comparative study between gas sensors arrays device, sensory evaluation and gc/ms analysis

Sensors and Actuators B 103 (2004) 55–68

Comparative study between gas sensors arrays device, sensory evaluationand GC/MS analysis for QC in automotive industry

S. Garriguesa,∗, T. Taloua,1, D. Nesaba Laboratoire de Chimie Agro-Industrielle, ENSIACET, 118 route de Narbonne, 31077 Toulouse Cedex 4, France

b RENAULT DIMat Sce 64160 API, TCR LAB 2 52, 1 av. du Golf, 78288 Guyancourt Cedex, France

Available online 22 July 2004

Abstract

Odours of new cars are important today for the consumers’ comfort. Due to the interior trim parts’ manufacturing process and to theirpetrochemical compounds based, many rubbers and foams used in automotive materials result in a “new car odour” mostly enjoyed butsometimes felt as unpleasant by customers. The use of olfactory sensory panels, especially trained to describe odours, is only one way ofaddressing that issue. Although they offer interesting characterisation methods, olfactory sensory panels may also have some drawbacks.

The odours of several PVC skins were described by human assessors and the corresponding volatile organic compounds were alsocharacterised by a commercially available Electronic Nose technique, based on QMB sensors, whereas their identification was carried outby GC/MS analysis.

Finally, a confrontation of the three types of obtained data was performed and showed the existence of correlation as well as the interestof gas sensors device for automotive industry.© 2004 Elsevier B.V. All rights reserved.

Keywords: Electronic Nose; Chemical sensor; Automotive; PVC skin

1. Introduction

Since a few years, “Electronic Nose” technology has ap-peared in a new field of application: the car industry whichis confronted with a lot of polymer-based elements that con-tribute to the “new car odour”. Different types of chemicalgas sensors have been already tested on this kind of mate-rials: a first generation ones like MOS, MOSFET and CPsensors[1], as well as a second generation ones like quartzcrystal microbalance (QMB) and MS-based chemosensors[2].

Quartz crystal microbalance gas sensors is the chosentechnology for our study. They present some advantagesupon the above mentioned devices, as their smaller size.

Multiple gas sensors devices, abusively qualified “Elec-tronic Noses”, have long been supposed to react like artifi-cial olfactory systems being able to mimicking the olfactoryreceptors mechanisms of human nose, with nevertheless alower selectivity.

∗ Corresponding author.E-mail addresses: [email protected] (S. Garrigues), [email protected](T. Talou).

1 Tel.: +33 5 62 88 57 24; fax:+33 5 62 88 57 30.

However, detection mechanisms of “Electronic Noses” arestill difficult to predict. And the recurrent question remains,is to say: “Does gas sensors technology correctly transcribethe olfactory sensations perceived by human nose?”

To answer this question, a sensory analysis of differentPVC skins was performed by trained panel members usingthe olfactory referential “The Field of Odours®” developedby Jaubert et al.[3].

The present paper aims to evaluate the relevance of thediscriminations between PVC skins obtained using gas sen-sors system. In this way, relationships between ElectronicNose measurements and sensory attributes were establishedand discussed according to chemical characterisation. Theexperimental procedure and the results obtained are reportedbelow.

2. Experimental procedure

2.1. Materials and sampling

Volatile organic compounds (VOCS) emitted from threePVC skins samples, also called coated plastic tissues, werestudied by means of an “Electronic Nose” (QMB gas sen-sors), olfactory sensory analysis and GC/MS analysis. These

0925-4005/$ – see front matter © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.snb.2004.04.121

56 S. Garrigues et al. / Sensors and Actuators B 103 (2004) 55–68

Table 1PVC skin sample description

Code Characteristics

G Charcoal grey polymer coating on a white tissueV Multicoloured polymer coating on a white tissueVO Multicoloured polymer coating on a white tissue

Strong onion odour due to a migration product of theinjected polyurethane foam

polymer materials are issued from different manufacturingprocesses and labelled: G, V and VO. Their characteristicsare described inTable 1.

The PVC pieces were stored under inert atmosphere usingnitrogen gas, in individual specific plastic bags (VALSEMS156 films), at+4◦C refrigerated until required analysis.They were cut up and sampled in the form of 12 mm circles.

2.2. Static headspace optimisation

Experimental design methodology was used in orderto optimise static headspace analysis performance on thethree polymer materials in a classic GC/MS version (HP6890/HP 5973 mass detector—Agilent Technologies).This instrument was coupled with a headspace sampler(HP 7694—Agilent Technologies). The column (DB-5MS(J&W), 30 m× 0.25 mm i.d., 0.25�m d.f.) was programmedfrom 40 to 290◦C at 5◦C/min. The final temperature wasmaintained during 5 min. Injection port and detector tem-peratures were 150 and 290◦C, respectively. The differentplastic samples were introduced in 22 mL vials and analysedas mentioned above.

