ara Centre de Recb Sists. dInstru CN, Spc Articial Olfac , Spaid Phys. Electr. L zerlan

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Article history:Available onlin

Keywords:Gas sensorsElectronic nosSpoilage proceTemperature m

etalred. Itsh spnt te

1. Introdu

Themosis by means of sensory analysis. Like any human based assessment,this implies some advantages and disadvantages and a brief list ofboth is reported below. On the one hand, as advantages of the sen-sory analysis it can be said that: it is an analysis which is closerto ordinarytage of thethe sensorybe very seninto specigation. On tthe impossness, poorand/or adaptication aopen to disand this fasory analysdevelop altgood performents. Alththe years, othese methprocess tha

CorresponE-mail add

ine tof

measK value (the percentage of the initial ATP concentration at deaththat has been converted by enzyme action into hypoxanthine andits immediate precursor, called inosine, in the chain of decomposi-tion of ATP) [3]. Other chemical methods use the determination of

0925-4005/$ doi:10.1016/j.consumers experience, the assessment takes advan-integration of more than one sense from the assessors,analysis is good at detecting differences, and it can

sitive. Also, sensory analysis is acceptable for writingcations for quality and it is usually acceptable in liti-he other hand, there are some disadvantages such as:ibility of automated measurements, lack of objective-reproducibility, and questions regarding with fatiguetation. Additionally, it is not a good method for quan-

nd the interpretation of results is controversial andpute. Finally, high precision requires several assessorsct is directly related with cost, in other words, sen-is can be expensive [1,2]. It is therefore desirable toernative freshness evaluation methods providing withmance, better features in addition than objective judg-ough many alternative tests have been suggested overnly a few have found practical application. Some ofods are based on the characteristics of the chemicalt are going on in sh tissue. The decomposition of

ding author. Tel.: +34 93 403 91 68; fax: +34 93 402 11 48.ress: apardo@el.ub.es (A. Pardo).

nitrogen, ammonia, dimethylamine, trimethylamine, etc. Howeverpostmortemchanges of shmuscle are complicated. Therefore, it isdifcult to determinesh freshness accurately by a single indicator.Additionally, thesemeasurements usually involve analytical chem-ical instrumentation as high performance liquid chromatography(HPLC) or gas chromatography (GC). Although these techniquesare proven effective in analyzing the volatiles in the spoilage pro-cess, online determination by using these methods is still notfeasible. The complexity, cost and lengthiness of volatile analysismake them suitable only for specialized research and analyticallaboratories.

Other methods are based on physicalelectrical measurements.It is known that the conductivity of the sample tissue increaseswith the evolution of the spoilage. This change in the electrochem-ical properties of skin andmuscle in the degradation process can bemeasuredbymeansof adevicenamedTorrymeter [4,5]. Lougovoisaet al. have recently shown results of sensory evaluations in paral-lel with GR Torrymeter determinations and bacterial counts on seabream samples [6]. Authors report good correlation between a GRTorrymeter and freshness evaluated by the sensory panel. Unfor-tunately, other studies have shown that changes on sh catchingmethods induced different chemical states in sh samples (inosinemonophosphate) where a GR Torrymeter could not provide mean-

see front matter 2009 Elsevier B.V. All rights reserved.snb.2009.11.037tion of sh spoilage by means of a singlature modulationa, A. Pardob,, D. Barrettinod, A. Hierlermannd, S. M

erca en Enginyeria Biomedica (CREB), Pau Gargallo 5, 08028-BCN, Spainmentaci i Comunicacions (SIC), Universitat de Barcelona, Mart i Franqus 1, 08028-Btion Group, Inst. for Bioengineering of Catalonia (IBEC), Baldiri i Rexach 13, 08028-BCNab., Eidgenssische Technisiche Hochschule (ETH), Wolfgang-Pauli 16, 8093-ZRH, Swit

e i n f o

e 22 November 2009

essodulation

a b s t r a c t

In this paper the feasibility of using mfor sea bream (Sparus aurata) is explois able to nd a correlation with the conditions; that is, at low measureme

ction

t standard and acceptedway of determining sh quality

adenoslizationas them/locate /snb

etal oxide sensor under

cob,c

ainnd

oxide gas sensor technology for evaluating spoilage processis shown that a single sensor under temperature modulationoilage process. Results are obtained in real frigoric storage

mperatures with variations of relative humidity. 2009 Elsevier B.V. All rights reserved.

