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Sensors and Actuators B 146 (2010) 477–482

Contents lists available at ScienceDirect

Sensors and Actuators B: Chemical

journa l homepage: www.e lsev ier .com/ locate /snb

valuation of fish spoilage by means of a single metal oxide sensor underemperature modulation

. Pereraa, A. Pardob,∗, D. Barrettinod, A. Hierlermannd, S. Marcob,c

Centre de Recerca en Enginyeria Biomedica (CREB), Pau Gargallo 5, 08028-BCN, SpainSists. dıInstrumentació i Comunicacions (SIC), Universitat de Barcelona, Martí i Franquès 1, 08028-BCN, SpainArtificial Olfaction Group, Inst. for Bioengineering of Catalonia (IBEC), Baldiri i Rexach 13, 08028-BCN, SpainPhys. Electr. Lab., Eidgenössische Technisiche Hochschule (ETH), Wolfgang-Pauli 16, 8093-ZRH, Switzerland

r t i c l e i n f o a b s t r a c t

rticle history:vailable online 22 November 2009

eywords:as sensorslectronic nose

In this paper the feasibility of using metal oxide gas sensor technology for evaluating spoilage processfor sea bream (Sparus aurata) is explored. It is shown that a single sensor under temperature modulationis able to find a correlation with the fish spoilage process. Results are obtained in real frigorific storageconditions; that is, at low measurement temperatures with variations of relative humidity.

© 2009 Elsevier B.V. All rights reserved.

poilage processemperature modulation

. Introduction

The most standard and accepted way of determining fish qualitys by means of sensory analysis. Like any human based assessment,his implies some advantages and disadvantages and a brief list ofoth is reported below. On the one hand, as advantages of the sen-ory analysis it can be said that: it is an analysis which is closero ordinary consumers experience, the assessment takes advan-age of the integration of more than one sense from the assessors,he sensory analysis is good at detecting differences, and it cane very sensitive. Also, sensory analysis is acceptable for writing

nto specifications for quality and it is usually acceptable in liti-ation. On the other hand, there are some disadvantages such as:he impossibility of automated measurements, lack of objective-ess, poor reproducibility, and questions regarding with fatiguend/or adaptation. Additionally, it is not a good method for quan-ification and the interpretation of results is controversial andpen to dispute. Finally, high precision requires several assessorsnd this fact is directly related with cost, in other words, sen-ory analysis can be expensive [1,2]. It is therefore desirable toevelop alternative freshness evaluation methods providing withood performance, better features in addition than objective judg-

ents. Although many alternative tests have been suggested over

he years, only a few have found practical application. Some ofhese methods are based on the characteristics of the chemicalrocess that are going on in fish tissue. The decomposition of

∗ Corresponding author. Tel.: +34 93 403 91 68; fax: +34 93 402 11 48.E-mail address: apardo@el.ub.es (A. Pardo).

925-4005/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.snb.2009.11.037

adenosine tri-phosphate (ATP), a substance important in the uti-lization of energy in most living things, provides some methodsas the measurement of hypoxanthine or the determination of theK 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 ofnitrogen, ammonia, dimethylamine, trimethylamine, etc. Howeverpostmortem changes of fish muscle are complicated. Therefore, it isdifficult to determine fish freshness accurately by a single indicator.Additionally, these measurements 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 physical–electrical 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 and muscle in the degradation process can bemeasured by means of a device named Torrymeter [4,5]. Lougovoisaet al. have recently shown results of sensory evaluations in paral-lel with GR Torrymeter determinations and bacterial counts on sea

bream 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 fish catchingmethods induced different chemical states in fish samples (inosinemonophosphate) where a GR Torrymeter could not provide mean-

478 A. Perera et al. / Sensors and Actua

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2. Experimental

For this study, sea bream samples were cultured in a fish farmlocated in the Mediterranean (CRIPE S.A., Ametlla de Mar, Spain).