The studied sampling parameters were: temperature (from30 to 131◦C), equilibrium time (from 120 to 960 min) andratio sample volume/gas volume equalling to occupy a par-ticular height in the vial (from 0.32 to 3.68 cm). Recordedresponses were both total peaks area and number of peaks.A central composite design was set up (Table 2), based on23 factorial design, six star points and six replicates at thecentre of the domain (20 experiments).

Data were processed by using UNSCRAMBLER Version7.5 (CAMO ASA, Oslo) software allowing the drawing ofthe isoresponses curves.

2.3. “Electronic Nose” analysis

The VOCmeter device (AppliedSensors GmbH, Reutlin-gen) was employed to analyse the volatile organic com-pounds emitted from each PVC skin batch. It consists of achemosensor array based on quartz crystals microbalance.The headspace generation was permitted by coupling a HS40 XL sampler (Perkin-Elmer) to the VOCmeter system, andrealised under optimised conditions. Height sensor elementsare integrated on a common quartz substrate. The transduceris a thin quartz plate with gold electrodes attached to thefront and back surfaces. Using electrically conductive glue,the plate is fixed to the two contacts which form the sensor

socket. Applying ac voltage with the quartz fundamental fre-quency will force the transducer to oscillate (piezoelectriceffect). Different polymers or supramolecular compoundsare airbrushed to either side of the quartz plate. These mate-rials show specific sorption properties that can be tailored byintroducing polar, non-polar, chiral, etc., functional groups.When analyte molecules from the gas phase hit the sensor,absorption may take place on the surface as well as in thebulk of the sensitive coating. This temporary and reversiblemass change will in turn lead to a “detuning” of the quartzresonator, resulting in a change of the vibrational frequency[4,5]. The sensors are built into a temperature-controlledmeasuring chamber. Each oscillator has a basic frequency inthe range of 10 MHz and the resolution of the sensor signalis ±1 Hz.

The sequence of the analysis was: sample acquisition time60 s; recovery time 540 s. To test the reproducibility of theanalysis, 10 coated plastic tissues were measured; and thenaveraged for data treatment. The order of the analysis wasbalanced for plastic samples[6] with the aim to minimisethe observation of memory effect of the gas sensors.

The “Electronic Nose” data were collected and stored bythe Argus Version 1.15 software (AppliedSensors GmbH,Reutlingen).

2.4. Sensory analysis

Olfactory characterisation of materials is one of the fieldswhere exists a high need of common tools and methodsbecause descriptions with only individual evocations, evenwithout any hedonic reference, are very quickly limited.

Such descriptions with evocations can create misunder-standings even in a specialists group, without speaking ofimpossibilities to communicate with other persons who donot belong to such a group.

That is why several reference systems[7] have been de-veloped in the past, even if the existence of an olfactory stan-dard observer can still be seen as a questionable assumption.

The olfactory sensory analysis was performed by anin-house expert panel composed of 14 assessors from RE-NAULT company, on the basis of the olfactory referential“The Field of Odours®”. It has allowed to qualify and char-acterise the odours emitted from the two kinds of polymermaterials.

That system of references has been obtained from sta-tistical tests on a great number of persons, in a way whichcould be compared to the establishment of colour referencesystems, for applications in the field of aroma, fragrancesand perfumery at its beginning. It organises or marks out theolfactory space by using six main poles: amine, hesperidic,terpenic, sulphured, pyrogeneous and sweet, with threedominant characteristics: fruity, fatty and woody. Forty-fivereference molecules (descriptors), diluted in alcohol, can beseen as the alphabet used to analyse and describe odours. It isshown inFig. 1and can also be used to describe the interiorair of new static cars, in addition to interior trim materials.

S. Garrigues et al. / Sensors and Actuators B 103 (2004) 55–68 57

Table 2Central composite design matrix—experimental and coded values

Condition Test order Equilibrium time Temperature Height

TEP PP Real values (min) Coded values Real values (◦C) Coded values Real values (cm) Coded values

Factorial design points1 7 4 290 −1 50 −1 1 −12 11 16 790 +1 50 −1 1 −13 12 11 290 −1 110 +1 1 −14 14 17 790 +1 110 +1 1 −15 3 6 290 −1 50 −1 3 +16 16 13 790 +1 50 −1 3 +17 5 8 290 −1 110 +1 3 +18 1 12 790 +1 110 +1 3 +1

Star points9 13 3 120 −� 80 0 2 0

10 18 1 960 +� 80 0 2 011 19 19 540 0 30 −� 2 012 20 7 540 0 131 +� 2 013 17 15 540 0 80 0 0.32 −�

14 2 20 540 0 80 0 3.68 +�

Central points15 6 14 540 0 80 0 2 015 15 9 540 0 80 0 2 015 10 10 540 0 80 0 2 015 4 5 540 0 80 0 2 015 9 2 540 0 80 0 2 015 8 18 540 0 80 0 2 0

Fig. 1. The projected space of the olfactory space according to the “Field of Odours®”.