ri-phosphate (ATP), a substance important in the uti-energy in most living things, provides some methodsurement of hypoxanthine or the determination of the

478 A. Perera et al. / Sensors and Actuators B 146 (2010) 477482

Fig. 1. Evol

ingful inforonly on wh

In front osory analysoption for fast evaluatinterestingmetal oxideing temperamodulation

A numbgeneral aspreections;accid, a sli

Howeveprocess is thlight, pleasahave unplea

As the ospoilage prmethod forthe processthe sh (Fig

The anachromatogrsh generaresponsibletheir analys

Apart frmeasure sis the prefeand sensormethods asture measuperformanc

Gas sensization of cspecic comdecade, papsor arrays [[1719]. Fi

in these past years, seeking for alternative markers than humansensory-based evaluation. Jnsdttir et al. have extensively ana-lyzed the volatiles involved in the smoked salmon odor evolution

hese authors proved that selected key volatiles performedas predictors to explain variation in sensory attributes thanonal chemical andmicrobial variables. Sea bass freshness hastudied with electronic noses and chemical markers in food

journals. These studies employed chemical sensor arrayques to prove that the freshness of the sh can be assured forys in commercialization at controlled temperatures [21]. Annal contribution by Barat et al. monitored sea bream fresh-roughapotentiometric sensor. Theuseof thepotentiometrics showed a strong correlation with the K1 index, which isdent on the nucloside degradation (used as a good indicatortmortem time and freshness) [22]. An array of 6 tin oxideTaguchi gas sensors were used by El Barbri et al. to measureshness of Moroccan sardines stored at 4 C [23,24]. A com-with dielectric spectroscopy, and gas sensor array on the

tion of Baltic cod (Gadus morhua) was published by Ken et]. This study built a Quality Index from a prediction modelon a mixed gas sensor (NST 3320, from Applied Sensor, Swe-ith FE-sensors andMOX-sensors), although themulti-sensorch showed large co-linearity.his paper, we seek to nd a correlation with sh spoilagesingle metal oxide sensor, temperature modulated, while

ucinof

l unixidehulean ptes C

ng fa

haved seprod

ope ment

erim

thisin tution proles of the compounds related with whitesh freshness.

mation [7]. Additionally, the instruments can be usedole sh or llets with skin.f this lack of rapid and inexpensive alternatives to sen-is, gas sensors based technology offers an interestingsh freshness evaluation with a low cost and relativelyion when compared to other methods. Additionally, anoption for expanding the amount of information fromgas sensor is based on the modication of the work-ture of the sensingmaterial, usually called temperature[8].

er of features are characteristic of the sh spoilage. Itsect reveals dull pigmentation with absence of shines orthe body shows a soft consistency and is perceived asght pressure by the nger leaves a mark, etc [9].r, one of the main characteristic features of the spoilageeodoremittedby thesh. Freshsamples releasea fresh,nt water weeds characteristic odor, while spoiled shsant, acrid, acid, putrid, ammonia-like intense odor.ff-odors emitted by the sh are related to the shocess, off-odor analysis is considered as potential

[20]. Tbettertraditibeen ssciencetechni34 daadditioness thsensordepenof posbasedthe freparisonevaluaal. [25basedden,wapproa

In tusing areprodlationscontrometal oHochscEuropeintegraapplyirial.

