Chapter 10

Electronic tongues:new analytical perspectivefor chemical sensors

Andrey Legin,* Alisa Rudnitskaya and Yuri Vlasov

10.1 GENERAL APPROACH TO THE APPLICATION OF SENSORARRAYS

10.1.1 Why using sensor systems?

Traditional development of analytical instruments and methods wasalways aimed at obtaining the highest possible selectivity to an analyte.In the field of chemical sensors for solution analysis these effortsresulted in the development of multiple electrochemical sensors and, inparticular, potentiometric sensors, widely known now as ion-selectiveelectrodes (ISEs). Most of currently known and advanced electronictongues genetically originate from ISEs [1,2]. Therefore, historicaldescription of development from discrete sensors to the electronictongue system will be limited here by to the field of ISEs, though manyconsiderations may be expanded for the other types of chemical sensors.This was confirmed later when electronic tongues based on differentkind of chemical sensors, e.g. voltammetric, have been suggested [3].

*Corresponding author. Tel.: þ7-812-3289595; fax: þ7-812-3282835. E-mail address:andrew@sensor.chem.lgu.spb.su.

Comprehensive Analytical Chemistry XXXIX, pages 437–486

q 2003 Elsevier Science B.V. All rights reserved

ISSN: 0166-526X

437

The first ion-selective electrode with oxide glass sensitive membranefor determination of hydrogen activity in aqueous solutions wassuggested already in 1907 by Haber and Klemensiewicz [4]. Sincethen a significant number of ISEs’ membrane materials both inorganicand organic were implemented. The main types of ISEs are oxideglasses for Hþ, alkali- and alkali-earth cation determination; crystallinematerials for determination of halogenides and heavy metals; liquid orplasticized polymer compositions with ion-exchangers or neutralcarriers; chalcogenide glasses for heavy metal determination andmembranes with immobilized enzymes for detection of some organicsubstances, etc. Some important features of ion-selective electrodes areas follows: the concentration (activity) of ionic forms but not the totalcontent is being measured; an ISE response is linear; the directpotentiometric measurements are often possible. Theoretically, depen-dence between an ISE output (electrical potential) and logarithm ofactivity of the primary ion in an analyte is linear and is described by theNernst equation, which is commonly used to build calibration curve:

E ¼ E0 þRT

ziFln ai ð10:1Þ

where E is the potential difference (e.m.f.) of the electrochemical cellcomprising an ion-selective and reference electrodes; E0 is the standardpotential; R is the gas constant; T is the absolute temperature; F isFaraday constant; zi the electrical charge of primary ion and ai is theactivity of primary ion. The term RT=ziF is known as response slope Sthat is the sensitivity of an ISE.

An extensive description of existing ion-selective electrodes can befound in books and reviews [5–10]. Such features as the ease ofhandling, short analysis time, obvious possibility of automation, anoption of miniaturization and low self-cost make the ISEs an attractivetool for the analysis of solutions. In practice, this is particularly true forthe media, where the content of interfering species is low enough andelectrode response obey the Nernst equation. However, the lack ofselectivity limits significantly practical application of many ISEs in thepresence of the other species in solutions, besides primary ions, and thisis a common case in the real-world sample analysis, which is the mostimportant field. Wide number of new organic substances for ISEs withPVC plasticized membranes is produced each year, but no significantimprovements of selectivity and sensitivity were achieved during lasttwo decades. Thus, in spite of hectic efforts of numerous researchers,

438

pH glass electrode still remains the most selective ISE as well as the mostused one. Research in the fields of the other types of chemical sensors hasfollowed the same path. In spite of attempts to develop sensors withhighest possible selectivity, the most sensors display insufficientselectivity in multicomponent media, which results in certain stagnationin the field. The possible way to overcome this stagnation is theapplication of sensor systems instead of discrete sensors.

10.1.2 Inspirations from chemometrics and biology

A new emerging idea is easier to consider in relation to othercontributing areas and inputs. Two fields of knowledge mainlyinfluenced the development of the electronic tongues. New achieve-ments in biology such as better understanding of human sensorysystems and also new possibilities and approaches to the data handlingand processing offered by chemometrics helped generating novel ideasin the familiar field of chemical sensors. Before describing electronictongues themselves it seems useful to make a brief overview of thisbackground knowledge.

ChemometricsApplication of ISEs and other chemical sensors in multicomponentmedia is often hindered by their insufficient selectivity. Influence ofinterfering species on ISE response can be taken into account using thetheory, e.g. Nikolski–Eisenman equation:

E ¼ E0 þRT

ziFln ai þ

Xj

KijðajÞzi=zj

0@

1A ð10:2Þ

where Kij is the selectivity coefficient of the ISE to the primary ion i inthe presence of an interfering ion j and zi and zj are the charges of theprimary and interfering ions, respectively.

In case when an ISE is not highly selective the value of its output(potential) will be determined by the simultaneous presence and theratio of content of several ions or other species. The terms ai andP

j KijðajÞzi=zj in Eq. (10.2) may appear comparable and the electrode

response becomes non-linear. Though it is still possible to deal with anon-linear calibration curve, more then one electrode is needed to findcorrectly the parameters of Eq. (10.2) for multiple analytes. Evidently,

439

the number of electrodes should not be less then the number of analytesaccording to simple mathematical considerations. Different theories ofISEs and other sensors were suggested and developed but they all arebased on certain sensing mechanisms and certain assumptions thatmake them too simplistic for real multicomponent analyte. Ultimately,this restricts the usefulness of chemical sensors as analyticalinstruments.

In the mid-1980s, similar reasoning led to the idea of applying anelectrode array instead of a discrete ISE with the aim to improve theinsufficient selectivity of the ISEs in the presence of interfering ions.This approach assumes that the behaviour of each electrode of thearray in multicomponent solutions can still be described byNikolsky–Eisenman equation. Thus, the system of such equationsshould be solved to find ISEs’ parameter such as standard potentials,selectivity coefficients and/or slopes. The parameters thus found canbe used afterwards for the prediction of concentration of multipleanalytes. Numerous methods can be applied for calculating para-meters of Eq. (10.2) including different regression techniques andeven artificial neural networks (ANNs).

The first work dealing with the application of ISE arrays formulticomponent analysis was the one by Otto and Thomas in 1985[9]. A sensor array comprising eight sensors was used forsimultaneous determination of sodium, potassium, calcium andmagnesium at the concentration level typical for biological liquids.The main problem in this case is an insufficient selectivity of Mg-and Na-selective electrodes in the presence of calcium andpotassium, respectively. Multiple linear and partial least squareregressions were used for fitting two parameters of Nikolskyequation—the standard potential and the selectivity coefficient, theslope values being preliminary determined in the individualsolutions of analytes. The array gave a certain advantage incomparison with discrete ISEs. The best results were obtainedusing PLS regression.

Beebe and Kowalski et al. [12,13] used a sensor array comprising fivesparingly selective electrodes for the determination of Na and K inbinary solutions. A non-linear regression based on simplex algorithmand multiple linear regressions were used to determine the parametersof Nikolsky equation including the slope values. The calibration of asensor array in Ref. [12] was performed using a projection pursuitregression—a non-parametric multivariate technique, which requires

440

no a-priori information about the functional form of sensor response.The best results were obtained with non-linear regression, the errorsbeing 0.4% for Na and 5.3% for K.

A combination of three highly selective electrodes (sodium, potassiumand calcium) and one sparingly selective sensor with multipleionophores was employed for determination of sodium, potassium andcalcium in tertiary mixtures [14]. It was found that incorporation of thefourth sensor (a sparingly selective one) into the array of ISEs improvedthe error of determination down to 2.8% compared to 4.5% with onlythree highly selective sensors. The same sensor array was applied in theflow-injection set-up for Na, K and Ca determination in mineral waterand human blood plasma samples [15,16].

The next step from Nikolsky equation and theoretical description ofelectrode responses was the application of ANNs for sensor responseprocessing [17–19]. ANN is non-parametric technique that can learnrelationships in the data from the set of examples, though ANN worksas a ‘black box’ and produces hardly interpretable mathematicalmodels. The feed-forward neural network was trained to detect andidentify ions in the samples containing sodium, potassium and calciumat different concentration levels. Sensor array included the same threeion-selective electrodes as described in Ref. [16] implemented in flow-injection mode. Network parameters and training algorithm werevaried to investigate their influence on the neural net performance.Electrode response models were distorted in different ways includingnoise addition and baseline shift with the aim to investigate ability ofthe network to recognize previously unseen samples.

Bos and co-workers [18,19] used back-propagation neural net toobtain calibration model for sensor array, recurrent neural net wasapplied for the determination of Nikolsky equation parameters. Ion-selective sensor arrays were employed for the determination of calciumand copper and of potassium, calcium, nitrate and chloride simul-taneously. Calcium- and copper-selective electrodes together with pHelectrode were used in the former case. The average error of about 8% ofCa2þ and Cu2þ content determination was reported. The second arrayincluded corresponding ion-selective electrodes and also a pH electrode.An average error was found to be 6%, while the maximum one wasabout 20%.

These works illustrate the first attempts of using an array ofpotentiometric sensors instead of discrete sensors for quantitative iondetermination. The starting point was an idea to use the Nikolsky

441

equation instead of the Nernst one to take into account interferencesfrom other ions. This resulted in applying sparingly selective electrodeswith poorer selectivity than traditional ones but more useful in thearrays than selective electrodes. This also led to increasing interest tomultivariate and non-parametric data processing techniques that donot require information about the functional form of sensor signal/ana-lyte activity dependence.

Biological inspirationAnother approach that encouraged the development of chemicalmultisensor systems was an attempt to mimic organization andperformance of biological sensory systems, particularly the olfaction ofmammalians (the sense of smell) [20]. These principles were partlyrealized in the systems for gas analysis—‘electronic noses’ and later inliquid analysers—‘electronic tongues’. The olfaction was earlier recog-nized as the most effective sensing system owing to its high sensitivityand discrimination ability. The sense of smell is capable of distinguish-ing thousands of different volatile molecules including some verysimilar ones, such as stereoisomers. Human perception threshold forsome of the odorants can be as low as a few parts per trillion and is evenlower in animals [21]. Evidently, all odorant substances are volatile butvolatility (vapour pressure) and odour intensity are not proportionaland some compounds with very low vapour pressure can be powerfulodorants (e.g. musk) and vice versa. The relationship betweencompound structure and odour that it elicits is still unclear andcompounds of very different chemical compositions may have similarodours. However, a relation between fat solubility and odour intensitywas postulated: the strongest odorants are both water and fat-soluble[22]. An impressive performance of olfactory system is achieved due to awide set of non-specific or cross-sensitive receptors and processing oftheir signals in the neural system and in the brain. The detection ofodour is performed by olfactory receptor neurons placed in mucus layerin nostrils, where odorant molecules react with odorant bindingproteins—‘sensing layer’ of receptors. Since receptors are not selective,many of them respond to a given odour. This reaction results in anactivity pattern, which is transferred to olfactory bulb, where primarysignal processing is performed, and then to the higher level brain regionfor identification and recognition.