The followed method, here, is based on the D49 3001[8] test method carried out by RENAULT. So, a 60 mL darkglass vial is filled with 0.6± 0.06 g of polymer sample and itis capped with a metallic screwed stopper. Prior to that, eachvial is washed with demineralized water and ethanol. Then,samples are heated at 80± 2◦C during 2 h± 5 min. Thevials are let cool down at ambient temperature, 1 h beforethe sensory evaluation.

The description of odours obtained with the olfactory ref-erential “The Field of Odours®” is a qualitative one (pres-ence or absence of the 45 descriptors) but also a qualitativeone. An overall olfactory intensity value for each sensoryterm. These values are then gathered for each pole.

Fig. 2. An example of “response” of the VOCmeter device to the headspace emanating of the PVC skin “G” sample.

A six intensity levels scale is used to evaluate the per-ceived odour, and can be translated as follows:

0 no odour;1 weak odour requiring such an attention that it is difficult

to name;2 perceived by simple sniffing without any other informa-

tion;3 perceived even if subject’s attention is paid to anything

else;4 powerful odour taking up subject’s attention;5 overwhelming odour.

More details can be encountered in[9].


2.5. GC/MS analysis

Same instrumentation and same programming than theones used for static headspace optimisation were employed.Volatiles generation was performed under optimised condi-tions. The flow rate of the carrier gas (helium) was maintainat 1.5 mL/min. A solvent delay of 2 min was applied beforeGC profile recording. Mass spectra were obtained by EI ion-isation at 70 eV over the range of 35–550 amu. The GC/MSapparatus is equiped with the HP ChemStations (AgilentTechnologies) which permit GC profiles and mass spectraacquisitions as data analysis.

Each chromatographic peak was identified by comparingits mass spectrum with the NIST library (Version 1.6d, com-prising 100 000 spectra).

GC/MS analyses of each PVC skin were carried out intriplicate and replicate peaks were averaged.

2.6. Data processing

The signals generated by the VOCmeter device, consec-utively to the headspace injection of a coated plastic tis-sue sample, are reported inFig. 2. We were interested inthe information brought by the “sig-Base3” parameter de-fined as the maximum amplitude, positive or negative, of the“response” curve after substracting the baseline, and aver-aged on three adjacent values. First value, at the beginningof the signal acquisition, constitutes the baseline value.

Then, “Electronic Nose” data were normalised to high-light remarkable values. Values are remarkable when theyare singularly distinguished from the others and correspondthen to the greatest centre-reduced values of the matrix.“Electronic Nose” data were analysed by principal com-ponent analysis (PCA), using SPAD Version 3.5 (CISIA,FR) to investigate the differences between polymer ma-terials revealed by the VOCmeter system and to discussthe differences between the mechanisms of reaction of thesensors.

Olfactory sensory analysis data were analysed by princi-pal component analysis in order to reduce the descriptivedata dimensionality; and have been used for interpreting andpresenting the results. SPAD Version 3.5 (CISIA, FR) soft-ware was employed for that purpose. The olfactory profileof each PVC skin sample was also plotted from averagedscores attributed to each descriptor listed by the whole pan-elists. ANOVA was carried out on raw data to determinethe descriptive terms that discriminated best between coatedplastic tissues (P < 0.05). This analysis was carried out us-ing SPAD Version 3.5 (CISIA, FR) and allowed to determinethe reproducibility of the three replicate assessments.

GC/MS data were also analysed by ANOVA, using SPADVersion 3.5 (CISIA, FR), to determine the volatile com-pounds that discriminated best between PVC skin samples(P < 0.05). GC/MS selected data were then analysed byPCA to enable the differences between plastic materials tobe determined according to identified volatile compounds.

Relationships between data sets were investigated by mul-tiple factorial analysis (MFA)[10], using SPAD Version 3.5(CISIA, FR). The MFA was adapted to the processing of awhole of individuals described by different groups of vari-ables. It consisted of a realisation of a PCA on the wholeof the variables (all groups included), which were weightedto balance the influence of the groups on the total analy-sis carried out. This MFA made possible to consider thecommon character of the various groups and to discrimi-nate the same individuals while bringing the same weightto each group. To investigate relationships between “Elec-tronic Nose” and sensory measurements discussed accord-ing to volatile compounds, their data set was considered asactivated variables, while GC/MS data were considered asillustrative ones.

