Eur Food Res Technol (2004) 218:198–207DOI 10.1007/s00217-003-0834-9

O R I G I N A L P A P E R

S. Ampuero · S. Bogdanov · J.-O. Bosset

Classification of unifloral honeys with an MS-based electronic noseusing different sampling modes: SHS, SPME and INDEX

Received: 22 August 2003 / Revised: 20 October 2003 / Published online: 29 November 2003� Springer-Verlag 2003

Abstract An electronic nose based on mass spectrometryhas been applied to the control of the authenticity of thebotanical origin of honey. PCA and DFA models werebuilt based on groups of samples identified as typicalunifloral honey by a classical method, i.e. a combinationof sensory, pollen and physicochemical analysis. Swissunifloral honeys of the following types were analysed:acacia, chestnut, dandelion, lime, fir and rape. Threedifferent sampling modes were tested: static headspace(SHS), solid-phase micro-extraction (SPME) and inside-needle dynamic extraction (INDEX). The last two showedinteresting abilities to extract volatile components in ahigher concentration and, most important, heavier com-pounds than SHS. The best classification, under thesampling conditions used, was provided by the SPMEsampling mode. This method proved to be fast, reliableand powerful for this type of task. A good correlation wasfound between the present approach and the classical onefor the determination of the botanical origin of honey.

Keywords Unifloral honey · Group classification ·Volatile compound · VOC · Gas sensor · Electronic nose ·SHS · SPME · Inside-needle dynamic extraction

Introduction

Despite being mainly composed of sugars and water,honeys with a wide variety of flavour characteristics exist.The specificity of honey products, i.e. flavour, aroma,colour and texture, depends predominantly on the type offlowers, or plants, from where bees take the nectar, or the

honeydew, to produce honey. Some other factors have alesser influence on the properties of honey, i.e. thephysiology of bees and their foraging habits, the climaticconditions (temperature, humidity) and the post-collec-tion processing. Naturally, bees will forage any type offlower they can reach. As a result the honey producedmostly has a blend of flavours and is commonly sold inthe market simply as honey or mixed-flower honey.However, when the nectar is taken predominantly from asingle type of flower the honey produced has character-istic organoleptic properties adding to its commercialvalue. Thus, orange blossom or acacia honeys are soldworldwide at higher prices than other types of honey.Other examples are honeydew honeys which are espe-cially appreciated in central European countries such asGermany, Switzerland and Austria. Indeed, botanicaldenominations are widely employed on the marketbecause they offer consumers the choice among a varietyof different typical products, with prices depending onlocal consumer preferences.

The existing international norms, i.e. the honeystandard of the Codex Alimentarius of the United Nationsand the honey directive of the European Union, whichregulate the composition and manufacture of honey, alsoallow producers to indicate the botanical along with theregional, territorial, or topographical origin of theirproduct provided the honey originates essentially fromthe indicated origin and has the corresponding physico-chemical, organoleptic and microscopic characteristics.However, these norms do not specify the characteristicsof unifloral honeys. And so far, no certification methodsare available for assessing labelling compliance and thusprotecting consumers from fraud.

In fact, due to the enormous quantity of different floralsources normally attainable by bees for foraging and tothe great diversity within plant species, which is furtherinfluenced by the climatic and growing conditions, thedifferent parameters characterising unifloral honeys donot have a typical value but are defined in rather largeoften overlapping ranges [1, 2]. This is the reason whyclassical analytical methods for honey such as melissopa-

S. Ampuero ())Swiss Federal Research Station for Animal Production (RAP),CH-1725 Posieux, Switzerlande-mail: Silvia.Ampuero@rap.admin.chTel.: +41-26-407-7359Fax: +41-26-407-7300

S. Bogdanov · J.-O. BossetSwiss Federal Dairy Research Station (FAM),Schwarzenburgstrasse 161, CH-3003 Bern, Switzerland

lynology [3] (microscopic percentage determination ofthe different pollen particles present in honey), sensoryanalysis [4] and the different physicochemical analyses(although some of them have been validated andharmonised by the International Honey Commission [5])are unable on their own to classify honey samples by theirbotanical or geographical origin. Furthermore, pollen andsensory analyses need highly trained personnel and aretime consuming. Moreover, sensory analysis is alsosubjective (it is dependent on cultural/training influences,and the performance of the assessors may be influencedby external factors). Recent compilations of the currentanalytical methods for assessing the botanical origin ofhoney are available [1, 6]. Also, a data bank on thesensory, melissopalynology and physicochemical charac-teristics of the 17 main European unifloral honeys hasbeen prepared by the International Honey Commission[7]. Nowadays, the identification of the botanical origin ofhoney is carried out by a combination of these methods,electrical conductivity together with glucose/fructoseratio being the most important [2, 8].