Wecultureof thisin Eurassessm

2. Exp

Forlocatedsh freshness assessment. The characteristic odor fromwill be related to the biochemical changes occurring in. 1).lysis of the sh spoilage process by head-space gasaphic technique shows that the decomposition of thetes sulphur and nitrogen compounds. They are mostlyfor the bad smell perceived by the human nose andis allows an estimation of freshness [10].om gas chromatography (GC), variety of methods toh spoilage is found in the literature. Sensory evaluationrred and most reliable method. However, GC methodsy methods are both expensive and slow. Other fasterphysicalelectrical measurements or automatic tex-

rement machines show high variability and not enoughe.or array approach to this problem aims the character-hemical mixture patterns instead of the detection ofpounds. Several authors have published, in the pasters devoted to sh freshness evaluation with gas sen-1116], and specically with metal oxide sensor arraysh freshness decay studies are still being publishedg realistic conditions for the application (strong oscil-temperature and humidity) typical from refrigeratorts. The sensor employed (CMOSSens) is a prototype of agas sensor designed by the Eidgenssische Technisiche(ETH-Zurich, Switzerland) and developed under the

roject by the same name IST-1999-10579 [2628]. ItMOS circuitry with a metal oxide sensor, capable of

st temperaturemodulation proles to the sensingmate-

focused our attention on the freshness assessment ofa bream (Sparus aurata). The increase on the demanduct and the presence of important farming industriesotivates the research on new methods of freshness

for this particular sh species.

ental

study, sea bream samples were cultured in a sh farmhe Mediterranean (CRIPE S.A., Ametlla de Mar, Spain).

Fig. 2. Image version of the CMOSSens sensor.

A. Perera et al. / Sensors and Actuators B 146 (2010) 477482 479

Fig. 3. Picture of experimental setup for monitoring by using CMOSSens device.

Fish sampleswere slaughtered by hypothermia and dispatched in apolyurethaneboxwith ice toUniversitat deBarcelona. Immediatelyafter receptbottles andsamples wedata acquis

CMOSSesumption,temperaturtechnologyA substantiorder to ma

A versiotric membrarea of 300designed ancular symmlayer. The s5.5V supplytemperaturversions wewas the pro

An elecCMOSSens pware controacquisition

The volume of the sensor chamber was 0.35ml and the owthrough the sensor chamber during the complete experiment was0.3 l/m.

The structure of the system allowed vessel sampling under acontrolled period of time. CMOSSens device was located outsidethe refrigerator andconnected to thebottles byTeon tubing. Therehave been six bottles and an additional channel sampling air fromthe fridge. The different channels were selected using the electro-valve manifold available in ipNose (Fig. 3).

The measurement cycles consisted of 4min air intake from thebottles. The purge of the system was performed with 28min ofclean air sampling. Cycling all bottles lasted for 224min (3h, 44m).The ordering of the intakes from bottles was randomized.

A temperature modulation waveform was used for sensor exci-tation in all CMOSSens experimental stages. The temperaturewaveforms were generated at a periodicity of 16 s. Because thesampling frequency is 2Hz, this feature vector contains 32 points(Fig. 4).

3. Results

As it has been said above, we aim the use of a CMOSSens singleunder temperature modulation instead of a traditional gasarralowSix in thoseuousitioncycleFor ecle os washower (rasety sinhe inof thewavensibiape oA noce forwaveion,shwerecleaned,lleted, stored in six500mlPyrexconnected to themeasurement system. Bottleswith there immediately stored in a fridge and the experimentition proceeded without delay.ns metal oxide sensor offers limited power con-on-board signal acquisition, a digital control for thee of the heater and on-board tests. Metal oxide sensingis not compatiblewith the CMOS technological process.al effort has been done within the CMOSSens project inke both technologies compatible (Fig. 2).n of the CMOSSens sensor is shown in Fig. 2. The dielec-ane is about 500mm by 500mm in size with a heatedmm by 300mm. Although different geometries wered tested by ETHZ, the nal version had a strong cir-etry. The heater is build with the resistive poly-siliconensor achieves a maximum temperature of 400 C at avoltage. Additionally, the CMOSSens sensor contains a

e control of the membrane. Analog and digital controlre designed at ETHZ. The digitally controlled versiontotype used in this paper.tronic nose (ipNose) [29] was adapted to interfacerotocol and control the experiment. The systems soft-lled the application of the temperature proles and theof data from the CMOSSens sensor.