442

The sense of taste in mammalians is organized similarly to olfaction.The taste is perceived by non-specific taste buds, placed on the papillaeof the tongue. Conventionally, the overall taste is believed beingcorrelated to a combination of four basic tastes: sweetness, sourness,bitterness and saltiness. Sometimes another elementary taste charac-teristics are used such as umami. Umami was firstly introduced byJapanese researchers and it is described as delicious taste perceived inmeat, cheeses and mushrooms [23]. Since taste and odour are oftenperceived simultaneously the term ‘flavour’ is widely used for describ-ing their combination, especially speaking about food.

The relationship between taste (flavour) and chemical composition isoften not known precisely, particularly for complex media, likefoodstuffs. Another interesting and highly controversial issue is theinteraction between different tastes [22]. In most cases desensitisingeffect or threshold increase takes place when two substances elicitingdifferent tastes are present simultaneously. One more curious effect isthe decrease of sensitivity threshold when substances present at non-perceptible concentration can still be made felt if contrasting tastesubstance is applied to the tongue. Perception thresholds of humantongue to many taste substances are much higher than for olfactionwith exception of alkaloids, such as quinine. However, differential tasteand odour thresholds are comparable [22]. Thus, the function ofmammalian sense of taste is similar to olfaction but is less developed,possibly because it is less related to survival of living beings.

Spectacular capabilities of biological sensory systems inspiredscientists to implement their organization principles in artificialsensory devices. The latter were firstly intended for gas analysis andodour recognition. According to Ref. [20] the first attempt to develop anodour detection system dates back to the early 1960s [24]. The history ofintelligent multisensor systems for gas analysis starts in 1982 from thework of Persaud and Dodd [25]. Since then a lot of different groups triedto add to the development and application of such devices, which werenamed ‘electronic noses’ [20,26]. ‘Noses’ usually provide for qualitativerecognition of gas mixtures and/or identification of certain individualgases, e.g. leakages of chemicals.

The same principles were applied to the development of multisensorsystems for liquid analysis—‘electronic tongues’. Though an electro-nic tongue works in liquid media like biological one, the sensitivityand detection threshold of artificial ‘tongues’ could be much better,which makes their performance more similar to olfactory system.

443

Furthermore, many, if not all, volatiles are originating from eitherliquid or solid media, e.g. in foodstuffs. In fact, the electronic tongue canbe thought of as an analogy to both olfaction and taste sense and it canbe used for detection of all types of dissolved compounds, includingvolatile ones that form odours after evaporation.

A common feature of all electronic nose and electronic tongue systemsis the combination of an array of non-specific sensors together with dataprocessing by pattern recognition methods. Unlike the electronic nosesystems, however, the electronic tongues are much wider employed notonly for recognition and classification but also for quantitativedetermination of concentrations of multiple components. Multivariateanalysis data processing methods are useful for these purposes andwider appliet for electronic tongues.

Summarising, we can define the electronic tongue as an analyticalinstrument comprising an array of non-selective chemical sensors withpartial specificity to different components in liquids and an appropriatepattern recognition or multivariate calibration tool, capable to recog-nize quantitative and qualitative composition of simple and complexsolutions.

10.1.3 Advantages of sensor systems in comparisonwith discrete sensors

Already the first works dedicated to application of sensor arraysdemonstrated that the use of chemometrics to process responses sensorsallow compensating significantly lack of selectivity and performingquantitative determination of different ions in multicomponent solu-tions. The experimental evidence was found that partially selective orcross-sensitive sensors are preferable compared to highly selective oneswhen used in the sensor array [14]. The sensor array that includedpartially selective electrodes produced lower determination errors thanthe sensor array made solely from highly selective sensors.

Besides overcoming insufficient selectivity, application of cross-sensitive sensor arrays permitted obtaining lower detection limit incomparison with the same sensors used traditionally as discrete sensors[27].

Another interesting feature of the sensor arrays is the possibility tomake potentiometric measurements without reference electrode.Potential differences in this case are measured between all sensor

444

pairs in the array [28]. The resulting potential differences wereconsidered during the data processing and redundant values wererejected. Two PCA score plots displaying the electronic tongue datameasured with and without reference electrode are shown in Fig. 10.1aand b. Though the absolute positions of the points are different on thesetwo plots, the instrument can still easily distinguish the differencebetween classes. Therefore, discrimination abilities of the electronic

Fig. 10.1. Discrimination of Italian mineral waters using potentiometric electronictongue: (a) measurements made with reference electrode; and (b) measurements

made without reference electrode.

445

tongue do not depend on the data used (measurements with or withoutreference electrode), at least for selected applications.

The most exciting possibility that is provided by the electronic tonguesystems is performing classification and recognition of complex liquidsin the same way as humans do. This is very untypical type of analysisfor conventional analytical chemistry not only for ion-selective electro-des but also for any other traditional analytical techniques. On theother hand this is a new and promising analytical approach, which cancharacterize one more important feature of the material world—theflavour of different analytes. Selected examples of different analyticalapplications of the electronic tongue will be discussed later.

10.1.4 Specific features of the sensors for the electronic tongue

The most important part of an electronic tongue is obviously a sensorarray. There is experimental evidence that cross-sensitive, non-specificor sparingly selective sensors are the most useful in the multisensorsystems than selective ones [11–19]. Reproducibility of sensor responseis, of course, crucial in all cases, both selective and non-specific, whenserious analytical approach and results are going to be considered.Thus, the sensors to be used in the array should display goodreproducibility and as high as possible cross-sensitivity, which isunderstood as sensitivity to many components of the analyte simul-taneously. The term cross-sensitivity is commonly used for describingsensor properties in the literature devoted to multisensor systems.However, neither theoretical description of this property nor even itsunambiguous and widely accepted definition is available now. Theterms closely related to cross-sensitivity are also numerous (non-selectivity, cross-reactivity, poorer selectivity, partial specificity, globalselectivity, etc.). Often these terms are used and understood inter-changeably even if some of them are claimed to describe specificfeatures. The ideas standing behind ‘cross-reactivity’ or ‘globalselectivity’ or whatever are very similar and very close indeed. Belowwe will stick to the term cross-sensitivity since it seems the bestapproximation to the phenomenon itself.

Cross-sensitivity of potentiometric sensors cannot be treated simplyas a reverse value of the selectivity coefficient. The ‘classical’ selectivity

446

of ion-selective electrodes was always considered in the framework ofthermodynamic approach, on the basis of certain sensing mechanisms(ion exchange) and for the situation when one primary and oneinterfering ion are present. Recently, the equations describing generalsensor mixed response were suggested [29]. This approach takes intoaccount the response of a polymer based potentiometric sensor to anynumber of ions of different charges. It is based on phase boundarypotential model and assumes that sensitivity mechanism is still ionexchange. Sensing mechanism, however, may be different or evenvariable, for example, some sensors would respond both to ionic and tonon-ionic species in solutions. An adequate theoretical considerations ofcross-sensitivity seems to be not possible at the current stage and muchmore experimental evidence and theoretical considerations of sensingmechanism of different materials to different substances, including non-ionic ones are needed. For the sensors other than potentiometrictheoretical considerations of cross-sensitivity are even less developedand should be one of the aims of the responsible researchers in thefuture. However, an empirical method of sensor cross-sensitivityassessment of potentiometric sensors, which can be used to guidesensor choices in practical applications, was suggested [30,31].

The first necessary step is the determination of a set ofsubstances, to which cross-sensitivity is to be studied, and a set ofsensing materials. In most cases even ‘very’ non-selective or cross-sensitive sensors would not respond to any ion or substance insolution, but presumably to a certain group of substances. Calcu-lation of cross-sensitivity parameters was based on the sensitivitystudy of chalcogenide glass electrodes to a set of heavy metals. Laterthe same parameters were successfully applied to cross-sensitivityevaluation of other types of membrane materials on different set ofanalytes [32]. The experimental measurements used for cross-sensitivity estimation were simply calibrations of given sensors inindividual solutions of the chosen set of compounds.

After application of different fitting procedures and consideration ofliterature data the following three parameters were chosen fordescription of integral sensor response and cross-sensitivity. As far asparameters involved are empirical ones, it is possible to suggest anotherversion or set of them. However, these ones appeared to be representa-tive and fruitful enough.

447

The first one is the average sensor response slope S; measured insolutions of the chosen set of substances:

S ¼1

n

Xi

Si

where Si is sensor response slope in solutions of each individualsubstance, and n is the number of components in the set.

The second value is the averaged signal-to-noise ratio of a sensor (forall components of the set):

K ¼1

n

Xi

Ki ¼1

n

Xi

Si

s2i

where Si and si are response slope and its standard deviation insolutions of each substance.

The last parameter is named ‘non-selectivity factor’, because itdescribes the distribution of sensitivity of a sensor to differentcomponents from the chosen set and is calculated as follows:

F ¼S

s2

where S is the average slope (the first parameter) and s is its standarddeviation.

The average slope value is the main and the most importantcharacteristic of integral response and, hence, cross-sensitivity of asensor. The higher the value the better is the overall sensitivity of thesensor to the substances from the set. An optimal range of average slopecould be estimated in each case on the basis of the followingsuppositions. Let us consider, e.g. a study of sensor sensitivity to a setof divalent ions. In this case, the sensor response slopes are likely to fallinto range from 0 to 29 mV/pX according to the Nernst equation.However, super Nernstian response may be observed as well. Therefore,the average slope of a sensor close to 30 mV/pX is commonly related tocomparatively uniform distribution of sensitivity to the chosen set ofdivalent ions. The value of S . 30 mV/pX may be a result ofsignificantly super-Nernstian response to one of the ions. Thus, inthis particular case the range of S from 25 to 30 mV/pX should beconsidered the optimal one. The sensors displaying S . 25 mV/pXdisplay remarkable cross-sensitivity to all ions of the set and can beused for multisensor array analysis.

448

The average slope value is not the only valid response characteristic.At least two other measures appeared to be useful. To characterize thedistribution of sensitivity for different components the parameter F;which is called ‘non-selectivity parameter’, was used. F p 0:1 is typicalfor highly selective sensors with sharp sensitivity to the primary ionand very poor to the other ions. The increase of F up to 0.1 is theevidence of smoother distribution, but sensitivity to some componentscan still be not high. Rather uniform sensitivity distribution to most ofsubstances from the set is typical for sensors with F . 1: Finally, thevalue of F greater than 0.5 characterizes a reasonable distribution ofsensitivity and significant cross-sensitivity to different species incomplex solutions and thus, may be considered as the optimal one forsensors designed for array application.

A stability parameter is also important because in the preliminaryexperiments a correlation between stability in individual ion solutionsand that in complex liquids was found. The K value—the averagesignal-to-noise ratio is a valuable estimate of sensor stability. Thehigher is K ; the more reproducible is the sensor potential and the morestable is electrochemical sensor behaviour both in individual solutionsand mixed liquid media. It was experimentally determined that thevalue of K greater than 2 could be used as the measure of reasonablesensor stability for array applications.