In an attempt to unify and simplify the identification ofthe floral origin of honey in a single analytical methodsuitable for routine analysis, two different strategies havebeen proposed in the literature. The first one concerns theidentification of specific chemical markers for each typeof unifloral honey. The second strategy combines statis-tical tools (chemometrics) with a set of characteristics(“fingerprints”) of the different types of unifloral honey.

For the first strategy, the marker compounds bettersuited for the identification of the botanical origin ofhoney are obviously those originating predominantly inthe floral source, or which are produced by the beethrough biochemical transformations of the nectar com-pounds. These markers may be volatile (aroma), e.g.methyl anthranilate for citrus honey [9, 10], or non-volatile, e.g. flavonoids [11]. Aliphatic fatty acids andhydrocarbons, among others, most likely originate frombeeswax and therefore are less likely to be good botanicalmarkers [11, 12]. Some other compounds such asalcohols, branched aldehydes and especially furan deriva-tives are mainly related to microbiological purity or toprocessing and storage conditions [10]. Although a lot ofeffort has been devoted to the profiling of chemicalcompounds present in unifloral honeys [10, 13, 14, 15,16] (more than 700 volatile compounds have beenidentified in honeys from different floral and geographicalorigins [17]), there is no consensus among researchersabout the proposed unifloral honey markers in theliterature, other than methyl anthranilate, as not allcompounds are found in the same type of honey and/orat similar concentrations. This is probably due not only tothe great diversity of honey as a natural product and to theimpossibility of ascertaining the exclusive floral source ofhoney samples, but also to the different analyticalmethods employed and especially the use of differentextraction/pre-concentration techniques which, by prefer-entially extracting certain compounds, change the result-ing chemical profile. Solvent extraction (Nickerson–

Likens [18] is an exhaustive, time consuming methodfor profiling volatiles and semi-volatiles in honey.Dynamic headspace extraction [10, 13] and the morepowerful solid-phase micro-extraction (SPME) [14, 15,16, 19] sampling techniques have the advantage, over theclassical static headspace sampling (SHS), of increasingthe concentration of volatiles (making detection easier)and extracting semi-volatiles which otherwise would notbe detected.

The second strategy employs the detection of a seriesof parameters (fingerprint) together with chemometricanalysis. Such techniques have been tried out withdifferent routine physicochemical parameters [20, 21,22, 23, 24], near-infrared spectroscopy and pyrolysis–mass spectrometry [25]. Since volatiles (and semi-volatiles) are closely related to aroma, the use of theirprofiles combined with chemometrics seems to be anappropriate option for the determination of the botanicalorigin of honey [16]. This strategy was followed in thepresent study with an electronic nose. The principle ofsuch a system is to excite an array of sensors with thevolatile fraction of the sample as a whole, avoiding anyseparation into individual chemicals [26]. This generatesa fingerprint (response of the array of sensors) character-istic of each sample. Fingerprints of similar samples aresimilar and fingerprints of different samples are different.The set of signals for all samples (set of fingerprints) isthen processed by a statistical program. The system buildsup a model based on a training set and subsequentlyclassifies unknown samples into different groups accord-ing to the model. In the case of electronic noses based onmass spectrometry (MS), each mass-to-charge ratio actsas an individual sensor detecting all ions and molecularfragments corresponding to that value (m/z). The advan-tage of MS-based electronic noses over the other sensortechnologies is that some chemical information can bederived from the variables (m/z) relevant for the discrim-ination.

The aim of this investigation was to study the potentialof a MS-based electronic nose for the classification ofseveral Swiss honey samples by their botanical origin inan effort to develop a fast screening method for this typeof task. For this purpose, different sampling techniques,with and without pre-concentration of volatiles, weretested (SPME, INDEX and SHS).

Materials and methods

Honey samples

One hundred and sixty-seven honey samples were collected by 143producers from different regions in Switzerland. The honeysamples were harvested over a 4-year period, 1998–2001, withthe exception of eight samples harvested between 1995 and 1997.The samples were designated by the producers as having a mainbotanical origin: acacia (Robinia ssp.), chestnut (Castanea ssp.),dandelion (Taraxacum ssp.), lime (Tilia ssp.), rape (Brassica ssp.)and fir (Abies ssp.). Fifty of these samples corresponded to thetypical unifloral type according to a sensory panel composed ofthree specialists and to classical analytical methods, i.e. electrical

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conductivity, fructose/glucose ratio and percentage of unifloralpollen. They were taken as reference samples (model set ofsamples). The remaining samples formed the test set of samples.Table 1 shows an overview of all samples.