sensorsensorundertions.stored

IpNcontinacquispurgetimes.last cyminutetime isand aftthe datthe daFirst, tmodeof thethe sethe shFig. 6.varianon theFig. 4. (a) Excitation waveform (solid: temperature set point code, dashed: membrany. Additionally we aim to test freshness assessments,temperature conditions and variable humidity condi-llets of sea bream were held in six glass bottles ande fridge during 17 days at a temperature of 52 C.was programmed to execute a total number of 1524acquisitions among the length of the experiment. Eachapplied a 4min sampling of each bottle followed by aof 28min. All six channels (bottle) were analyzed 254ach acquisition, the lastwaveform corresponding to thef temperature modulation within the last 4 samplings taken as a feature vector. The evolution of signals inn in Fig. 5, showing the complete dataset before (left)

ight) normalization plotting the transients (bottom) oras a heat-map (top). The color in Fig. 5c and d points toce the test started. There are two observations to note.crease of the concentration contributes in the commonwaveform. Second, there is a continuous deformationform pattern suggesting that there is a maximum on

lity to spoilage volatiles. This can be corroborated byf the eigenvectors of the PCA decomposition, seen inrmalization was applied to force zero mean and unitevery measurements. This normalization aims to focusformmodication instead of a variation in the common

e temperature) and (b) typical air sensor response.

480 A. Perera et al. / Sensors and Actuators B 146 (2010) 477482

samp

mode, as thincrease or

The chanmeaning asto differentresponse ofwaveform (in the temp

The evotered with u

It can besignals are

Fig

re andepFig. 5. CMOSSens signals for lleted

e common mode contains variation due to the intensitydrift effect.

peratusignalsge in the shape of the waveform has a strong chemicalthe metal oxide sensing layer has different sensitivitiescompounds at different temperatures. The maximumthe sensor is smoothly displaced from the end of thesample 32, maximum temperature) to a point locatederature ramp section.lution in the PCA plot projection of dataset mean cen-nit variance is shown in Fig. 7observed that while at the beginning of the process thebasically dominated by perturbations (probably tem-

. 6. Loadings plot of the signals acquired from channel 1.

of the sh.evolution inferences resame behavPCA plane cthe spoilagdifferent l

Fig. 7. PCA scdays since caple no. 1.

d humidity changeswithin the fridge), fromdayve theart clearly from the initial point, indicating the spoilage

The principal component analysis reveals a non-linearthe gas sensor array overall response. With slight dif-

garding the shape and speed, several bottles show theior. Our interpretation is that a certain position in thean be attributed to a certain state of degradation duringe. However, this state is achieved at different times forlets.

ores of the evolution of lleted sh (normalised). Number indicatesture.

A. Perera et al. / Sensors and Actuators B 146 (2010) 477482 481

Fig. 8. Freshne el 1 (v4.

From thcapture fordata from thof the wavesamples) hahave been ufound optimbetween thin Fig. 8 . Th

Howevedicted signainitial periomodel is apup to 3 dayto real diffein the modchannel byshown in Fi

4. Conclus

The reviement of smethods exthis kind ofthat a charain the degraATP molecubased comp

In thiswthe approprwith the sp

This senand was abHowever, thduring the and check tsh farms bany changein the micro

On the osor was notemitted by ting layer wobetter corre

wled

authr theas b8-09ted bmn

nces

. Bremed, Jolafsd

d, I.Mhness97) 25indsaessingiston (234heyneeriesry adv. Lougssessam (Sattulacatchiengus. Cliffoponse. Johnss (in days) prediction of the sh (a) model: channel 1 (trainset), validation: chann

e present results it is possible to predict the time sincea particular sh. A PLS model has been built by usinge 1st channel. Featurematrix contained the 32 samplesform as described in previous section. Half dataset (112ve been used in building the model and the other halfsed for validation. A number of 4 latent variables wereum for describing the evolution. The correspondence

e real time and thepredicted time in validation is showne RMS error in prediction accounts for 1.5 days.r, we should note that there is a saturation of the pre-ls in the rst days. This is a clear indication that at thisd we are not able to monitor rst changes. If the sameplied to other sh llets the prediction error increasess. However, as commented previously, this can be duerences in the volatiles emission rather than decienciesel. A representation of the time prediction for the 4thusing the model computed with the 1st data channel isg. 8.