In conclusion it must be noted that the exact optimal values of cross-sensitivity parameters should be determined in each case individually.In particular, the value of average slope can vary significantly. Theparameters for cross-sensitivity estimation were developed and appliedonly for potentiometric chemical sensors. However, since no assump-tions about mechanism or theoretical description of sensor responsewere considered for cross-sensitivity parameters’ assessments, but onlyexperimental response value and its standard deviation, the samemethod could be applied for other types of sensors.

10.2 ELECTRONIC TONGUE SYSTEMS

10.2.1 Sensors

It was mentioned in Chapter 1 that the first works on application ofsensor arrays for multicomponent analysis of liquids were performedusing potentiometric chemical sensors (ion-selective electrodes).

449

However, the multisensor systems based on the same principles couldbe realized with another types of liquid sensors as well. Also the sametype of sensing material, e.g. plasticized PVC with different activesubstances can be used in sensors with different working principles. Inthis chapter main types of sensors and sensing materials, which wereimplemented in the electronic tongue systems, will be discussed.

Potentiometric sensors still remain the far most widely used ones inthe electronic tongue systems. The taste sensor, which was introducedin 1990 by Toko et al. [33,34] from Kyushu University, Japan, (see alsoChapter 11) consisted of eight potentiometric sensors with thick filmpolymer membranes based on polyvinyl chloride (PVC). The mem-branes contained dioctyl phenylphosphonate (DOPP) as plasticizer andactive substances called ‘lipids’ by the authors, tetrahydrofuran beingused as a solvent [2,35,36]. Membrane compositions are shown inTable 10.1. These membranes were used for the preparation ofpotentiometric sensors with liquid inner filling. Potential values ofthe sensors were measured vs. conventional Ag/AgCl electrode. Sensorsdisplayed sensitivity to inorganic and organic taste substances such as:NaCl, KCl and KBr (salty), HCl, citric and acetic acids (sour), quinine(bitter), monosodium glutamate (umami), sucrose (sweet) [37,38],catechin, tannic, chlorogenic and gallic acids (astringent) [39,40].

The sensors with the same polymer lipid membranes as sensitivelayer but employing other principles of signal transduction anddetection were developed with the aim to miniaturize the taste sensor.The most often used method of microsensor preparation is deposition ofLangmuir–Blodgett film of sensitive substance. Thin film membranes,

TABLE 10.1

Lipid materials used in the multichannel electrode

Channel Lipid (abbreviation)

1 Decyl alcohol (DA)2 Oleic acid (OA)3 Dioctyl phosphate (DOP)4 DOP/TOMA ¼ 9:15 DOP/TOMA ¼ 5:56 DOP/TOMA ¼ 3:77 Trioctyl methyl ammonium chloride (TOMA)8 Oleyl amine (OAm)

Reprinted from Sensors and Actuators B, 64 (2000) 205–215 (K. Toko, ‘Taste sensor’), withpermission from Elsevier Science.

450

which were prepared by dip-coating or by deposition of Langmuir–Blodgett film, were used for impedance measurements [41,42]. It wasfound that in contrast with electric potential, impedance of membraneschanged significantly in the presence of two umami substancessimultaneously and sucrose. It was also found that impedance of thinfilm sensors increased in the presence of many bitter substances, whichwere both electrolytes and non-electrolytes. MOSFET technology wasused to produce miniaturized version of the taste sensor [43]. FET tastesensor was prepared by pasting membranes of the same compositions asabove on the gate of a FET. Another method used for FET sensitive layerpreparation was a deposition of dihexadecyl phosphate Langmuir–Blodgett film. PVC polymer film was deposited on the MOSFET gateprior to Langmuir–Blodgett layer formation to enhance adhesion of thelatter to the substrate. The FET taste sensor displayed the samesensitivity to taste substances as the conventional one, but potentialreproducibility was lower and lifetime was shorter for miniaturizeddevice. Another types of transducers—light addressable potentiometricsensors (LAPS) and surface photovoltage (SVP) [44] were used withpolymer lipid membranes [45]. In both cases the sensitive layers wereprepared by integrating polymer membranes containing active sub-stances onto a semiconductor surface using Langmuir–Blodgetttechnique.

The electronic tongue based on potentiometric sensors was developedin the Laboratory of Chemical Sensors of St Petersburg University[1,46]. The same authors together with Italian colleagues suggested theterm ‘electronic tongue’ referring to multisensor systems comprisingcross-sensitive (partially selective) sensors and pattern recognitiontools for the data processing.

Awide number of sensing materials of different nature from inorganiccompositions to organic polymers were used for the sensor preparation.Membrane materials included chalcogenide glasses doped with differ-ent metals, PVC based polymers containing various plasticizers andactive substances such as ionophores, neutral carriers, metalloporphi-rines, etc. and crystalline compositions [30–32]. The sensor arrayscomprised from 10 to 30 sensors depending on the application. Differentsensors with both liquid inner filling and solid inner contact weremanufactured and applied.

Originally, the electronic tongue was intended for analysis of verydifferent objects including ground and natural waters, industrialsolutions, medicinal solutions, foodstuffs, etc. Thus, sensitivity of

451

sensors to numerous inorganic and organic substances should bestudied prior to their application, special attention being paid to sensorcross-sensitivity and response reproducibility. The sensitivity of thesensors with chalcogenide glass membrane to heavy metal cations (e.g.copper, lead, cadmium, zinc, iron and uranium at different oxidationstates, etc.) was investigated [47–51]. PVC based sensors displayedsensitivity to both alkali- and alkali-earth and heavy metal cations andsuch inorganic anions as chloride, sulphate, nitrate, nitrite, carbonate,etc. [32,52]. The sensitivity of about 40 different sensors to organic tastesubstances present in many foodstuffs and responsible for differentaspects of food quality and flavour was investigated [53]. The tastesubstances were organic acids, alcohols, ethers, aldehydes, phenols,amines, alkaloids, etc. Also the sensitivity to typical organic ‘basic taste’substances, such as quinine, urea, and glutamate was evaluated.

Microsensors based on thin films of chalcogenide glass were preparedusing a new technology—pulse laser depositions (PLD) [54,55]. Theresulting thin films had the same composition as corresponding bulkglasses. Thin film microsensors showed similar electrochemical proper-ties, such as sensitivity and selectivity, to conventional sensors withbulk membranes. The lifetime of microsensors was found to be abouttwo years. Thus, PLD seems to be promising tool for sensorminiaturization and preparation of microsensor arrays for the elec-tronic tongue.

Different analytical technique—voltammetry was used for theelectronic tongue development by the Swedish scientists from Linkop-ing University [3,56–59]. The same general idea of combining non-specific and partly overlapping sensor signals with pattern recognitiontools was realized in the voltammetric electronic tongue. In this casenon-specific signal patterns are produced by an array of workingelectrodes made from different noble metals. Up to six metal electrodes,which were platinum, gold, iridium, palladium, rhodium and rhenium,were combined together and used for different tasks.

An electronic tongue aiming at mimicking biological sensory systemswas developed in University of Texas [60]. The sensors mimicking tastebuds were prepared from poly(ethylene glycol)–polystyrene (PEG–PS)resin beads that were derivatized with a variety of indicator molecules.The sensors were fluorescein sensitive to pH, o-cresolphthaleincomplexone sensitive to Ca2þ and pH, alizarin complexone sensitiveto Ce3þ, Ca2þ and pH, and finally a boronic ester of resorufin-derivatized galactose sensitive to simple sugars. The sensor responses

452

were measure vs. a reference sensor, which was simply a resin beadwith the terminal amines acetylated. The sensor sensitivity was testedin solutions containing Ca2þ, Ce3þ and their mixture at different pHlevel. The aim of the present work was a proof of concept of a sensorsystem design mimicking biological tongue, which explains somewhatstrange choice and limited number of analytes and the absence ofinformation about device application for analysis of any real-worldobjects.

Four different conducting polymers were used to produce thin filmsensors for artificial taste sensor [61], namely, polyaniline, polypyrrole,sulfonated azobenzene polymer and ruthenium complex mer-[RuCl3(dppb)(4-Mepy)]. Thin films of polymers were deposited by usingLangmuir–Blodgett or self-assembly techniques on integrated metallicelectrodes for spectroscopic impedance measurements. Sensors wereable to discriminate between basic taste substances such as NaCl,hydrochloric acid, sucrose and quinine.

One would expect the development of new sensors and even newclasses of sensing materials for the electronic tongues in the nearfuture.

10.2.2 System designs

Design of multisensor systems is defined by the measuring principle ofsensors used in these systems and usually is quite similar for the sametype of sensors.

The measuring procedure with an electronic tongue based onpotentiometric chemical sensors generally consists of registration ofpotential value of each of the sensors in the array vs. a referenceelectrode, often Ag/AgCl. The measurements are made similarly todiscrete electrodes, using high input impedance voltmeters or pH-meters that should be multichannel in the case of the sensor array. Themeasurements are usually guided by PC and sensor responses arewritten into computer data file. Schematic and photo of such a device isshown in Figs. 10.2 and 10.3. This is a laboratory device and allmanipulation with sensor array and measuring liquid are done by hand.In some devices automatic manipulation of the sensor array andsamples by the robot arm is implemented (Fig. 10.4).

The flow-injection variant of potentiometric electronic tongue wasalso developed [50]. The measuring cells of different sizes and

453

configurations were manufactured and the system could comprise from3 to 11 sensors, which were made in special small bodies for thispurpose. The flow-injection multisensor system allows automatedsampling and calibration using a multichannel actuator. The samplevolume in this case could be as small as 50 mL, usually ranging from 150to 500 mL. The overall performance of flow sensor arrays systems wascomparable.

The measurements with voltammetric electronic tongue are made inconventional three-electrode scheme, which includes Ag/AgCl referenceelectrode, stainless steel auxiliary electrode and an array of noble metal

Fig. 10.2. The schematic of potentiometric electronic tongue.

Fig. 10.3. Laboratory prototype of potentiometric electronic tongue.

454

working electrodes instead of single working electrode in traditionalvoltammetry [3]. The schematic of the voltammetric electronic tonguewith two working electrodes is shown in Fig. 10.5. The responsefrom each of the working electrodes is recorded. Two types ofvoltammetric techniques were investigated: small amplitude pulsevoltammetry (SAPV) and large amplitude pulse voltammetry (LAPV).LAPV was chosen for further analytical application of the electronictongue. Different voltage steps ranging from 15 to 100 mV were used.Thus, a number of data points for each electrode were collected. Themost informative data points were chosen during the data processingand used afterwards for calibration and recognition. A hybrid electronictongue combining voltammetry with six working electrodes, three ion-selective electrodes (for pH, chloride and carbon dioxide), conductivityand temperature sensors was also suggested [57]. In this case, each partof system uses its native measuring principles and fusion of the dataoccurs at the stage of their processing.

Optical detection of sensor responses was used in the electronictongue based on derivatized PEG–PS resin [60]. Design of this

Fig. 10.4. Taste-sensing system SA402, Anritsu. Reprinted from Sensors and Actuators B,64 (2000) 205–215 (K. Toko, ‘Taste sensor’), with permission from Elsevier Science.