Sensory analysis and physicochemical characterisation

A panel of three analysts, experienced in the sensory analysis ofhoney, performed sensory analysis of the odour (smell) and of thetaste of the different samples. Since sensory analysis was usedtogether with pollen and physicochemical analysis for the sortingout of typical unifloral honeys, a reduced sensory panel of threeassessors was considered appropriate in the present case. Sampleswere individually rated by comparison to a “reference unifloralsample”. Each reference unifloral sample showed the typicalsensory properties of the corresponding unifloral honey as well asthe correct pollen and physicochemical characteristics. Thefollowing sensory rating was applied: 0=typical unifloral, 1=lesstypical but still belonging to the unifloral group considered, and2=non-typical for the unifloral group considered. The sensoryaverages and standard deviations for each sample were calculated,with n=6 (3 analysts�2 (odour and taste)).

Only samples with a sensory average �0.33 and with a sensorystandard deviation <0.5 were considered as typical unifloralreference material and constituted the model set of samples. Thepollen analysis and physicochemical characteristics (electricalconductivity and fructose/glucose ratio) of these samples alsocorresponded to those of each unifloral honey group. The rest of thesamples constituted the test set.

Classification results from the electronic nose were compared tothe sensory panel classification and to a classification resultingfrom the combination (classical method) of sensory, pollen analysis(according to [3]), electrical conductivity and fructose/glucoseratio. These last results will be presented in a separate publicationon the characterisation of Swiss unifloral honeys [27].

Preparation of samples for electronic nose analysis

Honey (7.00€0.01 g) was placed in 10-mL vials together with1.00€0.02 mL of MilliQ water and 1.05€0.02 g of NaCl. Threereplicates per sample were prepared. The vials were hermeticallysealed with a blue silicon/Teflon septum and a cap immediatelyafter filling. Samples were kept frozen at �20 �C except duringsample preparation (prior to which they were thawed overnight atroom temperature) and during analysis. Water and salt were addedto honey to help the volatilisation of relevant aroma compounds.

Three consecutive sets of analyses were performed: (i) with thestatic headspace sampling mode (SHS), (ii) with the solid-phasemicro-extraction sampling mode (SPME) and (iii) with inside-needle dynamic extraction (INDEX). Thus, each vial was analysedonce by each type of analysis. Since not all honey samples wereavailable from the beginning of the study, SHS analysis was

performed on 100 honey samples, SPME analysis on 96 samples(dandelion and lime honeys were omitted during this test) andINDEX analysis on 151 out of a total of 167 honey samples (seeTable 1).

Using the same vials for the three sampling techniques did notchange the quality of the samples, as proved by the unalteredintensity of peaks 97 and 126 amu typical of 5-hydroxymethylfur-fural (HMF) (results not shown). In fact, HMF and furfuralconcentrations become significant only in samples kept at no lessthan 70–80 �C for more than 48 h [28].

Electronic nose

The analysis of volatile compounds from honey samples wasperformed with an electronic nose based on MS, SMart Nose (fromLDZ, Marin-Epagnier, Switzerland). This instrument is equippedwith a quadrupole mass spectrometer QMS 422 (from Inficon AG,formerly Balzers Instruments), a programmable Combi PALautosampler (from CTC Analytics, Switzerland) with a fullyautomatic capacity for 2�32 vials (of 10-mL capacity), and acomputer equipped with user-friendly software, SMart Nose 151,for statistical multivariate analysis. This instrument was used withthe three following sampling techniques.

Static headspace sampling (SHS)

For the SHS sampling mode each vial was conditioned for 15 minat 100 �C with agitation at 500 rpm prior sampling. Then, 2.5 mL ofthe sample headspace was taken with a 5-mL syringe (syringetemperature=110 �C) and injected into the SMart Nose (injectortemperature=120 �C). The ionic masses 10–140 amu were scannedthree consecutive times at 0.5 s/amu. Thus, a complete measuringcycle per sample lasted 4 min 15 s, of which 3 min 15 s was spentin scanning and 1 min in syringe purging with a flow of 200 mL/min of nitrogen (99.95% at 0.5 bar). A new vial was immediatelyavailable as the vials were individually conditioned by theprogrammable autosampler. The injector was continuously purgedwith nitrogen except during measurement.