3. Results and discussion

The present work was carried out not only to investigatethe relationships between the gas sensors array measure-ments and the expression of perceived odours, but also torelate these relationships to the volatile compounds releasedby the PVC skin samples studied by GC/MS. The threecoated plastic tissues (G, V and VO) were analysed, un-der optimised headspace generation conditions, by the threemethods whose results were separately discussed before es-tablishing tentatively their relationships.

3.1. Static headspace optimisation

Experimental design responses obtained are reported inTable 3. The model equation resulting from this, has permit-ted the drawing of isoresponse curves. An example is pre-sented inFig. 3regarding total area responses achieved fromPVC skin. It is showed that, in the experimental domain stud-ied, increasing sample volume and equilibration time haveno effect upon responses contrary to temperature. As ex-pected, temperature seems to be the most significant parame-ter. A good reproducibility is observed. However, a technicalconstraint regarding exposure temperature of the differentautomotive trim materials was set by industrialists at 80◦Cin order to be more relevant to realistic conditions. Thischoice was reinforced by a study lead by Volkswagen[11] inArizona, on vehicles parked in the sunshine, into passengercompartment where temperatures around 80◦C have beenrecorded. Isosresponse curves have allowed to determinethe optima of the main parameters governing the headspacegeneration in the studied experimental field: one-third ofthe vial is filled by sample and heated at 80◦C during540 min.

Theoretical model was successfully validate owing to thefact theoretical and experimental values are very closed(Table 4).

Furthermore, the achievement of the thermodynamicequilibrium showed itself verified by the study of a kinetic


Fig. 3. Isoresponse curves from PVC skin experimental design obtained by UNSCRAMBLER software. The studied parameters are duration (A),temperature (B) and sample volume (C).


Table 3The 20 experiments carried out and the corresponding values of the recorded responses (total peaks area and number of peaks) for PVC skins

Conditions Duration (min) Temperature (◦C) Height (cm) Total peaks area Number of peaks

Factorial design points1 290 50 1 0.861E+8 82 790 50 1 0.975E+8 93 290 110 1 9.300E+8 134 790 110 1 8.794eE+8 125 290 50 3 1.269E+8 86 790 50 3 1.633E+8 87 290 110 3 14.373E+8 138 790 110 3 14.895E+8 12

Star points9 120 80 2 4.370E+8 10

10 960 80 2 4.901E+8 1111 540 30 2 0.319E+8 412 540 131 2 19.881E+8 1513 540 80 0.32 4.673E+8 1114 540 80 3.68 4.680E+8 11

Central points15 540 80 2 6.036E+8 1115 540 80 2 5.230E+8 1115 540 80 2 5.633E+8 1115 540 80 2 5.360E+8 1115 540 80 2 5.591E+8 1115 540 80 2 5.636E+8 11

follow-up. The results were presented and explained in[GARRIGUES, 2002][2].

3.2. “Electronic Nose” analysis

The PCA plotted by using the eight QMB gas sensors re-sponses (Fig. 4a) gives a good discrimination between thethree coated plastic tissues samples. The first plan of thePCA shows 99.51% of the total explained variance. How-ever, samples distribution is very mono-dimensional (PC 1:98.65%). The first principal component (PC 1) allows todistinguish the PVC skin materials according to their fab-rication process, whereas the second one (PC 2) permits todiscriminate between “V” and “VO” samples according totheir olfactory perception.

Furthermore, the coated plastic tissue “G” sample ap-pears to be strongly correlated with the eight QMB gassensors which are themselves negatively highly corre-lated with the first dimension (PC 1), as illustrated inFig. 4b.

On the other hand, the quasi-total correlation between theeight QMB gas sensors let us supposed of the similarity ofthe information brought by each ones.

Table 4Experimental design validation

Theoretical values Experimental values

Total peaks area Number of peaks Total peaks area Number of peaks

PVC skin 5.582E+8 11 5.581E+8 11

3.3. Sensory analysis

Among the 45 odourous notes constituting the olfactoryreferential “The Field of Odours®”, 13 descriptors were em-ployed. Each assessor has been also asked to evaluate theoverall intensity perceived for the three coated plastic tissuesconsidered.

The olfactory differences existing between these polymermaterials were pointed out by plotting, respectively, their ol-factory profile (Fig. 5). As expected, the odour emanatingfrom the “VO” sample appears to be stronger, if we have alook at the “overall intensity” descriptor, than the one per-ceived for coated plastic tissues “G” and “V”. This, mainlyresults from the considerable perception of the odourousnote “sulphured” related to “VO” sample evaluation.