455

electronic tongue aimed at mimicking mammalian tongues. Thus,to mimic the cavities, in which taste buds of mammalian tongue reside,the PEG–PS resin beads were positioned within micromachined wellsformed in Si/Si3N4 wafers, thus confining the beads to individuallyaddressable positions on a multicomponent chip. The size of the wellswas chosen so that they hold the beads in swollen and unswollen states.Changes of the light absorption of the beads in different analytes wereaccomplished by using a charge-coupled device that was interfaced withthe sensor array.

Light addressable potentiometric device (LAP) is another type oftransducer used in the electronic tongue systems [44,45]. The surfacepotential change caused by the reactions of thin film membrane withanalyte is detected by scanning a light beam across the sensor surface.Owing to a new differential measuring procedure based on time-sharingtechnique the sensitivity of the sensor system was increased by twoorders of magnitude in comparison with conventional LAP system.

Application of impedance spectroscopy for sensor signal detection wasbased upon the fact that in the low frequency region impedance ofsensors is related to the double layer, which is formed by absorption ofdifferent substances present in analysed solution [42]. AC measure-ments were carried out at fixed frequencies of 100, 120 Hz, 1, 10 and100 kHz.

Fig. 10.5. Schematic of the voltammetric electronic tongue with two workingelectrodes. Reprinted from Analytica Chimica Acta, 357 (1997) 21–31 (F. Winquist,P. Wide and I. Lundstrom, ‘An electronic tongue based on voltammetry’), with permission

from Elsevier Science.

456

A sensing microsystem based on shear horizontal surface acousticwave (SH-SAW) devices on 36-degrees rotated Y-cut X-propagatingLiTaO3 was applied for identification of liquids [62]. The systemconsisted of three SH-SAW devices with three different centrefrequencies of 30, 50 and 100 MHz.

More references and mentions of the electronic tongue devices may befound in the literature. However, the present description is restricted tothe most reliable and well-documented results regarding the electronictongue sensor systems and their analytical applications.

10.2.3 Hybrid systems

In human beings the senses of olfaction and taste always cooperateand influence each other, especially, in food flavour evaluation.Though the sensitivity of electronic and biologic noses and tonguesmay differ significantly in terms of the number of substances, whichcould be determined and in the sensitivity thresholds, the idea ofusing an integrated sensor system comprising both an electronicnose and an electronic tongue sounds quite natural. No wonder thatmany researchers explored the possibility of using such integratedsystems, particularly for the analysis of foodstuffs [58,63,64].

The application of an integrated sensor system gives rise to a problemof the fusion of the data of different origin. Different approaches wereused for electronic nose and tongue data fusion. The simplest approachassumes the merging of two matrixes of normalized data. The resultingmatrix should have the number of rows equal to the number of thesamples and the number of columns equal to the number of the sensorsin the electronic nose þ tongue combined system. The data fusion mayalso be performed at the higher level. This means that the data from thenose and the tongue are analysed separately and the results arecombined together afterwards. A method described in Ref. [63]suggested applying, e.g. PCA to the nose and the tongue data separatelyand then the most significant principal components were consideredtogether. More sophisticated approach was put forward in Ref. [58].A hierarchical system of neural network based classifiers was formed.Some of them were trained with the electronic nose data, other oneswith the electronic tongue data. A gating network that combines

457

outputs of all classifiers produced the final results. Different analyticalapplications of the integrated electronic nose–electronic tonguesystems are discussed below.

10.2.4 Data processing

The other important issue for the multisensor analysis, besides thesensor arrays, is processing of the sensor signals. In a multicomponentenvironment the sensor array produces complex signals (patterns),which, ideally, contain information about different compounds presentin the media and other characteristic features, including integral ones.The sensor signals should be analysed altogether to extract valuableanalytical information. Various methods of multivariate calibration andpattern recognition are available now and can be used for the dataprocessing from the sensor arrays.

In principle an electronic tongue may be applied for two main tasks:(1) for quantitative determination either of the content of components orsome other formalized parameters (e.g. taste assessments) and (2) forclassification (recognition, identification, discrimination) of simple andcomplex analytes and/or their features. The choice of the dataprocessing technique for a particular case depends on task to be solvedand the structure of the data (non-linearity, correlations, etc.). The moredetailed description of the data processing methods used in the sensoranalysis is beyond the scope and format of this chapter and should betreated in a specific publication. Theoretical discussions of availablemethods and some case studies of different sensor applications can befound in numerous books, manuals and papers, e.g. [65–67] andreferences therein.

10.3 SELECTED APPLICATIONS OF THE ELECTRONIC TONGUE

Since many of the results obtained by one of the most active researchgroups in the field (Toko et al.) are described in details in a separatechapter of the present book (Chapter 11), the illustrations of theapplications of the electronic tongues given below will concentratemainly on the results produced by the other researchers.

458

10.3.1 Application areas and analytes

The widest application area of the electronic tongue systems is, surely,the analysis of foodstuffs and particularly that of beverages. Theanalysis of foodstuffs may include various tasks: discriminationbetween different individual sorts, blends or brands; recognition ofproducts of different quality and their classification; quantitativedetermination of the content of multiple compounds, both inorganicand organic, and/or classes of compounds. The most exciting applicationarea of the electronic tongues, however, is taste quantification, which isunderstood as formalized assessment of taste (flavour) characteristics ofa product using an electronic tongue and establishing correlation of theinstrument response to human sensory perception. Selected examplesof analytical applications of electronic tongues will be discussed below.This chapter will be devoted to the short description of the objects,which were analysed using the electronic tongue systems, and to theanalytical tasks, which they attempted to solve.

A wide number of beverages and liquid foods were analysed using theelectronic tongue systems. Mineral waters represent relatively simpleand, hence, an evident object for analysis using the sensor systems.Both classification and quantitative analysis were performed in mineralwater samples. Discrimination and classification of natural and fakeGeorgian and Italian produced mineral waters were performed togetherwith determination of the content of ions such as sodium, potassium,chloride, fluoride, sulphate, bicarbonate and nitrate [68–71]. Theelectronic tongue output also nicely correlates to such parameters asconductivity and dry matter residual, which are related to the total saltcontent in mineral water. Recognition and classification of mineralwater samples according to their hardness was performed using thetaste sensor [72].

The electronic tongue systems were applied to the recognition ofindividual sorts of coffee of different origin and also commercial blendsthat is quite important task in industry [71,73,74]. An attempt was alsoperformed to establish a correlation of the instrument output withhuman perception of coffee flavour, aroma, acidity and body.

Different sorts of black, oolong and green teas with and without addedflavour were measured using electronic tongue systems [75,76]. It wasfound that black and green teas can be reliably discriminated using bothpotentiometric and voltammetric sensor systems, though oolong teasoverlapped with black ones.

459

Fruit juices are quite popular analytes for electronic tongue sensorsystems. Measurements with electronic tongue were made in orange,grape, apple and tomato juices with the aim to distinguish differentsamples, detect spoilage and juice dilution, correlate response of sen-sor system to juice taste and to perform quantitative analysis of juice[3,44,45,74,77–80]. The electronic tongues were capable to discrimi-nate between different sort of orange and tomato juices and fruit juicebased soft drinks [3,44,45,74,77], and juices produced from tomatoes ofdifferent sorts and ripeness grown at experimental orchards [78]. Thepossibility to detect changes in chemical composition of fruit juicesinduced by oxidation/ageing processes in juice after contact with airand spoilage process was demonstrated in many experiments [3,74,77–79]. Detection of fruit juice dilution by water and sucrose syrup,which is a common method of fruit juice adulteration, can be made withhigh accuracy by using the electronic tongue [79]. Quantitativeanalysis was performed in tomato juice samples, chemical analysisform conventional analytical methods being used for calibration.Concentrations of K, Na, Mg, Ca, phosphate, organic acids (malic andcitric) and UMP were determined by the electronic tongue with anaverage precision about 10–15% [78]. Comparison of multisensorsystem response with human taste perception was performed in tomatojuice. Two approaches were realized. The electronic was calibratedusing six taste estimates produced by sensory panel (saltiness,bitterness, sweetness, sourness, sharpness and umami) and thenperformance of sensor system in test samples was evaluated [78].

Alternatively, sensor system was calibrated in canned tomato juice, towhich four taste substances (NaCl, glucose, citric acid and sodiumglutamate) added [80]. The experimental data were processed by PCAand then the measurements in several tomato varieties (withoutadditives) were projected onto the principal axes, which represented akind of ‘taste map’. The resulted taste assessment agreed well withhuman perception.

The electronic tongue was used for discriminating regular and dietsodas and experimental recipes [71]. The main difference between thesesoda samples was the type of sweeteners used, which were natural,artificial and experimental substances not approved for use in foodindustry. Thus, capability of the electronic tongue to distinguishbetween soda samples was related primarily to sensitivity to differenttypes of sweeteners and other additives in sodas.

460

Another widespread application area of the electronic tongues isthe analysis of fresh and fermented milk [3,56,63,81–83]. The mostimportant practical tasks were discrimination of milk undergonedifferent heat treatment, since it affects both milk flavour andnutrition. Recognition of milk samples undergone different heattreatment and correlation of their taste with the system output wasperformed in Refs. [81,82]. Also monitoring of milk souring process[3,82] and bacteria growth was performed by different electronictongue systems [63]. The possibility to apply the electronic tongue todetection of mastitic milk was studied [83]. Automatic detection ofmastitis is an essential part of robotic milking, though currentlyused technology e.g. conductivity probes do not provide necessarysensitivity and discrimination.

Another big group of foodstuffs analysed by using the electronictongue system is alcoholic beverages and spirits. The main tasksincluded recognition of different samples, quality control, quantitativeanalysis and correlation with human taste perception.

The electronic tongues were applied to recognition of wine samples,which were different sorts of red and white wine [64] and wine of thesame denomination (Barbera) and the same vintage but from differentvineyards [68,69,84]. For the electronic tongue calibration results of thestandard chemical analysis and flavour quality assessments of winesamples made by the taste panel were used. Wine parameters such astotal and volatile acidity pH, ethanol, contents of tartaric acid, sulphurdioxide, total polyphenols, glycerol were determined using the elec-tronic tongue. Taste panel produced 14 or 15 different wine flavourestimates depending on wine type, correlation between most of themand the electronic tongue output was established.

The electronic tongue was applied to recognize the samples of spirits,such as cognac, brandy, eau-de-vie and vodka [85]. Electronic tonguereproducibly recognized different samples of cognac from each other,and from any eau-de-vie and also from brandy. Furthermore, it waspossible to distinguish between samples of the same eau-de-vie being incontact with different oak barrels for different periods of time. Inrecognition of vodka it was possible to distinguish between various sortsof spirit differing in ethanol quality, water purification level andpresence of taste additives. It was also possible to distinguish standardspirits from low quality ones.

Thirty-six samples of different brands of beer produced in differentcountries were analysed using sensor system. All samples were

461

distinguished from each other and correlation of sensor system outputwith beer taste characteristics such as richness/lightness and mild/sharp taste was found [2,36].