Fig. 1. Evolution profiles of the compounds related with whitefish freshness.

ngful information [7]. Additionally, the instruments can be usednly on whole fish or fillets with skin.

In front of this lack of rapid and inexpensive alternatives to sen-ory analysis, gas sensors based technology offers an interestingption for fish freshness evaluation with a low cost and relativelyast evaluation when compared to other methods. Additionally, annteresting option for expanding the amount of information from

etal oxide gas sensor is based on the modification of the work-ng temperature of the sensing material, usually called temperature

odulation [8].A number of features are characteristic of the fish spoilage. Its

eneral aspect reveals dull pigmentation with absence of shines oreflections; the body shows a soft consistency and is perceived asaccid, a slight pressure by the finger leaves a mark, etc [9].

However, one of the main characteristic features of the spoilagerocess is the odor emitted by the fish. Fresh samples release a fresh,

ight, pleasant water weeds characteristic odor, while spoiled fishave unpleasant, acrid, acid, putrid, ammonia-like intense odor.

As the off-odors emitted by the fish are related to the fishpoilage process, off-odor analysis is considered as potentialethod for fish freshness assessment. The characteristic odor from

he process will be related to the biochemical changes occurring inhe fish (Fig. 1).

The analysis of the fish spoilage process by head-space gashromatographic technique shows that the decomposition of thesh generates sulphur and nitrogen compounds. They are mostlyesponsible for the bad smell perceived by the human nose andheir analysis allows an estimation of freshness [10].

Apart from gas chromatography (GC), variety of methods toeasure fish spoilage is found in the literature. Sensory evaluation

s the preferred and most reliable method. However, GC methodsnd sensory methods are both expensive and slow. Other fasterethods as physical–electrical measurements or automatic tex-

ure measurement machines show high variability and not enougherformance.

Gas sensor array approach to this problem aims the character-zation of chemical mixture patterns instead of the detection of

pecific compounds. Several authors have published, in the pastecade, papers devoted to fish freshness evaluation with gas sen-or arrays [11–16], and specifically with metal oxide sensor array17–19]. Fish freshness decay studies are still being published

tors B 146 (2010) 477–482

in these past years, seeking for alternative markers than humansensory-based evaluation. Jónsdóttir et al. have extensively ana-lyzed the volatiles involved in the smoked salmon odor evolution[20]. These authors proved that selected key volatiles performedbetter as predictors to explain variation in sensory attributes thantraditional chemical and microbial variables. Sea bass freshness hasbeen studied with electronic noses and chemical markers in foodscience journals. These studies employed chemical sensor arraytechniques to prove that the freshness of the fish can be assured for3–4 days in commercialization at controlled temperatures [21]. Anadditional contribution by Barat et al. monitored sea bream fresh-ness through a potentiometric sensor. The use of the potentiometricsensors showed a strong correlation with the K1 index, which isdependent on the nucloside degradation (used as a good indicatorof postmortem time and freshness) [22]. An array of 6 tin oxidebased Taguchi gas sensors were used by El Barbri et al. to measurethe freshness of Moroccan sardines stored at 4 ◦C [23,24]. A com-parison with dielectric spectroscopy, and gas sensor array on theevaluation of Baltic cod (Gadus morhua) was published by Ken etal. [25]. This study built a Quality Index from a prediction modelbased on a mixed gas sensor (NST 3320, from Applied Sensor, Swe-den, with FE-sensors and MOX-sensors), although the multi-sensorapproach showed large co-linearity.

In this paper, we seek to find a correlation with fish spoilageusing a single metal oxide sensor, temperature modulated, whilereproducing realistic conditions for the application (strong oscil-lations of temperature and humidity) typical from refrigeratorcontrol units. The sensor employed (CMOSSens) is a prototype of ametal oxide gas sensor designed by the Eidgenössische TechnisicheHochschule (ETH-Zurich, Switzerland) and developed under theEuropean project by the same name IST-1999-10579 [26–28]. Itintegrates CMOS circuitry with a metal oxide sensor, capable ofapplying fast temperature modulation profiles to the sensing mate-rial.