Solid-phase micro-extraction headspace sampling (SPME)

Pseudo-dynamic headspace sampling was performed with a solid-phase micro-extraction device (SPME) (from Supelco, USA). TheSPME fibre used was made of three polymers: divinylbenzene,carboxen and polydimethylsiloxane (StableFlex 50/30 mm DVB/CAR/PDMS). The vials were individually pre-conditioned at 90 �Cand 500 rpm agitation. After 2 min the agitation was reduced toenable the introduction of the needle and the SPME fibre wasexposed to the headspace for 30 min while keeping the sametemperature. Thereafter the SPME fibre was introduced into theinjector (injector temperature=190 �C). Three scanning cycles from

Table 1 Data for honey samples

Sample Symbol Production year Total Prod.a SHSb INDEXc SPMEd Typicaluniflorale

1998 1999 2000 2001 other

Acacia A 1 2 5 4 1 13 10 12 11 10 8Chestnut Ch 2 6 8 7 1 24 13 19 23 18 6Dandelion D 8 10 7 – 1 26 24 18 18 – 9Lime Li 5 5 7 3 2 22 19 16 17 – 3Fir F – 2 9 30 3 44 42 15 43 34 14Rape R 13 4 17 4 – 38 35 20 39 34 10

Total 167 143 100 151 96 50

a Number of honey producersb–d Number of samples (model and test) analysed by: b SHS sampling mode, c INDEX sampling mode, d SPME sampling modee Number of samples certified as typical unifloral by a sensory panel (model samples)

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10 to 160 amu were performed at 0.5 s/amu. The SPME fibre wasthen cleaned for 3 min under a N2 flux by introducing it into thesmall oven provided for this purpose, which was set at 270 �C. Thetotal analysis time per sample was 39 min.

The SPME fibres showed a decrease in absorption capacity andwere used for no more than 50–60 cycles each. Furthermore, a fewfibres were mechanically damaged during service, therefore severalfibres were necessary in order to analyse the whole set of samples.

Inside-needle dynamic extraction sampling (INDEX)

A different pre-concentration technique was tested. The INDEXneedles contain the absorbing polymer phase very much like a fixedbed. The volatile compounds are forced through the needle byrepeated aspiration/ejection motions of the syringe plunger. Thepotential advantage of this system compared to SPME is itsmechanical robustness and the possibility of increasing the amountof absorbing polymer as well as the surface area available foradsorbing volatile compounds.

A three-polymer bed was used: divinylbenzene, carboxen andpolydimethylsiloxane in a proportion 1:1:1 (from LDZ, Marin-Epagnier, Switzerland). The vials were individually pre-condi-tioned at 90 �C and 500 rpm agitation. After 5 min the agitation wasstopped, the needle introduced and 2.5 mL of headspace wassubsequently aspirated and ejected ten times at a speed of 200 mL/s.After this, the INDEX needle (syringe temperature=110 �C) wasintroduced into the injector (injector temperature=200 �C) whereslow motions of aspiration and ejection were performed by thesyringe plunger to help desorption of volatile compounds (ten times150 mL at 150 mL/s). Three scanning cycles from 10 to 160 amuwere performed at 0.5 s/amu. Afterwards, the INDEX needle wascleaned in the injector under N2 flux for another 6 min whileperforming aspiration/ejection motions. The total analysis time persample was 17 min.

Data treatment

The average over the three scanning cycles per injection was takenas the intensity of each ionic mass. The raw data were normalisedagainst the ionic mass 40 (Ar), taken as an internal reference on theassumption that all measured 40-amu ionic fragments come fromAr in air and that the composition of air remains constant. Thisnormalisation is especially useful in the case of the SHS samplingmode due to the relatively large amount of air injected along withthe sample. The normalisation against 40 amu was also performedin a few other cases and was specified each time.

Principal components (PCA) and discriminating factor (DFA)analyses were performed with the software provided with theelectronic nose. Classification models were developed based on theset of typical unifloral samples defined by the sensory panel. Everyreplicate was attributed to a given group according to the shortestpossible Euclidean distance.

The classification models were then applied to the test group(less-typical and non-typical unifloral honeys as defined by thesensory panel). Euclidean distances were also used to define theattribution of every replicate of the test group to the differentgroups. It should be kept in mind that a PCA analysis shows, in areduced space, the structure of the data depending on its internalvariance, i.e. without any preconception of the data structure,whereas in DFA analysis, samples are first defined as belonging togiven groups and then the program looks for differences betweenthese groups. In other words, a DFA analysis searches for thosevariables which better discriminate the predefined groups. Al-though DFA analysis usually gives better classifications careshould be taken to avoid data over-fitting.