Discrimination of four kinds of sake from the same brewery wasperformed using the sensor system, ordinary sake being used asreference. An attempt was made to measure the concentration ofethanol and total acidity changes during sake fermentation [86].Taste evaluation of sake was made, model solutions containing mainsake compounds as well as different taste substances being used fortaste sensor calibration. However, no comparison with humanperception was made.

An untypical foodstuff for chemical sensor analysis—vegetable oilwas analysed using the potentiometric electronic tongue [87]. Sincethe oil is not conductive a special procedure of oil extraction by anorganic solvent was elaborated, the resulting extract being used forthe measurements. The aim was to discriminate between oilsproduced from different plants as well as to detect rancidity in oilsand both tasks were solved successfully using the system. Fast andeasy to use method of rancidity determination in oils using theelectronic tongue may be of big interest for oil producers, whereasthe possibility of determining oil origin and raw material used for itsproduction can be important for consumers and controlling agencies.

Though electronic tongues were primarily designed for analysis ofliquids, they could be also applied to measurements in suspensions,purees and other water–solid mixtures or homogenates. A samplepreparation is a necessary step in this case and it can be performedin different ways. The products with high water content such asfruits and some vegetables can be crushed or mashed and themeasurements can be made in the resulting pulp. Other productswith lower water content, for example, flesh food, should be firstlyminced and then mixed with water.

The applicability of the electronic tongue to flesh food analysis wasdemonstrated on the example of fish [71] and pork liver [88]recognition. The measurements were made in the homogenateprepared by stirring chopped fish with distilled water. It wasfound that the system is capable to distinguish between the sampleof fresh water fish and two samples of seawater fish as well as todetect and monitor fish spoilage.

Analysis of pork liver was aimed at discrimination between samplesfrom healthy and the sick animals. Also, it was found that ‘electronic

462

tongue’ could distinguish between the samples of pork liver from theanimals fed with an anti-stress drug before slaughter and controlanimals, which did not get any medication.

Another non-liquid foodstuff—miso, Japanese fermented soybeanpaste, was analysed using the ‘taste sensor’ [2]. Chemical parameters(e.g. titratable acidity) during miso fermentation process weremeasured by conventional analytical methods, while humans estimatedripeness and taste quality. Thus, the electronic tongue was applied tomonitoring of miso fermentation process with the aim to replace a partof routine analysis by simpler measurements using a sensor system.Recognition of different sorts of miso was also performed.

The application of the electronic tongues is the analysis of foodstuffs isalso popular due to the analogy with biological sensory systems.Nevertheless, the sensors used in the electronic tongues displaysensitivity to many ions and many other substances and can be appliedfor their determination in inedible media as well. On the other hand,there is a big concern in society about pollution of the environment byindustrial discharges, which makes the detection of pollution of thewater an important task. Currently, this kind of analysis is usuallyperformed in the laboratory and after sampling and, hence, is ratherslow and expensive. Therefore, a simple, rapid and low-cost method forautomated on-line or at-line evaluation of the content of watercomponents or assessment of water quality would be of high practicalvalue. Sensor systems and the electronic tongue appear to conform wellto these requirements and thus represent a perspective technique forenvironmental monitoring.

The potentiometric electronic tongue was applied to the analysis ofmodelled polluted ground waters, containing Cu, Fe, Mn, Zn, Ca2þ,Mg2þ, Naþ, Cl2, SO4

22 [47–49] and modelled flood waters fromabandoned uranium mines containing Fe(II), Fe(III), U(VI), U(IV)[51]. The errors of determination were from 5 to 30% that is acceptablefor environmental monitoring especially considering low concentrationlevel of components, which was as low as 1027–1026 mol/L for metals.

A flow-injection multisensor system was designed and applied todetermine the heavy metals (Cu, Pb, etc.) and acidic oxides (NOx, SO2)and HCl in the flue gases from incinerators—waste combustion plants[50]. The analysis was performed in solutions that were obtained bybubbling the flue gas through special absorbing liquids. After theabsorption heavy metals were converted in solution into ionic forms,while acidic oxides formed inorganic anions (NO3

2; NO22; SO4

22, Cl2).

463

The sensor array included up to 11 sensors. System calibration wasperformed in model solutions. Afterwards the samples from incinera-tion plants were analysed. The same samples were analysed by ICP-MSand liquid chromatography with the aim to evaluate the multisensorflow-injection system performance and to make the calibration of thesystem using the independent reference data. The system was able todetermine copper, lead and cadmium with an average error of 10–20%and chloride, sulphate, nitrite and nitrate with the average errors of5–15%.

Discrimination of detergents for washing machines and dish washeswas performed by taste sensor and voltammetric electronic tongue [76].Both systems were able to discriminate between different detergents,though to achieve discrimination between detergents for washingmachines and dishwashers merging of data from two devices wasnecessary.

Medicinal analysis is another field, where rapid and low costanalytical methods are needed. Application of the electronic tonguefor quantitative analysis of dialysis solutions for an artificial kidney isdescribed in more details in the next chapter. Also an attempt to use theelectronic tongue as a diagnostic tool was made [63]. In this work theurine samples containing blood due to different diseases and that fromhealthy subject were discriminated by the electronic tongue and nose.

10.3.2 Quantitative analysis

Determination of component concentrations is the most typicalapplication area of ion-selective electrodes as well as other types ofchemical sensors for liquids. Though in the application of the electronictongue systems main attention was paid to classification and recog-nition, quantitative analysis remains practically important. Below thetwo examples of electronic tongue’s application for multicomponentanalysis of liquids are discussed.

Nowadays, ion-selective electrodes are widely used in commercialblood analysers. However, insufficient selectivity in multicomponentmedia hinders further application of chemical sensors in this field.Another problem is the requirement of high accuracy of analysis, whichshould be usually within 1–2%, because concentration of manycomponents in bodily liquids would change only in a very narrowrange (8–10%).

464

The potentiometric electronic tongue was applied to componentcontent determination in solutions, which composition was close tohuman blood plasma [52]. The measurements have been made incomplex solutions containing simultaneously Kþ, Naþ, Ca2þ, Mg2þ,Cl2, HCO3

22, HPO422, H2PO4

2, Hþ in typical for human blood plasmaranges of concentrations. Complex solutions of 45 different compo-sitions have been used for system calibration and testing.

The sensor arrays included two types of potentiometric sensors: (1)conventional ones, for example, chloride-, sodium- and potassium-selective sensors both on the basis of solid-state and plasticized PVCorganic membranes. Also conventional pH glass electrode has beenused to obtain pH values that have been taken as reference onesduring calibration and testing. (2) Specially designed non-specificsensors with enhanced cross-sensitivities on the basis of PVCmembranes chosen using the cross-sensitivity estimation method.Also sensors with PVC membranes sensitive to anions, i.e. hydro-carbonate and phosphate have been included in the array. Totally, 10sensors have been incorporated into the sensor arrays. A certainarray composition evolved during experiments depending on sensorstability and/or cross-sensitivity. The details of sensor preparationcan be found elsewhere [14], and references therein. PVC mem-branes have been dipped into solutions of primary ions betweenmeasurements. To make potential values more reproducible andcomparable all sensors have been treated periodically by a condi-tioner solution. The sensors have been washed with water beforemeasurements to obtain a reproducible constant potential, then thearray has been exposed into analysed solution. Repeated readings ofsensor potentials have been registered with 5 min intervals.

Calibration of the sensor arrays has been performed using a back-propagation ANN and also by partial least squares regression (PLS).Sensor array performance was assessed using cross-validation and testvalidation.

Due to a specially elaborated measuring procedure, such componentsof the multicomponent solutions as Ca2þ, HCO3

2, Naþ, Kþ, Cl2 and alsopH could be determined with the errors as low as 2–4% that complieswith requirements of clinic analysis. Results of component determi-nation in test solution are shown in Table 10.2. The possibility todetermine precisely concentration of magnesium in the presence ofcalcium and the content of hydrophosphates using the electronic tonguewas discovered. It was shown that the application of sensor arrays

465

allows enhancing selectivity and decreasing determination errors.Thus, the electronic tongues could represent a new direction of chemicalsensor application in medical analysis.

The next example of quantitative analysis using the electronic tonguesystem deals with quite unusual analyte—bacterial colony counts. Thevoltammetric electronic tongue with five working electrodes (platinum,gold, iridium, palladium and rhodium) was applied to monitoring milksouring process and bacteria growth estimation [56].

Each measurement cycle started by cleaning of working electrodes bypolishing, dipping into sulphuric acid for 1 min and rinsing withdistilled water. When sensor array was dipped into the samplespotential of 2 V was applied for them for 3 s, then potential of 22 Vfor the same length of time as addition electrode cleaning. Afterwardsmeasurements with sensor array using LAPV technique were per-formed. Potentials applied to working electrodes were in the range from600 to –400 mV with the step of 100 mV.

The measurements were made in 11 samples of milk (pasteurized,homogenized, with fat content 3%) immediately after package openingand then with the interval of about 30 min during maximum 18 h.

TABLE 10.2

Results of simultaneous determination of calcium, magnesium, pH, hydrocarbonate andphosphates in solutions modelling human blood plasma using electronic tongue

Ion Real (mol/L) Found (mol/L) ðn ¼ 3Þ Sr

Mg2þ 1.0 £ 1023 1.0 £ 1023 4 £ 1025

7.6 £ 1024 7.6 £ 1024 2 £ 1025

5.0 £ 1024 5.0 £ 1024 1 £ 1025

Ca2þ 1.5 £ 1023 1.51 £ 1023 5.6 £ 1025

1.2 £ 1023 1.2 £ 1023 4 £ 1025

7.9 £ 1024 8.2 £ 1024 4 £ 1026

pH 6.90 6.95 0.017.00 7.02 0.047.10 7.07 0.04

HCO32 4.0 £ 1022 3.9 £ 1022 2 £ 1023

3.5 £ 1022 3.6 £ 1022 2 £ 1023

3.0 £ 1022 3.2 £ 1022 3 £ 1024

H2PO42 1.4 £ 1024 1.6 £ 1024 3 £ 1025

1.7 £ 1024 1.7 £ 1024 1 £ 1025

2.0 £ 1024 1.9 £ 1024 1 £ 1025

466

Milk samples were stored at the 218C. A bacteriostatic agent—sodiumazide was added to two milk samples to prevent bacterial growth.Bacterial colony counts in milk were checked every 2 h using dip-slidetest and the results were used afterwards as reference values forcalibration. Multivariate calibration was performed using partial leastsquare regression and back-propagation ANN. Similar results wereobtained using both techniques. Predicted vs. true values for bacterialcounts determined using dip-slide test are shown in the Fig. 10.6. Theresults of prediction were acceptable in most cases. However, it is notclear what is the mechanism of the voltammetric electronic tonguesensitivity to bacteria.

The electronic tongues will not possibly substitute for multiple andwidely known analytical methods of quantitative analysis. However,there are certain situations where the use of electronic tongues could behighly advantageous and desirable. Besides, the tongues may becapable performing quantitative analysis and recognition of theanalytes simultaneously, which is also an unusual feature helping toevaluate, e.g. integral quality of a product.

