06JSIdentification of Typical Wine Aromas by Means of an Electronic Nose

Identification of Typical Wine Aromas by Means of an Electronic NoseJesús Lozano,JoséPedro Santos,Manuel Aleixandre,Isabel Sayago,Javier Gutiérrez,and Maria Carmen HorrilloAbstract—In the field of electronic noses (e-noses),it is not very usual to find many applications to wine detection.Most of them are related to the discrimination of wines in order to prevent their illegal adulteration and detection of off-odors,but their objective is not the identification of wine aromas.In this paper,an appli-cation of an e-nose for the identification of typical aromatic com-pounds present in white and red wines is shown.The descriptors of these compounds are fruity,floral,herbaceous,vegetative,spicy,smoky,and microbiological,and they are responsible for the usual aromas in wines;concentrations differ from2–8the threshold concentration humans can smell.Some of the measured aromas are pear,apple,peach,coconut,rose,geranium,cut green grass,mint,vanilla,clove,almond,toast,wood,and butter.Principal compo-nent analysis and linear discriminant analysis show that datasets of these groups of compounds are clearly separated,and a com-parison among several types of artificial neural networks has been also performed.The results confirm that the system has good per-formance in the classification of typical red and white wine aromas.Index Terms—Aromatic compounds,pattern recognition tech-niques,thin film gas sensors,wine.I.I NTRODUCTIONTHE detection of aroma and the quality control of food-stuffs and beverages can be assessed by different analyt-ical methods for the identification of the organoleptic properties of the products.In fact,the classical methods of chemical anal-ysis,such as gas and liquid chromatography,mass spectrometry,nuclear magnetic resonance,and spectrophotometry,are highly reliable and suitable for these purposes,but these analytical techniques are of high cost,long processability,and low in situ and online measurableness.Although the human nose is very complex,several attempts to develop new electronic instrumen-tation capable of mimicking its remarkable ability have been performed successfully.In this way,several electronic noses (e-noses)have been used to assess the smell of various indus-trial products,such as ham [1],fish [2],olive oil [3],cheese [4],milk [5],fruit [6],beer [7],vinegar [8],coffee [9],etc.A design of a multisensor array combined with pattern recognition tech-niques has been proposed to identify and classify the chemical compounds or aromas added to the wine samples.Manuscript received November 26,2004;revised February 10,2005.This work was supported by the Spanish Science and Technology Ministry under the project TIC2002-04588-C02-01.The associate editor coordinating the review of this paper and approving it for publication was Prof.Krishna Persaud.The authors are with the Laboratorio de Sensores,Instituto de Física Aplicada,Consejo Superior de Investigaciones Científicas (IFA-CSIC),28006Madrid,Spain (e-mail:jesloz@ifa.cetef.csic.es;josepe@ifa.cetef.csic.es;manuel@ifa.cetef.csic.es;sayago@ifa.cetef.csic.es;javiergutierrez@ifa.cetef.csic.es;carmenhorrillo@ifa.cetef.csic.es).Digital Object Identifier 10.1109/JSEN.2005.854598TABLE IC HEMICAL C OMPOUNDS AND A ROMAS A DDED TO THE W HITE WINETABLE IIC HEMICAL C OMPOUNDS AND A ROMAS A DDED TO THE R ED WINEThe multivariate response of the sensors with broad and par-tially overlapping selectivities can be utilized as an “electronic fingerprint”to characterize a wide range of odors or volatile compounds by means of pattern-recognition techniques.During the last few years,e-noses have been developed as an alternative method to perform this aromatic analysis [10]–[12].II.E XPERIMENTALA.Wine SamplesA total of 16aromas have been analyzed:eight in white wine and eight in red wine.The measured aromas are the most common ones in white and red wines.The chemical compounds responsible of these aromas are dissolved in the same wine at concentrations from2–8the threshold concentration that humans can smell [13],[14].In Tables I and II,the chemical compounds and aromas added to white and red wines measured with the