Results and discussion

Response of the electronic nose to the different typesof honey

As expected from the literature the global mass spectra ofthe different unifloral honeys are not strikingly different(Fig. 1). This shows that most of the volatile compoundsare present in all types of honey, but in different ratios.

Extraction of volatiles using the different sampling modes

Figure 2 shows the mass spectra of the same rape honeysample performed by the SHS, SPME and INDEXsampling modes. These raw data show clearly that theSPME device is able to extract volatile compounds at anincreased concentration and, perhaps more importantly, itextracts heavier volatiles than the other two methods (m/z>110 amu). A further observation is that the patterns ofionic masses below 110 amu from SHS show greaterdifferences than the patterns obtained with the other twomethods (some similarity exists between SPME andINDEX patterns), showing that the presence of theabsorbing polymers in SPME and INDEX devicesinfluences the composition of the headspace, mainly interms of relative concentration of the volatile compounds.

INDEX shows a lower extraction capacity than SPME,probably due to the shorter vial conditioning time(leading to an insufficient evolution of volatiles, theevolution of volatiles being a function of temperature andtime) and to the smaller amount of polymer contained inthe syringe. A better-controlled production technology for

Fig. 1a,b Mass spectra of two unifloral honeys sampled under thesame conditions with a SPME device (y-axis is in log scale). aTypical unifloral rape honey. b Typical unifloral lime honey

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this kind of syringe should improve this type of analysis.Nevertheless, the extraction of volatile compounds byINDEX is somewhat better than that performed by SHSespecially with regard to heavier compounds (not shown).

Classification of honey by botanical originwith a SHS sampling mode

A PCA performed on the model data (typical unifloralsamples), analysed with a SHS sampling mode, showsthree distinct groups: lime, dandelion and a third onewhere the rest of the unifloral honeys are groupedtogether (Fig. 3). The model SHS–PCA shows a 100%correct classification rate based on ionic masses 51, 117and 132 amu and performed on data normalised to 40 amu(Table 2).

Clearly the signals generated by lime and dandelionunifloral honeys are too strong as compared to those

Fig. 3 Electronic nose analysis with a SHS sampling device. ThePCA performed on the model set of samples, typical unifloralhoneys, shows 100% correct classification rate based on ionicmasses 51, 117 and 132 amu and with data normalised to 40 amu.Li=lime. D=dandelion, others=the group formed by acacia, chest-nut, rape and fir together

Fig. 2 Mass spectra, raw data, of the same rape honey sampleperformed using the three sampling modes SHS, INDEX andSPME (y-axis is in linear scale)

Table 2 PCA and DFA classification parameters as obtained with the model set of samples (typical unifloral honeys)

Model samplegroups

Samplingmode

aCCR (%) by typeof stat. analysis

Discriminant variables(ionic masses)

Corresponding variable coefficients(for PCA 1 and DFA 1, respectively)

Dandelion, limeand others (A, Ch,F and R)

SHS PCA 100 51, 117, 132 0.1001, �0.9978, �0.9984DFA 100 �0.899, 0.9851, 0.9948

INDEX PCA 99.30 68, 117 0.7583, 0.7583DFA 99.30 0.9648, �0.1153

Acacia, chestnut,fir and rape

SHS PCA 93.85 44, 47, 54, 58, 94 0.3232, �0.8885, �0.9329, �0.0014, �0.9118DFA 92.31 0.6681, �0.6441, �0.7222, 0.6107, �0.5437