10.3.3 Qualitative analysis, recognition, identificationand classification

Classification and recognition of different liquid samples is probably thelargest area of the electronic tongue application. We have also chosenonly few examples illustrating this type of analysis though muchgreater number of them is abundant in the literature.

Recognition of orange juices, apple juices and fruit beverages wasperformed by using the electronic tongue based on 29 potentiometricsensors of different types [79]. Firstly, the sensitivity and selectivity ofthe sensors of the array towards the six main organic acids present infruits in highest quantities was investigated. Measurements were madein solutions of acetic, malic, tartaric, lactic, succinic and citric acids inthe concentration range from 1025 to 1021 mol/L. Based on thisknowledge sensors displaying sensitivity to organic acids were includedinto the sensor array.

The measurements were made in six types of 100% orange juice andthree types of 100% apple juice produced from concentrates by differentmanufactures, two types of beverages containing apple juice and freshlypressed orange juice. The sensitivity of the sensor system to juice dilution

467

by distilled water and sugar syrup was studied. For this purpose twotypes of orange juice (freshly pressed and commercial brand made fromconcentrate) and two types of apple juice (both commercial brands made

Fig. 10.6. Values of bacterial counts determined using dip-slide test (true) and the electro-nic tongue (predicted). Calibration was made using PLS. True values are represented onthe horizontal axis and predicted values are represented on the vertical axis: (a) sample 1;(b) sample 2; (c) sample 3; (d) sample 4; and (e) sample 7. Reprinted form MeasurementScience and Technology, 9 (1998) 1937–1946 (F. Winquist, C. Krantz-Rulcker, P. Wideand I. Lundstrom, ‘Monitoring of freshness of milk by an electronic tongue on the basis

of voltammetry’), with permission from IoP.

468

from concentrate) were chosen. Sample description is shown in Table10.3. These samples were diluted either by distilled water or by 10%sugar syrup in such a way that the added content of water or syrupcontent in the juice increased from 0 to 46 vol%. Three replicas of eachsolution were run. Aging dynamics of fruit juices was studied in threesamples of orange juice including freshly pressed one and in all samplesof apple juices and beverages. The samples were kept at roomtemperature (about 208C) for a week. The measurements with thesensor array were made in each sample two times a day. The sensorresponses in all samples were recorded after 5 min of stirring. Thesensors were washed with distilled water between measurements.

The system was able to discriminate between orange and apple juices,made from concentrate by different manufactures, and to distinguishthem from fresh-pressed natural juice (Figs. 10.7 and 10.8). Further-more, the electronic tongue was applied to monitor the process of thedilution of juices by water and sugar syrup. The dilution degree wasdetermined with high accuracy and reproducibility. Even 1% of water inthe juice could have been determined (Fig. 10.9). The freshness of the

TABLE 10.3

Description of samples of fruit juices and beverages measured using the electronic tongue

Name Description

A 100% orange juice, made from concentrate (Moscow,Russia)

B Another brand of 100% juice, made by the same manufactureras A (Moscow, Russia)

C 100% orange juice, made from concentrate(St Petersburg, Russia)

D 100% orange juice, made from concentrate(St Petersburg, Russia)

E 100% orange juice with pulp, made fromconcentrate (Lipetsk, Russia)

Fresh Natural orange juice, freshly pressedF 100% apple juice, made from concentrate, clarified

(Moscow, Russia)G 100% apple juice, made from concentrate with

sugar (St Petersburg, Russia)H 100% apple juice, made from concentrate (Schelkovo,

Russia)Beverage1 Apple nectar (min 50% natural juice, sugar syrup) (Schelkovo, Russia)Beverage2 Apple beverage, containing natural juice (min 12%),

sugar syrup, citric acid, vitamins and water (Moscow, Russia)

469

juices and the dynamics of juice deterioration with time were observedin an aging experiment. Different degrees of juice spoilage weredetected (Fig. 10.10). Thus, the possibility to use electronic tongue forfruit juice quality monitoring was shown.

Fig. 10.7. Discrimination of orange juices made by different manufactures fromconcentrate and freshly pressed orange juice using the electronic tongue.

Fig. 10.8. Discrimination of apple juices produced by different manufactures fromconcentrate and beverages containing apple juice using the electronic tongue.

470

Another example demonstrates application of the voltammetricelectronic tongue for recognition of different types of fermented milk[57]. The voltammetric electronic tongue included six working electrodes(gold, iridium, palladium, platinum, rhenium, and rhodium). It wascomplemented in this experiment by other types of liquid sensors, whichwere conductivity and temperature probes and three potentiometricsensors: pH, chloride-selective and carbon dioxide gas electrodes.Voltammetric measurements were made in the same way as describedin the pervious chapter, but potential applied to the working electrodeswas in the range from 1000 to 21000 mV with steps of 200 mV.Measurements were made in six samples of fermented milk, whichdiffered in microorganisms used for their preparation. Experimentaldata were processed by using principal component analysis and backpropagation ANN. It was demonstrated that the combination of signal ofdifferent nature, e.g. voltammetric, potentiometric and conductometricimproves the discrimination abilities of the device. Mixed electronictongue could distinguish almost all fermented milk samples (Fig. 10.11).

Fig. 10.9. Detection of the dilution of orange juice by water using the electronic tongue.Even minor water additives starting from 1% can be detected.

471

10.3.4 Comparison with human perception of flavours

The electronic tongues were widely applied to the recognition ofdifferent sorts of food, often close in taste. This fact together with anallusion concerning similar organization principles of artificial andbiological (mammalian or human) taste and olfaction lead to thenumerous attempts of looking for correlation between human tasteperception and the instrument output. This approach may be illustrated

Fig. 10.10. Monitoring of orange juice spoilage by the electronic tongue. The numbersdenote the duration (the days) of juice storage. Number 1 is the first day, when the package

with the juice was opened.

472

by the good correlation between electronic tongue response in differentsorts of coffee and assessments produced by the trained sensory panel.

Almost all commercial brands of coffee are actually blends of differentsorts of coffee beans. The quality of harvests differs from year to year butthe same commercial coffee brand must be prepared each year withreproducible and flavour characteristic familiar to consumers. Thisprocedure is performed now with the help of the taste panels and, hence,is time-consuming and expensive. The possibility to partly replacehumans in the coffee industry (and similar tasks) may be of big practicalimpact. The attempts of using sensor systems (both noses and tongues)for coffee analysis were carried out many times.

As an example one can mention coffee recognition performed using thepotentiometric electronic tongue [71]. Ten coffee samples were analysed,including seven individual sorts and three commercial brands. Themeasurements were made in coffee brews, which were prepared using

Fig. 10.11. PCA plot resulting from measurements in fermented milk samples usingvoltammetric electronic tongue, ion-selective electrodes and conductivity probe, that is thehybrid electronic tongue. Points corresponding to measurements in the same fermentedmilk sample are encircled. Reprinted from Analytica Chimica Acta, 406 (2000) 147–157(F. Winquist, S. Holmin, C. Krantz-Rulcker, P. Wide and I. Lundstrom, ‘A hybrid electronic

tongue’) with permission from Elsevier Science.

473

weighted amount of coffee and standard water volume and cooled down toroom temperature. The electronic tongue was able to distinguish allcoffee samples. The experiments made with different coffee concen-trations and water compositions (distilled, salted distilled and tap water)showed that coffee samples were correctly distinguished in all cases (Fig.10.12). Coffee taste assessments made by the professional sensory panelof a manufacturing company were obtained from the producer along withcoffee samples. Four parameters were evaluated: flavour, acidity, bodyand smell, which obviously characterize taste, odour and flavour ofcoffee. It was found that the first PC correlates with flavour and smell ofcoffee. Then the multivariate calibration was performed using back-propagation neural network, taste panel scores being used as thereference data. Afterwards, the electronic tongue could correctly predictsensory assessment values of all four flavour parameters, results areshown in the Table 10.4.

Fig. 10.12. Recognition of 11 types of coffee (eight individual coffees and three blendedcommercial brands) using the electronic tongue. The data were processed by PCA. Thebrew was prepared using different types of the water (and salted water) and different

coffee concentrations.

474

TABLE 10.4

Organoleptic scores of coffee produced by a professional panel of tasters and the values determined by the ‘electronic tongue’

Sample Flavour Acidity Body Smell

e-tongue Tasters e-tongue Tasters e-tongue Tasters e-tongue Tasters

5 4.8 ^ 0.2a 3–7 4.8 ^ 0.1 5–8 7.0 ^ 0.1 5–9 5.3 ^ 0.2 4–76 7.8 ^ 0.1 7–9 3.6 ^ 0.3 2–5 8.5 ^ 0.01 8–9 8.4 ^ 0.01 8–97 3.1 ^ 0.3 1–2 5.7 ^ 0.3 5–8 8.4 ^ 0.1 8–9 3.8 ^ 0.3 2–38 4.1 ^ 0.4 2–4 5.4 ^ 0.3 5–8 6.0 ^ 0.1 5–7 4.7 ^ 0.3 3–5

aThe score and its standard deviation are shown.

475

The examples of high correlation of perceived food flavour with theelectronic tongue output are really numerous but are restricted by thesize of this publication.

10.3.5 Taste quantification

Besides different foodstuff recognition and attempts to establishcorrelation between electronic tongue output and human perception offlavour, quantification of the taste using the multisensor systems wasperformed. Taste quantification starts from the calibration of anelectronic tongue when basic taste substances are used in the sameway, as for sensory panel training (calibration) before food flavourassessment. Training samples of known flavour type and intensity areanalysed and processed using the system. After this calibration theelectronic tongue is presumed producing taste estimate of foodstuffsanalogous to human assessments. Another task in taste quantificationapproach is the evaluation of taste masking effect of differentsubstances, e.g. degree of bitterness masking by sweet substances.

The first example in this respect describes a simple experiment withtaste substances widely used for human sensory panel training(calibration). Measurements with the electronic tongue were performedin solutions containing three taste substances: NaCl (salty), lactic acid(sour) and L-leucine (bitter) and their binary mixtures [53]. After theelectronic tongue calibration in individual solutions, the presence andintensity of each individual taste in binary mixtures was correctlypredicted (Table 10.5).

An attempt to build a taste map using the taste sensor and thereby toexpress the tastes of foodstuffs by combinations of basic tastes wasperformed using the taste sensor [2]. For this purpose 256 solutionscontaining four basic taste substances NaCl, HCl, quinine and sucroseat four concentration levels were prepared and measured. Dependenceof each sensor potential on four component concentrations wasapproximated by quadratic function. Then the measurements incommercial soft drink were performed and the resulted pattern wasused to calculate quantity of basic taste substances, necessary toproduce the same taste. A drink made with calculated content of basicsubstances appeared to have the same taste as the commercial oneaccording to human perception. However, the possibility to model

476

the taste by combination of four basic tastes was successfullydemonstrated on the single sample of a commercial soft drink only.

Suppression of certain taste perceived by humans in the presence ofthe other substances is well known. The suppression of bitter taste bysweet substances, which is often used to mask bitter taste of drugs, wasstudied in several works.