Then, a variance analysis was processed on these 14sensory attributes (with nonaveraged data) to appreciateeach descriptor reproducibility. It permits to measure eachodourous note ability for differentiating at least one PVCskin among the three ones. Results are presented inTable 5.

As indicated in bold and italic print, only three sensoryattributes show a probability below 0.05 (P < 0.05), is to say“sulphured”, “phenoled” and “overall intensity” descriptors.


Fig. 4. (a) TEP samples projections and (b) eight QMB gas sensors contribution on the first plan of the PCA.

So, only those significant sensory terms were taken intoaccount to study the distribution and the characterisation ofcoated plastic tissue samples into the space that they define.

The three PVC skin samples are clearly discriminated bythe three previous selected descriptors, as shown in PCAgraphical representation (Fig. 6). The two multicolouredcoated plastic tissues (“V” and “VO”) are distinguished fromthe charcoal grey one (“G”) according to the second dimen-

sion (CP 2: 8.89% of the explained variance), while the firstfactorial axe (CP 1: 91.11% of the explained variance) al-lows to discriminate “good” PVC skin samples (“G” and“V”) from the “bad” malodourous one (“VO”) (this last ap-preciation depends on dictated criteria by specifications).

Regarding the descriptors distribution onto the correlationcircle, “sulphured” and “global intensity” ones appear to bestrongly correlated with themselves (r = 0.97) and charac-


Fig. 5. Olfactory profiles of the three PVC skins, realised with 14 assessors from averaged scores attributed by the whole judges to each sensory descriptorfor each polymer sample.

terise the “VO” sample. The “phenoled” odourous note, asfor it, does not present some correlation with these sensoryterms, and describes more particularly the “V” sample.

Finally, the first principal component (PC 1) seems toreflect a deep olfactory overall intensity scale.

3.4. GC/MS analysis

Thirteen volatile organic compounds emitted by the threePVC skins were detected by GC/MS analysis, consecutively

Table 5Variance analysis—Fisher criteria (F) and probability (P) associated tothe 14 descriptors

Descriptors Fisher (F) Probability (P)

Amine 1.8053 0.1890Sulphured 8.4212 0.0021Roasted 0.3735 0.6928Phenoled 7.4230 0.0036Balsamic 1.0000 0.3847Honeyed 1.0000 0.3847Fatty 0.3994 0.6757Fruity 0.8305 0.4496Ethereal 4.4226 0.2634Esterified 0.8305 0.4496Animal 1.0000 0.3847Earthy 0.0827 0.9209Musty 1.0000 0.3847Overall intensity 13.9305 0.0001

to a static headspace generation. Among these 13 VOCs, onewas only detected for one coated plastic tissue. It translatesthe specific feature of this substance to one kind of PVCskin. Two detected VOCs among the 13 ones could not beidentified formally but their role in the discrimination of thecoated plastic tissue materials was studied.

The identified VOCs belong for the majority to the chem-ical families of the alkanes, alcohols, ketones, aromatic andamides. Some examples of compounds met in the GC pro-files are reported inTable 6, as their elementary statistics.In the four last columns of this table are mentioned min-imum, maximum, mean and standard deviation values ofthe chromatographic peaks corresponding to each chemicalcompounds, all GC profiles merged. The calculated stan-dard deviation represents the variability of the importance

Fig. 6. Averaged PVC skin samples points projections on the first planof the PCA and correlation between the three selected descriptors.


Table 6Volatile organic compounds released in the headspaces of the different PVC skins

Peak number Irexp Compounds G V VO Minimum Maximum Mean S.D.

1 734 Methyl isobutyl ketone 357898 216537.5 217690 183887 368273 264041.8 69436.92 762 Toluene[12] 50807.5 522215.5 603764 47795 633971 392262.3 267738.23 794 N,N,-dimethyl-formamide 993916 2957557 485138.5 481508 3300357 1478870.5 1196330.44 893 Cyclohexanone[13] 486489.5 446238 50367.5 47546 506470 327698.3 216591.75 972 Phenol 267541 59740.5 86341.5 28394 267583 137874.3 107613.56 980 Siloxan derived 42003.5 45213 13305.5 12533 49675 33507.3 15035.37 1026 2-Ethyl 1-hexanol 56208.5 110018 61041 31753 128738 75755.8 34530.98 1103 Ni 1a 20447.5 20329.5 19267.5 18546 21366 20014.8 1047.89 1197 3,4-Dihydro 3-vinyl 1,2-dithiin 39761.5 47751.5 31273.5 13741 48806 39595.5 13530.7