e-nose are shown.The white base wine comes from Malvar grapes and the red base wine comes from Tempranillo variety.Both white and red wines have been elaborated in the Instituto Madrileño de Investigaciones Agroalimentarias (IMIA),Madrid,Spain,with1530-437X/$20.00©2005IEEEFig.1.Measurement setup.1:Nitrogen bottle.2:Massflowmeter controller.3:Electrovalves.4:Dreschell bottle with sample in a thermostatic bath.5:Sensors cell.6:PC.7:DMM with multiplexer.grapes from the Madrid region.All compounds were of analyt-ical quality and were provided by Merck and Sigma-Aldrich.B.Electronic NoseAn e-nose based on a tin oxide–array has been used for mea-suring wine samples using headspace analysis.The e-nose usedhas been home fabricated and home developed for wine aromapurposes[15].The sensor array was prepared by RF sputteringonto alumina substrate.The array was formed by16thinfilmsensors with thicknesses between200and800nm.Some sen-sors were doped with chromium and indium either as surface orintermediate layer.The operating temperature of the sensors iscontrolled at250C with a PID regulator.The array was placedin a stainless-steel cell with a heater and a thermocouple.The main components of the measurement setup are shownin Fig.1.The carrier gas used had99.998%nitrogen purity.Gas line tubes were of stainless steel covered with fused silicain order to minimize gas adsorption in the line.The samplingmethod employed was static headspace followed by a dynamicinjection.Ten milliliters of solution are kept in a50-ml Dreschelbottle at30C for30min.Then,the electrovalves are switchedand nitrogenfluxes for20min carrying the volatile compoundsto the sensor cell.Then,the electrovalves are switched again toallow the sensors to recover the baseline.This procedure is re-peated several times for each compound.Sensors are calibratedonce a week with a blank solution[12%(v/v)ethanol in deion-ized water]in order to reduce the drift of the sensors[16].Mea-surements are carried out at a total gasflow of200ml/min.The resistance of the sensors was measured with a Keithley270071/2digits digital multimeter(DMM)with a40-channelmultiplexer connected to the personal computer through a GPIBinterface.The measurement system was fully automated andcontrolled with a program developed at Testpoint.Responses of the individual sensors are defined relative tothe minimum resistance to12%(v/v)of ethanol for all themeasurementswhere is the minimum resistance of the sensor in the mea-surement of wineand is the minimum resistance ofthe sensor in a solution of12%of ethanol.The data collected were analyzed by means of patternrecognition techniques using a commercial software package(Matlab6.1)for linear methods,such as principal componentanalysis(PCA)and linear discriminant analysis(LDA),and fornonlinear methods based on artificial neural networks(ANNs),such as perceptron,backpropagation,and probabilistic neuralnetworks.There are many possible linear techniques for the analysis ofdata:PCA and LDA are two commonly used linear techniquesfor dimensionality reduction and visualization of datasets.Theprime difference between LDA and PCA is where PCA seeksdirections that are efficient for representation,LDA seeks direc-tions that are efficient for discrimination.In PCA,the shape andlocation of the original datasets change when transformed to adifferent space and LDA tries to provide major class separabilityand draw a decision region between the given classes.PCA is a signal representation technique that applies a lineartransformation to the data and results in a new space of variablescalled principal components.PCA reduces the dimensionalityof feature space by restricting attention to those directions alongwhich the scatter of the cloud datapoints is greatest[17].Usu-ally,thefirst two components carry most of the information ofthe old variables.LDA is a signal-classification technique that directly maxi-mizes class separatibility,generating projections where the ex-amples of each class form compact clusters and the differentclusters are far from each other.These projections are alterna-tively defined by thefirst eigenvectors of thematrix,whereand are the within-class and between-class