INDEX PCA 90.00 48, 57, 58, 64, 74, 94, 104 0.7939, 0.8675, 0.0918, 0.8890, 0.8707,0.9187, 0.6362

DFA 97.78 0.8687, 0.6483, �0.3794, 0.8705, 0.4149,0.7356, �0.0040

SPME PCA 97.56 59, 76, 94, 104, 144, 145, 157 0.2960, 0.9045, 0.6125, 0.9310, 0.8626,0.6898, 0.7906

DFA 97.56 0.2957, �0.0587, 0.3189, �0.4786, �0.6094,�0.8169, 0.2304

a Correct classification rate, in percentage

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generated by the rest of the samples. By omitting lime anddandelion unifloral honeys from the group of modelsamples, the internal structure of this weaker-signal groupshould be more visible. As expected, the PCA and DFAperformed on acacia, chestnut, rape and fir unifloralhoneys show 93.85% and 92.31% correct classificationrates, respectively (Fig. 4). Both SHS–PCA and SHS–DFA are based on ionic masses 44, 47, 54, 58 and 94 amu(Table 2), showing that the classification of this group bySHS is mainly based on smaller ionic fragments ascompared to the classification where lime and dandelionhoneys are included. The analysis performed here by theelectronic nose with a SHS sampling mode fails to give acomplete separation between chestnut and fir unifloralhoneys (Fig. 4 and Table 3). However, by taking thesetwo groups (chestnut and fir honeys) separately a 100%correct separation between them is obtained with a SHS–PCA based on ionic masses 58, 71, 103 and 104 amu (notshown). The application of these models to the test groupof honey samples is shown in Table 4.

Classification of honey by botanical originwith an INDEX sampling mode

In the case of the INDEX sampling mode, a similarsituation as with SHS is encountered. An INDEX–PCAover the whole group of typical unifloral samples gives adistribution into three groups: lime, dandelion and the restof the unifloral honeys (Fig. 5). This INDEX–PCA showsa 99.30% correct classification rate based on 68 and117 amu and performed over data normalised to 40 amu(Tables 2 and 3). Again a heavy ionic fragment and alighter one are necessary for this classification.

As in the case of the SHS sampling mode, the group oftypical unifloral samples was reduced to acacia, chestnut,fir and rape honeys. Analysis of the reduced group gives

Fig. 4a,b Electronic nose analysis with a SHS sampling device.The classifications are performed on the model set of samplesincluding acacia, chestnut, fir and rape honeys only. The PCA andDFA are based on ionic masses 44, 47, 54, 58 and 94 amu usingdata normalised to 40 amu and showing 93.85 and 92.31% correctclassification rates, respectively. a PCA. b DFA. A=acacia(squares), Ch=chestnut (circles), R=rape (diamonds), F=fir (cross-es)

Table 3 Model PCA and DFA analyses performed on typical unifloral honey samples, including acacia, chestnut, fir and rape honeys. Theclassification of every replicate into a given group is determined by Euclidean distances

Sampling mode Model samples(replicates)

CA DFA

A Ch F R CCR (%)a A Ch F R CCR (%)a

SHS A 18 0 0 0 100 18 0 0 0 100Ch 0 17 1 0 94 0 17 1 0 94F 0 3 15 0 83 0 4 14 0 78R 0 0 0 12 100 0 0 0 12 100

Total 94 92

INDEX A 14 1 0 0 93 15 0 0 0 100Ch 3 8 1 0 67 1 10 1 0 83F 4 0 32 0 89 0 0 36 0 100R 0 0 0 26 100 0 0 0 26 100

Total 90 98

SPME A 15 0 2 0 88 15 0 2 0 88Ch 0 11 0 0 100 0 11 0 0 100F 0 0 30 0 100 0 0 30 0 100R 0 0 0 24 100 0 0 0 24 100

Total 98 98

a Correct classification rate, in percentage

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an INDEX–PCA and an INDEX–DFA (Fig. 6), presenting90.00% and 97.78% correct classification rates, respec-tively, based on ionic fragments 48, 57, 58, 64, 74, 94 and104 amu (Table 2). These classifications were performedon raw data. In this case a few heavier fragments arepresent showing the increased ability of the INDEXdevice to extract heavier compounds than SHS. Theapplication of these models to the test group of honeysamples is shown in Table 4.

Classification of honey by botanical originwith a SPME sampling mode

The analysis performed with a SPME device wasrestricted to acacia, chestnut, rape and fir honey samplesbecause of the longer analysis time of this technique andbecause lime and dandelion honey samples may be easilydiscriminated by SHS, a faster technique. The SPME–PCA (Fig. 7a) and SPME–DFA (Fig. 8a) obtained withthis restricted group of typical unifloral samples both givea 97.56% correct classification rate. These analyses werebased on ionic masses 59, 76, 94, 104, 144, 145 and157 amu (Tables 2 and 3), and were performed on datanormalised to 40 amu. Only one replicate of two different

acacia samples was wrongly assigned by the program tothe fir honey group, probably due to the use of Euclideandistances. Indeed, Euclidean distances are calculatedbased on spherically shaped groups, whereas in thepresent case the acacia and fir honey groups showelongated forms (Fig. 7). Other types of determinationsexist, for instance Mahalanobis distances, that take intoaccount the actual shape of the group.