10 1210 2,3,5-Trimethyl benzen-1,4-diol 13310.5 – 64305 – 66859 25871.8 30408.211 1300 Tridecan 20907.5 24342 – – 29019 15083.1 12179.912 1390 Ni 2 – – 37552.5 – 52642 12517.5 21613.113 1436 Quinolin derived 47370 44296 41813 20462 74278 44493 20664.9

Polymer samples values are averaged values of each chromatographic peak area for the two replicates. Elementary statistics of the 13 chemical compoundsare also indicated. The chemical substances are listed according to their coming out order of the chromatographic column.

a Nonidentified.

of the peak considered regarding to the other chromato-graphic peaks of the GC profiles. So, detected VOCs in dif-ferent PVC skins and corresponding to significant peaks onthe chromatograms show high standard deviation values, asfor example theN,N-dimethyl formamide. Peak area of thiscompound varies in a considerable way from a coated plas-tic tissue to the other, this letting us supposed of its signif-icant role in the discrimination of the PVC skins betweenthemselves.

The nonidentified compound—ni 2—as for it, is specificto the “VO” sample and have, consequently, a not inconsid-erable role in coated plastic tissues discrimination.

The “G”, “V” and “VO” samples release, in a generalway, the same volatile substances (12/13 for PVC skins “G”and “VO”; and 11/13 for the “V” one), the only differencelies in their respective concentrations inside each coatedplastic tissue. It is important to notice here that the staticheadspace generation method, more attractive in terms ofeasily implementation and reproducibility, is less sensitivethan other volatile extraction techniques, dynamics thoseones, and does not allow to maintain of the similarity (ordissimilarity) of the samples composition (since only a lowpart of VOCs is extracted, not necessarily representative ofthe plastic sample).

ANOVA was carried out on each chemical compound. A77% of the VOCs were significant (P < 0.05) to discrim-inate at least one PVC skin between the others. Then, toinvestigate how these volatile substances enabled to differ-entiate the coated plastic tissues between themselves, a PCAwas performed on the normalised data set, consisting of 13compounds met in the GC profiles of different PVC skins.The first plan of the PCA, accounting for 78.35% of the ex-plained variance (51.97 and 26.40%, respectively, distributedon the PCs 1 and 2), is shown inFig. 7. GC/MS analysisseparated the coated plastic tissues into three well-definedgroups corresponding to the three kind of PVC skins con-sidered. The first factorial axe (PC 1), bringing just a lit-

tle more of the half of the information provided by the 13variables (chemical compounds), permits to distinguish the“VO” sample, characterised by a strong onion odour, be-tween “G” and “V” samples, exempt from this odour. Theirdistribution, each one at the antipodes of this component,accounts for this observation. The second dimension (PC2) underlines the chemical composition differences existingbetween the charcoal grey coated plastic tissue “G” and themulticoloured ones “V” and “VO”.

Loadings of the VOCs on the PCs 1 and 2 are plotted inFig. 8. So, correlation between the compounds are pointedout there. On this first plan, only few compounds appearstrongly positively (for example, the phenol and the methylisobutyl ketone) or negatively (as the cyclohexanon and the2,3,5-trimethyl benzene-1,4-diol) correlated between them-selves. Such chemical substances provide the same infor-mation about the samples group studied, and discriminatecoated plastic tissues in a similar way. Three main directionsare indicated by the response vectors of the compounds,each of them characterising one class of PVC skin. So, thephenol, the methyl isobutyl ketone and the toluene seem tocharacterise more particularly the coated plastic tissue “G”,whereas theN,N-dimethyl formamide, the tridecane and the2-ethyl 1-hexanol seem to present some correlation with the“V” sample. Finally, the PVC skin “VO” is more particularlycorrelated with the 2,3,5-trimethyl benzene-1,4-diol and theni 2 compounds.

3.5. Relationships between “Electronic Nose” andsensory measurements discussed according to chemicalcharacterisation

Were the results given by gas sensors device relatedto the odours perceived by assessors? And thus, did the“Electronic Nose” effectively allow to “mimic” humannose discriminations perceived between various PVCskins?


Fig. 7. PCA scores on PCs 1 and 2 for three PVC skins (“G”, “V” and “VO”) analysed in two replicates by static headspace/GC/MS.

Fig. 8. PCA loadings for volatile compounds on PCs 1 and 2 for three PVC skins (“G”, “V” and “VO”) analysed in two replicates by staticheadspace/GC/MS.


Fig. 9. Averaged (G, V, VO) and partial (G1, V1, VO1 for Electronic Nose data and G2, V2, VO2 for sensory data) PVC skin samples projections onthe first plan of the MFA.