ions

wed literature showedanumberofmethods for assess-h freshness. It is shown that, although a variety ofists, there is no accepted objective way for providinganalysis. GCMS analysis found in the literature showscteristic volatile pattern is emitted by the sh samplesdation process. These spoilage patterns are due to thele decomposition change and the generation of sulde

Ackno

TheS.A.) fowork hTIC199supporthe Ra

Refere

[1] H.Amin

[2] G.Olanfres(19

[3] R. LassL. L221

[4] A. CFish

[5] Tor[6] V.P

of abre

[7] T. Honhar

[8] P.Kres

[9] D.D

ounds by bacterial activity.orkwehavedemonstrated thatwith a single sensor andiate signal processing it is possible to nd a correlationoilage process of the sh samples.sor is exposed to the same experimentswith lletedshle to predict the storage day with an error of 1.5 days.is model was unable to distinguish the odor evolutionrst 3 days. Further work should consider other species

he validity of the models with samples coming not fromut from open sea. This may add increased difculty, asin the diet of the animal will be reected in differencesbiological activity triggered after slaughtering.ther side the sensing layer used in the CMOSSens sen-optimized for sensing any of the volatiles known to behedecompositionprocess. Anoptimization of the sens-uld yield to an improved resolution allowing nding alation within the rst days of the process.

season ocharacter

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[16] Gudrun OFood Sciealidation set) and (b) model: channel 1 (trainset), validation: channel

gments

ors wish to acknowledge Sr. Guillermo Bores (CRIPEir kind collaboration providing the fresh samples. Thiseen performed under funding by the IST-1999-10579,78-C03-03 and DPI-2001-3213-C02-0. This work wasy the Spanish Ministerio de Educacin y Ciencia undery Cajal Program.

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Biographies

Alexandre Perera Alexandre Perera was born in Barcelona, Spain, in 1973. Hereceived the physics degree in 1996, the degree in electrical engineering in 2001,and the Ph.D. degree in physics in 2003 from the University of Barcelona. From2003 to 2005, he was a postdoctoral research associate at Texas A&M University,College Station. His research activity is focused in pattern recognition with appli-cations ranging from chemical sensors to power systems, embedded systems, andintelligent instrumentation.

Antonio Pardo Dr. Antonio Pardo received his diploma in physics 1991 and hisPh.D. in 2000 from the University of Barcelona. During his Ph.D. studies he workedin system identication with applications in gas sensor systems. Since 2005 he isassociate professor at the University of Barcelona. His research interest focused onsignal processing for gas sensors and pattern recognition as well as on hardwareand software developments for electronic nose instrumentation.

D. Barrettino Dr. Ing. Diego Barrettino received his diploma in electronic engineer-ing in 1997 from the Universidad de Buenos Aires. From 1997 until 2000 he workedas circuit designer for magnetic sensors in Electrnica Bilotti EnterpriseAllegroMicrosystems Inc. (Worcester, MA, USA). He nished Ph.D. studies at 2004 in Eid-genssische Technisiche Hochschule (ETH Zurich). From 2007 is senior lecturer atElectronics Department in Glasgow University.

A. Hierlermann received the diploma in chemistry in 1992 and the Ph.D. degree inphysical chemistry in1996 fromtheUniversity of Tbingen,Germany.Afterworkingas a postdoctoral researcher at Texas A&M University, College Station, in 1997 andSandia National Laboratories, Albuquerque, NM, in 1998, he is currently an asso-ciate professor at the Physical Electronics Laboratory at ETH Zurich, Switzerland.The focus of his research activities is on CMOS-based microsensors and interfacingCMOS electronics with electrogenic cells.

Santiago Marco Santiago Marco received his diploma in physics in 1988 and hisPh.D. in physics in 1993 from the University of Barcelona. During his Ph.D. studieshe developed a novel micro-machined pressure sensor for biomedical applicationsbased in vertically structured silicon membranes. Since 1995 he is associate pro-fessor at the University of Barcelona. His research interests focuses on the design,simulation and modeling of micro-machined sensors and actuators as well as on thedynamic modeling of chemical sensor arrays.

Evaluation of fish spoilage by means of a single metal oxide sensor under temperature modulationIntroductionExperimentalResultsConclusionsAcknowledgmentsReferencesBiographies