co-variance matrices,respectively[17].LDA maximizes the ratio of between-class variance to thewithin-class variance in any particular dataset,thereby guaran-teeing maximal separability.The discrimination of the tested wines belonging to thesame class has been tackled with a pattern recognizer based onANN providing nonlinearity in the multivariate classificationperformance.Three types of neural networks have been testedfor the classification of the wine aroma.Perceptron network,LOZANO et al.:IDENTIFICATION OF TYPICAL WINE AROMAS BY MEANS OF AN ELECTRONIC NOSE175Fig.2.Typical time responses of four of the 16sensors to white wine.feedforward fully connected network using the backpropaga-tion learning algorithm,and probabilistic networks based on radial basis transfer functions have been used to determine the best classi fication.The perceptron network consists of a single layer of nine per-ceptron neurons connected to the inputs through a set of weights.The perceptron learning rule is capable of training only a single layer.This restriction imposes limitations on the computation a perceptron can perform.The weights of the neurons can be adapted on an iteration-by-iteration basis.For the adaptation,we used an error-correction rule known as the perceptron con-vergence algorithm [18].The backpropagation network architecture is formed by three layers:the input layer has 16neurons corresponding to the 16sensors,a variable number in the hidden layer,and nine neurons in the output layer,the same number of existing classes.A probabilistic neural network PNN composed of three layers,with radial basis transfer functions in the hidden layer and a competitive one in the output [16],was used for classi fi-cation purposes.Leave-one-out (LOO)cross validation was applied to check the performance of the network [19].LOO consists oftraining distinct nets (in thiscase,is number of measurements)byusing training vectors,while the validation of the trained network is carried out by using the remaining vector,excluded from the training set.This procedure isrepeated times until all vectors are validated [20].III.R ESULTS AND D ISCUSSIONFig.2shows the typical transient responses of four chemor-resistive sensors,operating at 250C exposed to an aroma of the white wine.The array was cyclically exposed to 20-min pulses of the tested wine flavor followed by 40-min nitrogen purges.The measurements were repeated at least eight times for each tested wine sample.As it can be noticed,the electrical resistance (sensor signal)of each sensor downshifts upon exposure of the examined wine samples and returns to baseline level when ni-trogen is switched again into sensor test cells to recover them.The sensor responses are fast and reproducible with acceptablenoise.Fig.3.Polar plot of multisensor response to typical white winearomas.Fig.4.Polar plot of multisensor response to typical red wine.A feature extraction is performed on the data stored on the hard disk.The minimum value of the resistance when sensors are exposed to the wine sample is divided by the resistance of the sensors to a 12%sample of ethanol in order to obtain the vectors of each aromatic compound.A polar plot of the average signals of the sensors for eight aromatic compounds in white and red wines is shown in Figs.3and 4,respectively,in terms of relative resistance changes.The contour of these polar plots differs from one sample to another,illustrating the discrimination capabilities of the array.A.Principal Component AnalysisThe PCA for the aromatic compounds in white and red wines is shown in Figs.5and 6,respectively.The percentage of vari-ance explained by each principal component is in brackets.As is shown in Fig.5,all clusters are well separated.The cluster corresponding to white wine is clearly separated among the other aromatic compounds.In the case of red wine,all clusters are well separated.In this case,the base wine is more separated than the case of the white wine,but some of the other clusters are closer.This measuring system permits us to classify the signals in separate clusters and to discriminate one set from the others.176IEEE SENSORS JOURNAL,VOL.6,NO.1,FEBRUARY2006Fig.5.PCA plot of multisensor