First of all, to compare measurements of the PVC skinssamples carried out by the three methods of analysis, thereplicates were averaged for each coated plastic tissue withinthe three data sets: “Electronic Nose” (eight gas sensors),sensory analysis (three descriptors) and physicochemicalanalysis (13 compounds).

To investigate these apparent relationships, the choice wasmade on the realisation of a multiple factorial analysis fo-cused on the sensory and “Electronic Nose” data, i.e. byconsidering the sensory and gas sensors array data as acti-vated variables and the GC/MS ones as illustrative variables.This method allowed to compare data sets of various sizesgiving them an identical weight.

A previous comparison of the distribution of the explainedvariance over dimensions of separated principal componentanalysis on each data set (gas sensors and sensory attributes)was carried out. It showed that the information brought byElectronic Nose system is mono-dimensional whereas infor-mation delivered by sensory analysis is shared among twodimensions.

Relationship between chemical QMB gas sensors mea-surements and sensory attributes are shown on the first planof the MFA (Fig. 9). The first plan showed 100% of theexplained variance and firstly discriminated coated plastictissue materials between themselves according to their dif-

ferences of fabrication (PC 1). On the other hand, PC 2 al-lows, also, to distinguish them according to their olfactoryperception, is to say “G” and “V” samples from “VO” mal-odourous sample.

Those results are also explained by the study of the corre-lation between the canonical variables of the two groups andthe MFA factors (Table 7). The first factor is common to thetwo groups but explains more particularly gas sensors dataaspect (RG1/Dim1 = 0.91 whereasRG2/Dim1 = 0.62). Thesecond one mainly expresses sensory data aspect (RG2/Dim2= 0.90).

Furthermore, the graphical representation of both aver-aged (“G”, “V”, “VO”) and partial (“G1”, “G2”, “V1”, “V2”,“VO1”, “VO2”) PVC skin samples on the first plan of theMFA shows a good discrimination between the three poly-mer materials by the two data groups—“Electronic Nose”(G1) and sensory (G2) data (Fig. 9).

Table 7Correlation between canonical variables and MFA factors

Factor 1 2

G1a 0.91 0.42G2b 0.62 0.90

a Gas sensors data.b Sensory data.


Table 8Correlation (R > 0.7) between QMB gas sensors and chemical compounds

Chemical compounds

+ −QMB gas sensors Methyl isobutyl ketone

phenol quinolin derivedToluene 2-ethyl1-hexanol

Exterior location of partial coated plastic tissue samples“V2” and “VO2” on the MFA graph indicates that theseplastic materials are particularly well characterised in a sen-sory point of view, and less specifically by VOCmeter gassensors device, which partial points are situated in a morecentral area on the graph.

At the opposite, the “G” sample was better described byQMB gas sensors analyses (G1).

So, although good differentiation between the three PVCskin samples is obtained whatever the analysis performed,the two types of considered data (“Electronic Nose” and sen-sory data) do not discriminate coated plastic tissues uponsame basis. It is also important to indicate that the differ-ences observed regarding gas sensors measurements and ol-factory sensory ones during the confrontation of the twokinds of analyses, could result from the different experimen-tal conditions applied to each ones, notably in term of sam-ple equilibration time (“Electronic Nose”: 540 min; sensoryanalysis: 120 min).

In order to investigate the relationships between gassensors and sensory descriptors, and then gas sensors andvolatile compounds,Tables 8 and 9gather the chemicalcompounds and the sensory terms presenting a coefficientof correlation with the QMB gas sensors higher thanR >0.7, respectively. Thus,Table 8shows that the eight QMBgas sensors are strongly related to the same chemical sub-stances sets, that indicating that they all react to same com-pounds and so that they provide same information aboutthe samples. They are highly positively correlated with the“methyl isobutyl ketone”, “the phenol”—these chemicalsubstances characterising the PVC skin “G” sample—andwith the “quinolin derived” compound, whereas theypresent negative correlation with the “toluene” and the“2-ethyl 1-hexanol”. Among the chemical compounds themore strongly correlated with sensory descriptors, i.e.R

Table 9Correlation (R > 0.7) between sensory descriptors and chemical com-pounds

Chemical compounds Sensory descriptors

N,N-dimethyl formamide PhenoledSiloxan derived Phenoled2-Ethyl 1-hexanol PhenoledNi 1 Phenoled3,4-Dihydro 3-vinyl 1,2-dithiin Phenoled2,3,5-Trimethyl benzen-1,4-diol Sulphured overall intensityTridecan PhenoledNi 2 Sulphured overall intensity

> 0.7 (Table 9), 5 are characteristics of the coated plastictissues “V” and “VO”. The “V” sample is mainly corre-lated with “phenoled” descriptive term which is associ-ated with “N,N-dimethyl formamide”, “2-ethyl 1-hexanol”and ni 1 chemical compounds. While the “VO” samplepoints out a good correlation with “sulphured” and “globalintensity” sensory descriptors, both associated with the“2,3,5-trimethyl 1,4-benzenediol” and ni 2 chemical sub-stances.