Suppression of quinine bitterness by sucrose and artificial sweetsubstances—phospholipids was studied using the taste sensor [89,90].The response of the taste sensor to quinine was expressed using t—scalethat is used to described taste strength. A linear relationship betweenthe strength of bitterness t and first principal component calculatedfrom the taste sensor data was established. The measurements inindividual quinine solutions were used in this case. Predicted degree ofbitterness dropped significantly at sucrose concentration 1 M(Fig. 10.13). The same experiment was repeated with phospholipids.

Recognition of different bitter drug substances was also performed bythe potentiometric electronic tongue [53]. Masking effect of differentflavourings and artificial and natural sweeteners added to bitterpharmaceuticals was evaluated.

The electronic tongue system appears being an useful tool for tastemasking assessment especially when the samples with unpleasant tastehave to be evaluated, or the samples containing active drug substancesmust be characterized.

TABLE 10.5

Prediction of taste intensity in binary mixtures of taste substances using the electronictongue after its calibration in individual substances

Sample Sourness Bitterness Saltiness

Real Predicted Real Predicted Real Predicted

B1 Low Low Low Low 0 0B2 Low Medium 0 0 Low MediumB3 0 0 Low 0 Low MediumB4 High High High High 0 0B5 High High 0 0 High HighB6 0 0 High Low High HighB7 High High 0 0 Low MediumB8 0 0 High Medium Low LowB9 0 0 Low Low High High

477

10.3.6 Application of hybrid systems

An idea of applying combined systems consisting of liquid and gassensors simultaneously to food analysis was inspired by the fact thatmammalians (and humans) use both olfaction and taste for food flavoursensing. It is worth mentioning that sensitivity and selectivity ofsensors used in electronic nose and tongue systems are usually quitedifferent from those of biological receptors due to the different physicaland chemical principles artificial senses are normally based upon.Nevertheless, the idea to put together hybrid systems combining bothelectronic nose and tongue and use them for discriminating such objectsas wines [64], fruit juices [58,59], milk and even urine samples [63] lookslogic and, hopefully, fruitful.

As an example an application of a hybrid system to the recognition ofmilk and to classification of urine samples is discussed here [63]. Bothelectronic nose and tongue were based on metalloporphyrins, which

Fig. 10.13. The suppression of bitterness of quinine by sucrose expressed by the t scale.Reprinted from Sensors and Actuators B, 64 (2000) 205–215 (K. Toko, ‘Taste sensor’) with

permission from Elsevier Science.

478

are capable of forming strong complexes with different inorganicand organic substances. Properties of metalloporphyrins may varydepending on coordinated metal, peripheral radicals and functionalgroups and conformations of macrocyclic skeleton. The electronic noseconsisted of eight quartz microbalance (QMB) sensors with thin layer ofmetalloporphyrin deposited onto them. Eight different metallopor-phyrins were used. The electronic tongue included seven potentiometricsensors with plasticized polyvinylchloride membranes also containingdifferent metalloporphyrins.

The measurements with both systems were made in milk samplesand also in the samples of urine. Six different milk samples produced bydifferent manufacturers and thermally treated in different ways wereused. Three of them were pasteurized milk and three other ones wereultrahigh temperature treatment (UHT) milk. Two packages of eachmilk were purchased at the retail store. All milk samples had the samefat content—2.5%. Milk packages were opened simultaneously and themeasurements were made in each of them. One opened package of eachtype of milk was stored between measurements in the fridge at thetemperature of about þ58C, the other similar package was stored atroom temperature. The measurements were made (all at roomtemperature (þ258C)) during three days after package opening.

Both electronic nose and tongue could distinguish between fresh andsour or spoiled milk samples (Figs. 10.14 and 10.15). A separationbetween UHT and pasteurized is not achieved by the electronic nosealone but the data related to spoiled samples are plotted in the center ofthe plot, and in particular at the center of the first principal componentaxis. Discrimination of milk samples undergone different heat treat-ment was possible by using the electronic tongue data alone (Fig. 10.15).A clear distinction, along the second principal component, between UHTand pasteurized milk is achieved in this case. On the other hand, thetransition from fresh to spoiled samples occurs along the first and thesecond principal component in the case of UHT and pasteurized milk, re-spectively. Merging of the data from the two instruments allowed im-proving separation offresh and sour milk and at the same time somewhatbetter distinguishing of pasteurized and UHT milk samples (Fig. 10.16).

Urine samples were provided by the analytical laboratory of Instituteof Pediatric Clinic of hospital ‘Santa Cuore’ in Rome. Twenty-sevensamples of human urine were measured, including three samples fromhealthy people and four samples containing blood due to differentdiseases of the patients. The data processing was performed mainly

479

using principal component analysis. Two approaches to the data fusionwere used. Low level of abstraction was used for processing measure-ment results in urine. This means that data from the two instrumentswere merely combined into the same data set and all mathematicaloperation, e.g. normalization and PCA calculations, were performedwith this ‘supra-sensor’ data set. High level of abstraction assumed thatthe data from different instrument were analysed separately andmerged together after feature extraction by, e.g. PCA. Later approachwas also used for processing the measurement results in milk samplesdescribed above.

Correlation between the electronic tongue response in urine samplesand pH and specific weight of the urine was found. On the other handthe output of the electronic nose was better correlated to blood cell countin the samples. This suggests the independence of the performance ofthe two systems. Reliable discrimination of urine samples from healthy

Fig. 10.14. Score plot of the PCA of the electronic-nose data in milk experiment. Reprintedfrom Sensors and Actuators B, 64 (2000) 15–21 (C. Di Natale, R. Paolesse, A. Macagnano,A. Mantini, A. D’Amico, A. Legin, L. Lvova, A. Rudnitskaya and Yu. Vlasov, ‘Electronicnose and electronic tongue integration for improved classification of clinical and food

samples’) with permission from Elsevier Science.

480

and ill subjects was possible using the combined data from bothinstruments.

Thus, the combination of an electronic tongue with an electronic noselikely enhances recognition power of the hybrid system. This featuremay be of significant practical impact when very similar samples areanalysed and reliable discrimination is crucial.

10.4 PROBLEMS AND PROSPECTIVE

The electronic tongues present an emerging and promising field ofchemical sensor R&D, which should be considered as a noveland somewhat unusual branch of analytical science. Such systemsmay be very useful as quality control tool in food industry, medicine,

Fig. 10.15. Score plot of the PCA of the electronic tongue data in milk experiment. Arrowsconnect the transition from fresh to spoiled milk for each milk sample and the shadowedregion contains the spoiled sample. Reprinted from Sensors and Actuators B, 64 (2000)15–21, (C. Di Natale, R. Paolesse, A. Macagnano, A. Mantini, A. D’Amico, A. Legin,L. Lvova, A. Rudnitskaya and Yu. Vlasov, ‘Electronic nose and electronic tongueintegration for improved classification of clinical and food samples’) with permission

from Elsevier Science.

481

environmental monitoring, etc. Also they can be rapid and easy-to-usemulticomponent quantitative analysis instruments. The correlation ofthe electronic tongue output with human sensory perception has beenfound for numerous analytes. This is an exciting feature, promisingnew, unconventional but practically very important and highlyinteresting applications. Though some of the electronic tongues arealready offered commercially, this is still young scientific area andthe knowledge in this field remains significantly empirical. Seriousefforts are demanded in sensing mechanism studies and theoreticalconsiderations, in the development of new sensor compositions and inthe procedures of electronic tongue application for practical tasks. Wewould expect new achievements in all these fields in the nearest future.

Fig. 10.16. Plot of the second principal component of the electronic tongue data vs. thefirst principal component of the electronic nose data. This plot represents the fusion ofthe two sensor systems preserving the main features. Arrows connect the transition fromfresh to spoiled milk for each milk sample and the shadowed region contains the spoiledsample. Reprinted from Sensors and Actuators B, 64 (2000) 15–21, (C. Di Natale,R. Paolesse, A. Macagnano, A. Mantini, A. D’Amico, A. Legin, L. Lvova, A. Rudnitskayaand Yu. Vlasov, ‘Electronic nose and electronic tongue integration for improved classi-

fication of clinical and food samples’), with permission from Elsevier Science.

482

REFERENCES

1 Yu. Vlasov, A. Legin, A. Rudnitskaya, A. D’Amico and C. Di Natale, Sens.Actuat. B, 65 (2000) 235.

2 K. Toko, Meas. Sci. Technol., 9 (1998) 1919.3 F. Winquist, P. Wide and I. Lundstrom, Anal. Chim. Acta, 357 (1997) 21.4 F. Haber and Z. Klemensiewicz, Z. Phys. Chem. Leipzig, 64 (1909) 385.5 K. Camman, Working with Ion-Selective Electrodes, Springer, Berlin, 1979.6 Yu. Vlasov and E. Bychkov, Ion-Selective Electrode Rev., 9 (1987) 5.7 E. Bakker, P. Buhlmann and E. Pretsch, Chem. Rev., 97 (1997) 3083.8 P. Buhlmann, E. Pretsch and E. Bakker, Chem. Rev., 98 (1998) 1593.9 D.G. Buerk, Biosensors: Theory and Application, Technomic, Lancaster,

1993.10 J. Janata, M. Josowicz, P. Vanysek and D.M. DeVaney, Anal. Chem., 70

(1998) 179R.11 M. Otto and J.D.R. Thomas, Anal. Chem., 57 (1985) 2647.12 K. Beebe and B. Kowalski, Anal. Chem., 60 (1988) 2273.13 K. Beebe, D. Uerz, J. Sandifer and B. Kowalski, Anal. Chem., 60 (1988) 66.14 R.J. Forster, F. Regan and D. Diamond, Anal. Chem., 63 (1991) 876.15 R.J. Forster and D. Diamond, Anal. Chem., 64 (1992) 1721.16 D. Diamond and R.J. Forster, Anal. Chim. Acta, 276 (1993) 75.17 M. Hartnett, D. Diamond and P.G. Barker, Analyst, 118 (1993) 347.18 W.E. van der Linden, M. Bos and A. Bos, Anal. Proc., 26 (1989) 329.19 M. Bos, A. Bos and W.E. van der Linden, Anal. Chim. Acta, 233 (1995) 31.20 J.W. Gardner and P.N. Bartlett (Eds.), Sensors and Sensory Systems for an

Electronic Nose, NATO ASI Series E: Applied Sciences, Vol. 212. Kluwer,Dordrecht, 1992.

21 W. Gopel, Ch. Ziegler, H. Breer, D. Schild, R. Apfelbach, J. Joerges andR. Malaka, Biosens. Bioelectron., 13 (1998) 479.

22 G.F. Stewart and M.A. Amerine, Introduction to Food Science andTechnology, Academic Press, New York, 1973.

23 Y. Kawamura and M.R. Kare, Umami: A Basic Taste, Marcel Dekker, NewYork, 1987.

24 R.W. Moncrieff, J. Appl. Physiol., 16 (1961) 742.25 K. Persaud and G.H. Dodd, Nature, 299 (1982) 352.26 T.A. Dickinson, J. White, J.S. Kauer and D.R. Walt, TIBTECH, 16 (1998)

250.27 A. Legin, A. Rudnitskaya, Yu. Vlasov, C. Di Natale and A. D’Amico, Sens.

Actuat. B, 58 (1999) 464.28 A. Legin, A. Rudnitskaya, Yu. Vlasov, A. D’Amico and C. Di Natale, Proc.