Table 4 Classification of test samples, including acacia, chestnut, fir and rape honeys, using the model PCA obtained previously with thetypical unifloral honey samples. The classification of every replicate into a given group is determined by Euclidean distances

Test samples(replicates)

SHS INDEX SPME

A Ch F R CCR (%)a A Ch F R CCR (%)a A Ch F R CCR (%)a

A 21 0 0 0 100 17 4 0 0 81 10 0 0 0 100Ch 3 22 14 0 56 18 29 0 0 62 0 29 0 1 97F 11 0 12 3 46 10 2 66 3 81 0 0 37 6 86R 1 0 0 44 98 5 5 1 63 85 1 0 2 63 95

Total 76 78 93

a Correct classification rate, in percentage

Fig. 5 Electronic nose analysis with an INDEX sampling device.The PCA performed on the model set of samples, typical unifloralhoneys, shows 99.30% correct classification rate based on ionicmasses 68 and 117 amu and with data normalised to 40 amu.Li=lime, D=dandelion, others=the group formed by acacia, chest-nut, rape and fir together

Fig. 6a,b Electronic nose analysis with an INDEX samplingdevice. The classifications are performed on the model set ofsamples including acacia, chestnut, fir and rape honeys only. ThePCA and DFA are based on ionic masses 48, 57, 58, 64, 74, 94 and104 amu; they are performed on raw data and show 90.00 and97.78% correct classification rates, respectively. a PCA. b DFA.A=acacia (squares), Ch=chestnut (circles), R=rape (diamonds),F=fir (crosses)

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Figures 7b, 8b and Table 4 show the application of themodels SPME–PCA and SPME–DFA to the test samples(less-typical and non-typical honeys). The models ob-tained with the SPME device provide a better classifica-tion for the acacia, chestnut, fir and rape honey samplesthan the models obtained with INDEX or SHS devices.Not only is there no overlapping of the typical unifloralhoneys with SPME (Figs. 7a and 8a), but also the testsamples are better fitted into the different types of honey(Table 4). This is probably due to the use of heavier ionicmasses for the classifications using the SPME device ascompared to the other two methods (Table 2).

Comparison of the classification of test samples carriedout using the electronic nose (with SPME), the sensoryanalysis and the classical method

A comparison of the classifications of the test group ofsamples obtained by three methods is shown in Table 5:

(i) the electronic nose with the SPME sampling mode, (ii)the sensory panel and (iii) the classical method (acombination of percentage of unifloral pollen, electricalconductivity, fructose/glucose ratio and sensory analysis).Test samples having a sensory average >1.67 wereconsidered non-typical (maximum possible sensory aver-age=2). All remaining samples are considered unifloralhoneys. Among these, the ones with the highest sensoryaverages (�1.17 and �1.67) are included in the less-typical sub-group of unifloral samples.

Excellent correspondence is found between the elec-tronic nose and the sensory analysis with respect to theclassification of the test samples into unifloral and non-typical groups, except for the rape honeys. In this lastcase, samples presenting fermented taste are identified bythe electronic nose as non-typical, the rest of theconflicting samples belonging to the less-typical groupby sensory analysis. More differences are found betweenthese two methods and the classical one, which is morerestrictive. The classical method places fewer samples inthe unifloral groups of fir and rape honeys, mainly due toodd values of the electrical conductivity and of fructose/glucose ratio. Nonetheless, these samples are found in thecorresponding less-typical groups by sensory analysis andby electronic nose.

Fig. 7a.b Electronic nose analysis with a SPME sampling device.The classifications are performed on the model set of samplesincluding acacia, chestnut, fir and rape honeys only. The modelPCA is based on ionic masses 59, 76, 94, 104, 144, 145 and157 amu; it is performed on data normalised to 40 amu and shows97.56% correct classification rate. a Model PCA. b Test samplesapplied to the model PCA. The ellipses surrounding each unifloralhoney group contain all samples (model and test samples)recognised by the electronic nose as belonging to the group.Typical unifloral honey samples are in dark/filled-in signals. Thetest samples are in light/open signals. A=acacia (squares),Ch=chestnut (circles), R=rape (diamonds), F=Fir (crosses)

Fig. 8a,b Electronic nose analysis with a SPME sampling device(same conditions as in Fig. 7). a Model DFA. b Test samplesapplied to the model DFA

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Conclusion

The case studied here dealt with natural products. Thismeans that no reference 100% pure unifloral honeys wereavailable to build model PCA and DFA classifications.This was confirmed by the high standard deviation valuesobtained by sensory analysis as well as by the physico-chemical and pollen analyses. In this situation, limits forthe different classification groups (typical, less-typical,non-typical honeys) were a compromise, especially withregard to the less-typical group.