We have focused our attention on the freshness assessment ofcultured sea bream (Sparus aurata). The increase on the demandof this product and the presence of important farming industriesin Europe motivates the research on new methods of freshnessassessment for this particular fish species.

Fig. 2. Image version of the CMOSSens sensor.

A. Perera et al. / Sensors and Actua

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the sensibility to spoilage volatiles. This can be corroborated by

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

ish samples were slaughtered by hypothermia and dispatched in aolyurethane box with ice to Universitat de Barcelona. Immediatelyfter reception, fish were cleaned, filleted, stored in six 500 ml Pyrexottles and connected to the measurement system. Bottles with theamples were immediately stored in a fridge and the experimentata acquisition proceeded without delay.

CMOSSens metal oxide sensor offers limited power con-umption, on-board signal acquisition, a digital control for theemperature of the heater and on-board tests. Metal oxide sensingechnology is not compatible with the CMOS technological process.substantial effort has been done within the CMOSSens project in

rder to make both technologies compatible (Fig. 2).A version of the CMOSSens sensor is shown in Fig. 2. The dielec-

ric membrane is about 500 mm by 500 mm in size with a heatedrea of 300 mm by 300 mm. Although different geometries wereesigned and tested by ETHZ, the final version had a strong cir-ular symmetry. The heater is build with the resistive poly-siliconayer. The sensor achieves a maximum temperature of 400 ◦C at a.5 V supply voltage. Additionally, the CMOSSens sensor contains aemperature control of the membrane. Analog and digital controlersions were designed at ETHZ. The digitally controlled versionas the prototype used in this paper.

An electronic nose (ipNose) [29] was adapted to interfaceMOSSens protocol and control the experiment. The system’s soft-are controlled the application of the temperature profiles and the

cquisition of data from the CMOSSens sensor.

Fig. 4. (a) Excitation waveform (solid: temperature set point code, dash

tors B 146 (2010) 477–482 479

The volume of the sensor chamber was 0.35 ml and the flowthrough 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 and connected to the bottles by Teflon 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 4 min air intake from thebottles. The purge of the system was performed with 28 min ofclean air sampling. Cycling all bottles lasted for 224 min (3 h, 44 m).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 2 Hz, this feature vector contains 32 points(Fig. 4).

3. Results

As it has been said above, we aim the use of a CMOSSens singlesensor under temperature modulation instead of a traditional gassensor array. Additionally we aim to test freshness assessments,under low temperature conditions and variable humidity condi-tions. Six fillets of sea bream were held in six glass bottles andstored in the fridge during 17 days at a temperature of 5 ± 2 ◦C.

IpNose was programmed to execute a total number of 1524continuous acquisitions among the length of the experiment. Eachacquisition applied a 4 min sampling of each bottle followed by apurge cycle of 28 min. All six channels (bottle) were analyzed 254times. For each acquisition, the last waveform corresponding to thelast cycle of temperature modulation within the last 4 samplingminutes was taken as a feature vector. The evolution of signals intime is shown in Fig. 5, showing the complete dataset before (left)and after (right) normalization plotting the transients (bottom) orthe dataset as a heat-map (top). The color in Fig. 5c and d points tothe day since the test started. There are two observations to note.First, the increase of the concentration contributes in the commonmode of the waveform. Second, there is a continuous deformationof the waveform pattern suggesting that there is a maximum on

the shape of the eigenvectors of the PCA decomposition, seen inFig. 6. A normalization was applied to force zero mean and unitvariance for every measurements. This normalization aims to focuson the waveform modification instead of a variation in the common

ed: membrane temperature) and (b) typical air sensor response.

480 A. Perera et al. / Sensors and Actuators B 146 (2010) 477–482

als for

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same behavior. Our interpretation is that a certain position in thePCA plane can be attributed to a certain state of degradation duringthe spoilage. However, this state is achieved at different times for

Fig. 5. CMOSSens sign

ode, as the common mode contains variation due to the intensityncrease or drift effect.