response to typical white winearomas.Fig.6.PCA plot of multisensor response to typical red wine aromas.B.Linear Discriminant AnalysisThe LDA plot for the aromatic compounds in white and red wines is shown in Figs.7and 8,respectively.The clusters of datasets are separated.C.Neural Networks AnalysisPCA results are con firmed with a more powerful pattern-recognition method:ANN.To classify the measurements we have used three types of neural networks:perceptron,backprop-agation,and probabilistic networks.1)Perceptron Networks:The results of the classi fication made with the perceptron network are shown in Tables III and IV .The classi fication success for the aromas is 98%for white wine and 90%for red wine.2)Backpropagation Networks:In the training of feed-for-ward networks,several number of neurons in the hiddenlayerFig.7.LDA of multisensor response to typical white winearomas.Fig.8.LDA of multisensor response to typical red wine aromas.have been tested.The optimal number turned out to be 14neu-rons.The results of the classi fication made with the multilayer perceptron network using the backpropagation learning algo-rithm and 14neurons in the hidden layer are shown in Tables III and IV .The classi fication success is 100%for the aromas in white and red wine.3)Probabilistic Networks:Probabilistic neural network analysis shows an overall 100%classi fication success for the aromas in white wine and 98%success for the aromas in red wine;the confusion matrix for those measurements is shown in Tables III and IV .In this application,the use of more complicated and powerful feature extraction methods that consider the dynamic informa-tion of the curve is not necessary because good results in the classi fication have been obtained (almost 100%with different neural networks)by using a simpler feature extraction method,but it could be very useful in far more complicated applications.LOZANO et al.:IDENTIFICATION OF TYPICAL WINE AROMAS BY MEANS OF AN ELECTRONIC NOSE 177TABLE IIIC ONFUSION M ATRIX OF THE P ERCEPTRON (P),B ACKPROPAGATION (BP),AND P ROBABILISTIC (PR)N ETWORKS FOR T YPICAL A ROMATIC C OMPOUNDS IN W HITE WINETABLE IVC ONFUSION M ATRIX OF THE P ERCEPTRON (P),B ACKPROPAGATION (BP),AND P ROBABILISTIC (PR)N ETWORKS FOR T YPICAL A ROMATIC C OMPOUNDS IN R ED WINEIV .C ONCLUSIONAn e-nose has been realized for purposes of recognition of typical aromas in white and red wines.The headspace technique has been used for extracting the aromatic compounds dissolved in wine.The signals recorded from the multisensor array ex-posed to the aroma of the wine samples have been used for dis-criminating the main aroma of the wine through PCA as linear explorative techniques and dimensionality reduction.In addition,a comparison between several neural networks has been performed.A nonlinear pattern-recognition system based on perceptron,backpropagation,and radial-basis neural networks has been trained for the identi fication of aromatic compounds added to wine.The best classi fication was obtained by backpropagation with 100%classi fication success followed by probabilistic neural networks with 99%,although the time of training is much lower than in that of the probabilistic network.The lower success obtained by the perceptron network is due to the limitation of being a one-layer network.In conclusion,this study successfully demonstrates the fea-sibility of an e-nose as an analytical tool for the recognition of aromatic compounds added to white and red wines by using a nonlinear pattern recognition system based on ANNs.A CKNOWLEDGMENTThe authors would like to thank the Instituto Madrile ño de Investigaciones Agroalimentarias for the wine samples.R EFERENCES[1]M.Garc ía,M.C.Horrillo,J.P.Santos,M.Aleixandre,I.Sayago,M.J.Fern ández,L.Ar és,and J.Guti érrez,“Arti ficial olfactory system for the classi fication of Iberian hams,”Sens.Actuators B ,vol.96,pp.621–629,2003.[2]G.Olafsdottir et al.,“Multisensor for fish quality determination,”TrendsFood Sci.Technol.,vol.15,pp.86–93,2004.[3] A.Taurino,S.Capone,C.Distante,M.Epifani,R.Rella,and P.Sicil-iano,“Recognition of olive oils by means of an integrated sol-gel SnO electronic nose,”Thin Solid Films ,vol.418,pp.59–65,2002.