The electronic nose, using the SPME device, presented98% correct classification of the model samples both withPCA and DFA analyses. Furthermore, a good correlationwas found between sensory analysis and the electronicnose for the classification of the test samples. Finally, agood correspondence was found between classical anal-yses and the electronic nose despite the fact that the PCAand DFA models were built on samples which wereunlikely to be 100% unifloral. When considering sampleswith other types of impurities, as well as honeys with adifferent botanical origin than those included here, these

models may not apply, as any chemometric analysis isrealistic only if the models are built on a set of samplescovering all probable intra-group variations. However, thelarge number of samples used in this study support itsrobustness.

In other words, the electronic nose has proved to havethe capacity to perform the classification of unknownhoney samples by their botanical origin, in a fast andreliable way, keeping in mind that the discriminationsperformed reflect the variance intra- and inter-group ofthe model samples. Some particularities were observed;the electronic nose seems to sort out some weak aroma-defective samples, e.g. fermented, which are also detectedby sensory analysis but not by the classical method.Finally, the electronic nose (as well as the sensoryanalysis) does not seem to recognise samples withinadequate values of electrical conductivity, this beingthe main reason for the discrepancy from classicalmethods.

During a practical application of the system the fewsamples showing a low confidence index could berepeated or further analysed with classical techniques

Table 5 Classification of the test samples performed by three different methods: sensory analysis, electronic nose with SPME samplingmode and a classical method

Honeytype

Number oftest samples

Classificationgroups

Electronicnose (EN)[%]a

Sensoryanalysis (SA)[%]b

Classicalmethod (CM)[%]c

Comments

A 4 Unifloral 100 100 100Non-typical 0 0 0

Ch 12 Unifloral 100 100 100 All these samples have additional tastesand some have suspect lower values forthe electrical conductivity

Unifloral(less-typical)

(42) (25)d

Non-typical 0 0 0

F 20 Unifloral 95 95 70Unifloral(less-typical)

(45) (50)e 30% intersection between EN and SA

Non-typical 5f 5 30 25% of the 30% by CM are non-typicaldue to too low values for the electricalconductivity. They belong to the less-typical group by EN and SA

R 25 Unifloral 84 92 88Unifloral(less-typical)

(28) (36)e 20% intersection between EN and SA.Some of these samples show suspect highvalues for the electrical conductivity orthe fructose/glucose ratio

Non-typical 16 8f 12 4% intersection between EN, SA andCM. 8% and 4% of the 12% by CM are inthe less-typical group by SA and EN,respectively. The difference between ENand SA are in the less-typical group bySA; of these, 4% present a fermentedtaste

a The electronic nose classifies samples into the unifloral groups via Euclidean distances; from these, the samples presenting lowconfidence indexes are also placed in the less-typical groupsb Classification by the sensory analysis is made depending on the sensory -average as follows: non-typical >1.67; unifloral � 1.67; andunifloral (less-typical) � 1.67 and � 1.17c The classical method is a combination of electrical conductivity, fructose / glucose ratio, %percentage of unifloral pollen and sensoryanalysis; in this case no less-typical group was definedd Sensory average � 1.33 € 0.75e Sensory average � 1.17 € 0.90f Sensory average � 1.83 € 0.37

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such as chemical, pollen or sensory analysis in order toconfirm their classification. Lime and dandelion honeyscan be easily discriminated from the other types of honeyby the faster SHS sampling mode. A second step using theSPME sampling mode can further discriminate betweenacacia, chestnut, fir and rape honeys. Although not testedhere, a single analysis with SPME should be able toclassify all samples using two successive chemometricmodels.

Heavier ionic masses were selected as discriminatingvariables when the analysis was carried out with theSPME device as compared with INDEX and SHSanalyses, probably having a direct impact on the qualityof the classifications. Although SPME showed the bestclassifications, the INDEX device presented promisingfeatures, i.e. capability of extracting heavier compounds,versatility concerning the kind and the amount of polymerused as extracting material and mechanical robustness.

Acknowledgements This work was supported by a grant from theSwiss Federal Commission for Technology and Innovation (CTIproject 4614.1). It was undertaken with honey samples kindlyprovided by the Swiss beekeepers. The authors are also grateful toDr. T. Zesiger for invaluable discussions and to Dr. Urbach forlanguage improvements in the text.

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