Eighth Int. Conf. Electroanal. Bonn, Germany, 2000, p. A 27.29 M. Nagele, E. Bakker and E. Pretsch, Anal. Chem., 71 (1999) 1041.

483

30 A. Legin, Yu. Vlasov, A. Rudnitskaya and E. Bychkov, Sens. Actuat. B, 34(1996) 456.

31 Yu. Vlasov, A. Legin and A. Rudnitskaya, Sens. Actuat. B, 44 (1997) 532.32 A. Legin, A. Rudnitskaya, A. Smirnova, L. Lvova and Yu. Vlasov, J. Appl.

Chem. (Russ.), 72 (1999) 114.33 K. Toko, K. Hayashi, M. Yamanaka and K. Yamafuji, Tech. Digest Ninth

Sensor Symp. 1990, pp. 193–196.34 K. Hayashi, M. Yamanaka, K. Toko and K. Yamafuji, Sens. Actuat. B, 2

(1990) 205.35 K. Toko, Mater. Sci. Engng C, 4 (1996) 69.36 K. Toko, Sens. Actuat. B, 64 (2000) 205.37 K. Oohira, K. Toko, H. Akiyama, H. Yoshihara and K. Yamafuji, J. Phys.

Soc. Jpn, 64 (1995) 3554.38 K. Hayashi, K. Toko, M. Yamanaka, H. Yoshihara, K. Yamafuji, H. Ikezaki,

R. Toukubo and K. Sato, Sens. Actuat. B, 23 (1995) 55.39 S. Iiyama, K. Toko, T. Matsuno and K. Yamafuji, Chem. Senses, 19 (1994)

87.40 S. Iiyama, S. Ezaki, K. Toko, T. Matsuno and K. Yamafuji, Sens. Actuat. B,

24 (1995) 75.41 H. Akiyama, K. Toko and K. Yamafuji, Jpn. J. Appl. Phys. Part 1, 35 (1996)

5516.42 K. Toko, H. Akiyama, K. Chishaki, S. Ezaki, T. Iyota and K. Yamafuji, Sens.

Mater., 9 (1997) 321.43 K. Toko, R. Yasuda, S. Ezaki and T. Fujiyishi, Trans. IEE Jpn E, 118

(1998) 1.44 Y. Kanai, M. Shimizu, H. Uchida, H. Nakahara, C.G. Zhou, H. Maekawa

and T. Katsube, Sens. Actuat. B, 20 (1994) 175.45 Y. Sasaki, Y. Kanai, H. Uchida and T. Katsube, Sens. Actuat. B, 25 (1995)

819.46 Yu. Vlasov and A. Legin, Fresenius J. Anal. Chem., 361 (1998) 255.47 C. Di Natale, F. Davide, J.A.J. Brunink, A. D’Amico, Yu. Vlasov, A. Legin

and A. Rudnitskaya, Sens. Actuat. B, 34 (1996) 539.48 C. Di Natale, A. Macagnano, F. Davide, A. D’Amico, A. Legin, Yu. Vlasov,

A. Rudnitskaya and B. Seleznev, Sens. Actuat. B, 44 (1997) 423.49 A. Rudnitskaya, A. Ehlert, A. Legin, Yu. Vlasov and S. Buttgenbach,

Talanta, 55 (2001) 425.50 J. Mortensen, A. Legin, A. Ipatov, A. Rudnitskaya, Yu. Vlasov and K. Hjuler,

Anal. Chim. Acta, 403 (2000) 273.51 A. Legin, B. Seleznev, A. Rudnitskaya, Yu. Vlasov, S. Tverdokhlebov,

B. Mack, B. Abraham, T. Arnold, L. Baraniak and H. Nitsche, Czechoslo-vak. J. Phys., 49(S1) (1999) 679.

52 A. Legin, A. Smirnova, A. Rudnitskaya, L. Lvova and Yu. Vlasov, Anal.Chim. Acta, 385 (1999) 131.

484

53 A. Legin, A. Rudnitskaya, B. Seleznev and Yu. Vlasov, Proc. Seventh Int.Symp. Olfaction Electron. Nose ISOEN. Brighton, UK, 2000, p. 195.

54 M. Schoning, C. Schmidt, J. Schubert, W. Zander, S. Mesters, P. Kordos,H. Luth, A. Legin, B. Seleznev and Yu. Vlasov, Sens. Actuat. B, 68 (2000) 254.

55 Yu. Mourzina, M. Schoning, J. Schubert, W. Zander, A. Legin, Yu. Vlasovand H. Luth, Anal. Chim. Acta, 433 (2001) 103.

56 F. Winquist, C. Krantz-Rulcker, P. Wide and I. Lundstrom, Meas. Sci.Technol., 9 (1998) 1937.

57 F. Winquist, S. Holmin, C. Krantz-Rulcker, P. Wide and I. Lundstrom,Anal. Chim. Acta, 406 (2000) 147.

58 P. Wide, F. Winquist, P. Bergsten and E.M. Petriu, IEEE Trans. Instrum.Meas., 47 (1998) 1072.

59 F. Winquist, I. Lundstrom and P. Wide, Sens. Actuat. B, 58 (1999) 512.60 J.J. Lavigne, S. Savoy, M.B. Clevenger, J.E. Ritchie, B. McDoniel, S.J. Yoo,

E.V. Anslyn, J.T. McDevitt, J.B. Shear and D. Neikirk, J. Am. Chem. Soc.,120 (1998) 6429.

61 A. Riul Jr, D.S. Dos Santos Jr, K. Wohnrath, R. Di Tommazo, A.C.P.L.F.Carvalho, F.J. Fonseca, O.N. Oliveira Jr, D.M. Taylor and L.H.C. Mattoso,Lamgmuir, 18 (2002) 239.

62 J. Kondoh and S. Shiokawa, Jpn. J. Appl. Phys. Part 1, 33 (1994) 3095.63 C. Di Natale, R. Paolesse, A. Macagnano, A. Mantini, A. D’Amico, A. Legin,

L. Lvova, A. Rudnitskaya and Yu. Vlasov, Sens. Actuat. B, 64 (2000) 15.64 S. Baldacci, T. Matsuno, K. Toko, R. Stella and D. De Rossi, Sens. Mater.,

10 (1998) 185.65 Neural Computing, Neural Ware, Pittsburg, USA, 1997.66 K. Esbensen, Multivariate Analysis in Practice, 5th edn, Camo ASA/CAMO

Process AS, Norway, 2001.67 H. Martens and T. Naes, Multivariate Calibration, Wiley, 1989.68 A. Legin, A. Rudnitskaya, Yu. Vlasov, C. Di Natale, A. Mantini, E. Mazzone,

A. Bearzotti and A. D’Amico, Alta Frequenza (Italian), 10 (1999) 1.69 A. Legin, A. Rudnitskaya, Yu. Vlasov, C. Di Natale, E. Mazzone and

A. D’Amico, Electroanalysis, 11 (1999) 814.70 A. Legin, A. Rudnitskaya, Yu. Vlasov, C. Di Natale, E. Mazzone and

A. D’Amico, Sens. Actuat. B, 65 (2000) 232.71 A. Legin, A. Rudnitskaya, B. Seleznev and Yu. Vlasov, Food Sci. Technol.,

37 (2002) 375.72 S. Iiyama, M. Yahiro and K. Toko, Sens. Mater., 7 (1995) 191.73 T. Fukunaga, K. Toko, S. Mori, Y. Nakabayashi and M. Kanda, Sens. Mater.,

8 (1996) 47.74 A. Legin, A. Rudnitskaya and Yu. Vlasov, Proc. Sixth Int. Symp. Kinetics

Anal. Chem. KAC’98, Kassandra Halkidiki, Greece, 1998, p. 106.75 P. Ivarsson, S. Holmin, N.-E. Hoejer, C. Krantz-Ruelcker and F. Winquist,

Sens. Actuat. B, 76 (2001) 449.

485

76 P. Ivarsson, Yu. Kikkawa, F. Winquist, C. Krantz-Ruelcker, N.-E. Hoejer,K. Hayashi, K. Toko and I. Lundstrom, Anal. Chim. Acta, 449 (2001) 59.

77 A. Legin, A. Rudnitskaya, Yu. Vlasov, C. Di Natale, F. Davide andA. D’Amico, Sens. Actuat. B, 44 (1997) 291.

78 A. Rudnitskaya, A. Legin, C. Salles and P. Mielle, Proc. Eighth Int. Symp.Olfaction Electron. Nose ISOEN, Washington, DC, 2001.

79 S. Makarychev-Mikhailov, A. Legin, A. Rudnitskaya, O. Goryacheva andYu. Vlasov, Proc. Int. Conf. Anal. Sci, Tokyo, Japan, 2001.

80 Y. Kikkawa, K. Toko and K. Yamafuji, Sens. Mater., 5 (1993) 83.81 A. Legin, A. Rudnitskaya, L. Lvova, Yu. Vlasov, A. D’Amico, C. Di Natale,

R. Paolesse, Proc. Fifth Ital. Conf. Sens. Microsyst., Lecce, Italy, WorldScientific, Singapore, 2000, pp. 263–269.

82 K. Toko, T. Iyota, Y. Mizota, T. Matsuno, T. Yoshioka, T. Doi, S. Iiyama,T. Kato, K. Yamafuji and R. Watanabe, Jpn. J. Appl. Phys. Part 1, 34 (1995)6287.

83 T. Mottram, A. Legin, A. Rudnitskaya, J.L. Fitzpatrick and P.D. Eckersall,Livestock Production Science (2003) in press.

84 A. Legin, A. Rudnitskaya, Yu. Vlasov, C. Di Natale, E. Mazzone andA. D’Amico, Proc. Int. Conf. Eurosens. XIII, The Hague, The Netherlands,1999, pp. 1017–1020.

85 A. Legin, A. Rudnitskaya, B. Seleznev, Yu. Vlasov, P. Mielle, Proc. SecondSymp. In Vino Anal. Sci., Bordeaux, France, 2001, p. 195.

86 Y. Arikawa, K. Toko, H. Ikezaki, Y. Shinha, T. Ito, I. Oguri and S. Baba,Sens. Mater., 7 (1995) 261.

87 Yu. Vlasov, B. Seleznev, A. Ivanov, A. Rudnitskaya and A. Legin, Proc. FifthItalian Conf. Sensors Microsyst., Lecce, Italy, 2000.

88 A. Legin, A. Rudnitskaya, B. Seleznev, Yu. Vlasov and V. Velikzhanin,Proc. Eighth Int. Symp. Olfaction Electron. Nose ISOEN, Washington, DC,2001.

89 S. Takagi, K. Toko, K. Wada, H. Yamada and K. Toyoshima, J. Pharm. Sci.,87 (1998) 552.

90 S. Tagaki, K. Toko, K. Wada and T. Ohki, J. Pharm. Sci., 90 (2001) 2042.

486