4. Conclusion

Multiple gas sensors devices, abusively qualified “Elec-tronic Noses”, have long been supposed to react like artifi-cial olfactory systems being able to mimicking the olfactoryreceptors mechanisms of human nose, with nevertheless alower selectivity.

However, detection mechanisms of “Electronic Noses”are still difficult to predict. And the recurrent question re-mains, that is to say: “Does gas sensors technology cor-rectly transcribe the olfactory sensations perceived by humannose?”

The present paper has tried to answer this question. Therelevance of the discriminations between three kinds of PVCskins obtained using QMB gas sensors system was eval-uated. In this way, relationships between Electronic Nosemeasurements and sensory analysis data were establishedand discussed according to physico-chemical characterisa-tion of the coated plastic tissue samples.

The main conclusions resulting from this work are listedbelow:

• Controlling headspace generation conditions is proved tobe necessary. Whatever the statistical data processing maybe used, we need to be the nearest as possible of the realolfaction conditions because it does not allow to correctaccess data obtained with a nonhigh-performance “Elec-tronic Nose” instrument, for example.

• All those correlations would deserve to see their“robustness” tested by trying to verify that the moleculesresponsible for discriminations are the same as for thepanel than for the gas sensors devices.

• Taking into account the present experience as well as theonly cases studied until now, a certain carefulness may becalled for, before envisaging the use of “Electronic Nose”for quality control in automotive industry. Multi-gas sen-sors devices are still laboratory instruments.

• Apparatus responses reliability, for an use on a large scale,seems to be strongly linked to the quality of the trainingof the human panel. So, a trained and efficient panel has tobe maintained even if its task can be shared by employinggas sensors instruments.

• The global intensity evaluation constitutes an importantcriteria (taking into accountP ≤ 0.05), but is it the sameregarding “Electronic Nose” system?


• The chemical gas sensors device evaluated in this work,partly transcribed the human perception of the PVC skinsamples. The “Electronic Nose” provided interesting butfew correlation with sensory attributes and identifiedvolatile compounds. These results are encouraging butnow require additional work to obtain stronger correla-tion between multi-gas sensors array system and sensoryanalysis, with the aim to better understand the principlesof reaction of gas sensors.

Finally, regarding the initially asked question, that is to say:“Can “Electronic Noses” technologies take the place of ol-factory sensory analysis?”, two answers can be given:

(1) no, they cannot—because the observed differences arenot based on same compounds;

(2) yes, they can—because the differences are robust due toa good reproducibility of the systems, if they are welldesigned.

Therefore, “Electronic Noses” could be assimilated to a goodpredictive tool.

However, much work needs to be done in the future toimprove this situation.

Acknowledgements

The authors of this paper thank RENAULT for its finan-cial support and wish to gratefully acknowledge most partic-ularly Stéphane COUDERC, as also all the members of theolfactory panel—RENAULT, for their valuable contributionand availability during the work thesis.

References

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Biographies

Sandrine Garrigues obtained her PhD in chemistry, sponsored by RE-NAULT, at the Agro-industrial Chemistry Laboratory at the NationalSchool of Chemistry of Toulouse (INPT-ENSIACET). She obtained herDEA in Agroresources Sciences speciality of the National PolytechnicInstitute of Toulouse (INPT).

Thierry Talou is a Chemical Research Engineer and the Scientific Man-ager of the Aromas and Sensory Metrology group of the Agro-industrialChemistry Laboratory. He completed his DEA in 1984 and his PhDdegrees in aroma chemistry in 1992, all from INPT. His research interestis focused on flavour analysis both with classical techniques and originalones, like multi-gas sensors technology.

Daniel Nesa is in charge of sensory analysis at Renault Materials Engi-neering. He is an engineer from Institut des Sciences de l’ Ingénieur deNancy (ISIN). He obtained a DEA and a PhD in Materials Science andEngineering at Ecole Nationale Supérieure des Mines de Paris (ENSMP)in 1987. After works in the fields of fracture mechanics, composite ma-terials, foundry and acoustical properties of materials, his activities arenow related to sensory analysis (odours and touch) of cars interior trimparts.

comparative study between gas sensors arrays device, sensory evaluation and gc/ms analysis

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