[4]J.Bargon et al.,“Determination of the ripening state of emmentalcheese via quartz microbalances,”Sens.Actuators B 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York:Marcel-Dekker,1991,p.483.[15]J.P.Santos,J.Lozano,M.Aleixandre,I.Sayago,M.J.Fern ández,L.Ar és,J.Guti érrez,and M.C.Horrillo,“Discrimination of different aro-matic compounds in water,ethanol and wine with a thin film sensor array,”Sens.Actuators B ,vol.103,pp.98–103,2004.[16]R.Gutierrez-Osuna,“Pattern analysis for machine olfaction:a review,”IEEE Sensors J.,vol.2,no.3,pp.189–202,Jun.2002.[17]R.O.Duda,P.E.Hart,and D.G.Stork,Pattern Classification .NewYork:Wiley,2001,pp.115–120.[18]S.Haykin,Neural Networks:A Comprehensive Foundation .UpperSaddle River,NJ:Prentice-Hall,1999,pp.135–143.[19] B.G.M.Vandeginste,D.L.Massart,L.M.C.Buydens,S.de Jong,P.J.Lewi,and J.Smeyers-Verbeke,Handbook of Chemometrics and Qualimetrics:Part B .New York:Elsevier,1998,pp.238–239.[20] C.M.Bishop,Neural Networks for Pattern Recognition .Oxford,U.K.:Oxford Univ.Press,1999.Jes ús Lozano received the B.Sc.degree in electronic engineering from the Universidad Complutense de Madrid,Madrid,Spain,in 2001.He is currently pursuing the Ph.D.degree in e-noses for analysis of wines at the Laboratorio de Sensores,Consejo Superior de Investigaciones Cient íficas (CSIC),Madrid.His research interests include arti ficial olfactory systems,pattern recognition techniques,aroma extraction techniques applied to e-noses,instru-mentation and measurement systems,and chemicalsensors.Jos éPedro Santos received the B.Sc.and Ph.D.de-grees in physics from the Universidad Complutense de Madrid,Madrid,Spain,in 1987and 1995,respec-tively.He was with the University of Milan,Milan,Italy,at the Institute of Advanced Materials of the European Commission ’s Joint Research Centre,Ispra,Italy,and with the Electronics Department of the Universidad Complutense de Madrid.Currently,he is with the Instituto de Fisica Aplicada of the Consejo Superior de Investigaciones Cienti ficas(IFA-CSIC),Madrid,where he works on several projects related to the devel-opment of sensors for volatile compound and pollutantdetection.Manuel Aleixandre received the B.Sc.degree in physics from the Universidad Autonoma of Madrid,Madrid,Spain,in 1999.He is currently pursuing the Ph.D.degree at the Instituto de Fisica Aplicada of the Consejo Superior de Investigaciones Cienti ficas (IFA-CSIC),Madrid.His research interests include neural networks,sta-tistics,multivariate data analysis,and characteriza-tion of surfaces with AFM.At present,his main field of research is the development of fiber-opticsensors.Isabel Sayago received the Ph.D.degree from the Universidad Complutense de Madrid,Madrid,Spain,in 1993.Currently,she works on chemical sensors at the Laboratorio de Sensores,Instituto de Fisica Aplicada of the Consejo Superior de Investigaciones Cienti ficas (IFA-CSIC),Madrid.She has worked on microsensors at the CNR-LAMEL Institute,Bologna,Italy.Her main research interests are in the preparation and characterization of gas sensors (chemoresistive,SAW,andcantilevers).Javier Guti érrez received the Ph.D.degree in physics from the Universidad Complutense de Madrid,Madrid,Spain,in 1977.At present,he is the Director of the Instituto de Fisica Aplicada of the Consejo Superior de Inves-tigaciones Cienti ficas (IFA-CSIC),Madrid.His re-search interest is in chemical sensors,including semi-conductor sensors,gravimetric sensors and optoelec-tronic devices applied to arti ficial olfactometrics for environmental monitoring,consumer protection,and the safety ofaliments.Maria Carmen Horrillo received the Ph.D.degree in chemistry from the Universidad Complutense de Madrid,Madrid,Spain,in 1992.From 1993to 1995,she was with the Institute for Advanced Materials of the European Commission ’s Joint Research Centre,Ispra,Italy.Since then,she has been with the Instituto de Fisica Aplicada of the Consejo Superior de Investigaciones Cienti ficas (IFA-CSIC),Madrid,where she works on I +D of chemical microsensors and e-noses for environ-mental protection and the quality control of foods.Since 1999,she has been the Head of the Department of Tecnolog ía de Gases y Super ficies.。