The change in the shape of the waveform has a strong chemicaleaning as the metal oxide sensing layer has different sensitivities

o different compounds at different temperatures. The maximumesponse of the sensor is smoothly displaced from the end of theaveform (sample 32, maximum temperature) to a point located

n the temperature ramp section.

The evolution in the PCA plot projection of dataset mean cen-

ered with unit variance is shown in Fig. 7It can be observed that while at the beginning of the process the

ignals are basically dominated by perturbations (probably tem-

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

filleted sample no. 1.

perature and humidity changes within the fridge), from day five thesignals depart clearly from the initial point, indicating the spoilageof the fish. The principal component analysis reveals a non-linearevolution in the gas sensor array overall response. With slight dif-ferences regarding the shape and speed, several bottles show the

different fillets.

Fig. 7. PCA scores of the evolution of filleted fish (normalised). Number indicatesdays since capture.

A. Perera et al. / Sensors and Actuators B 146 (2010) 477–482 481

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ig. 8. Freshness (in days) prediction of the fish (a) model: channel 1 (trainset), vali.

From the present results it is possible to predict the time sinceapture for a particular fish. A PLS model has been built by usingata from the 1st channel. Feature matrix contained the 32 samplesf the waveform as described in previous section. Half dataset (112amples) have been used in building the model and the other halfave been used for validation. A number of 4 latent variables were

ound optimum for describing the evolution. The correspondenceetween the real time and the predicted time in validation is shown

n Fig. 8 . The RMS error in prediction accounts for 1.5 days.However, we should note that there is a saturation of the pre-

icted signals in the first days. This is a clear indication that at thisnitial period we are not able to monitor first changes. If the same

odel is applied to other fish fillets the prediction error increasesp to 3 days. However, as commented previously, this can be dueo real differences in the volatiles emission rather than deficienciesn the model. A representation of the time prediction for the 4thhannel by using the model computed with the 1st data channel ishown in Fig. 8.

. Conclusions

The reviewed literature showed a number of methods for assess-ent of fish freshness. It is shown that, although a variety ofethods exists, there is no accepted objective way for providing

his kind of analysis. GC–MS analysis found in the literature showshat a characteristic volatile pattern is emitted by the fish samplesn the degradation process. These spoilage patterns are due to theTP molecule decomposition change and the generation of sulfideased compounds by bacterial activity.

In this work we have demonstrated that with a single sensor andhe appropriate signal processing it is possible to find a correlationith the spoilage process of the fish samples.

This sensor is exposed to the same experiments with filleted fishnd was able to predict the storage day with an error of 1.5 days.owever, this model was unable to distinguish the odor evolutionuring the first 3 days. Further work should consider other speciesnd check the validity of the models with samples coming not fromsh farms but from open sea. This may add increased difficulty, asny change in the diet of the animal will be reflected in differencesn the microbiological activity triggered after slaughtering.

On the other side the sensing layer used in the CMOSSens sen-or was not optimized for sensing any of the volatiles known to bemitted by the decomposition process. An optimization of the sens-ng layer would yield to an improved resolution allowing finding aetter correlation within the first days of the process.

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: channel 1 (validation set) and (b) model: channel 1 (trainset), validation: channel

Acknowledgments

The authors wish to acknowledge Sr. Guillermo Bores (CRIPES.A.) for their kind collaboration providing the fresh samples. Thiswork has been performed under funding by the IST-1999-10579,TIC1998-0978-C03-03 and DPI-2001-3213-C02-0. This work wassupported by the Spanish Ministerio de Educación y Ciencia underthe Ramón y 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 identification 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 Electrónica Bilotti Enterprise—AllegroMicrosystems Inc. (Worcester, MA, USA). He finished Ph.D. studies at 2004 in Eid-genössische 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 in 1996 from the University of Tübingen, Germany. After workingas 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 his

Ph.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.