Use of an array of metal oxide sensors coupled with solid phase microextraction for characterisation of wines

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Sensors and Actuators B 132 (2008) 125–133

Use of an array of metal oxide sensors coupled with solid phase microextraction for characterisation of wines Study of the role of the carrier gas S. Villanueva a,1 , A. Guadarrama a,1 , M.L. Rodr´ıguez-Mendez b,∗ , J.A. de Saja a,1 a

Department of Condensed Matter Physics, Sciences Faculty, Prado de la Magdalena s/n, 47011 Valladolid, University of Valladolid, Spain b Department of Inorganic Chemistry, Engineers School, University of Valladolid, Paseo del Cauce s/n, 47011 Valladolid, Spain Received 2 October 2007; received in revised form 8 January 2008; accepted 9 January 2008 Available online 31 January 2008

Abstract This paper investigates the behaviour of metal oxide (MOX)-based sensors exposed to wines in the presence of different gas carrier backgrounds including air (oxygen), inert gases (nitrogen and helium) and mixtures of air/inert gas. The influences of the gas on the resistance of the sensors, and on the intensity, kinetics, repeatability and reproducibility of the responses have been analysed. The sensors exposed to nitrogen or helium show a higher conductivity due to the displacement of the oxygen adsorbed on the surface of the sensors. Signals registered in an oxygen background are faster than those observed in the presence of N2 or He, indicating that it is easier to displace the oxygen than inert gases from the sensor surface. In addition, inert gases cause irreproducibility of the responses, particularly in the case of helium that decreases drastically the lifetime of the sensors. A purposely designed system of MOX connected to solid phase microextraction (SPME) has been developed that makes it possible for mixtures of air/inert gas to reach the sensor chamber instead of the pure inert gas. Using such mixtures, a drastic improvement of the kinetics and the reproducibility of the responses has been attained. The optimised system SPME-MOX has permitted to discriminate wines with similar characteristics such as red wines elaborated with the same variety of grape but aged using different types of oak woods. Moreover, the improvement of the stability and reproducibility of the signals has allowed monitoring the ageing of wines. The system has demonstrated its ability in discriminating and recognising among wines after 3 and 6 months of permanence in oak wood barrels. Both the calibration and the validation values obtained by using a partial least squares (PLS2) regression method indicate a good-quality model performance (slope near 1, offset near 0 and large correlation between sensors and categorised variables). © 2008 Elsevier B.V. All rights reserved. Keywords: E-nose; Array of sensors; MOX; SPME; Wine; Carrier gas

1. Introduction Multisensing systems have attracted considerable attention during the last years [1,2]. These systems (also known as electronic noses), consist of an array of unspecific sensors coupled with pattern-recognition software. Electronic noses have been used to analyse the odours of a variety of foods and beverages [3–5]. In particular, a number of studies have been devoted to the analysis of wines [6–11].

∗ 1

Corresponding author. Tel.: +34 983 423540; fax: +34 983 423310. E-mail address: [email protected] (M.L. Rodr´ıguez-Mendez). Fax: +34 983 423572.

0925-4005/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2008.01.035

The detection of alcoholic beverages with arrays of resistive sensors is a challenging problem due to the notorious sensitivity of resistive gas sensors towards the water and ethanol present in the headspace of these samples [12–16]. Injection techniques such as solid phase microextraction (SPME) have been used to eliminate or at least substantially diminish the presence of water and ethanol in the volatile mixture [17–20]. In these techniques, absorbent resins (with low affinity towards water and ethanol, and high affinity towards other volatiles) are used to collect the headspace of the sample. The volatiles are released afterwards by applying a temperature program to the trap. It has to be noticed that desorption requires the use of an inert carrier gas (such as He or N2 ) to avoid oxidation or combustion of the organic matter during heating. Such inert gases reach the

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sensor chamber forcing the array of sensors to work under inert atmosphere. Few attempts have been made to understand the sensing phenomena that occur when metal oxide (MOX)-based sensors are used in the absence of oxygen. The sort and amount of products related to sensing during the detection of CO and hydrocarbons with tin oxide sensors have been reported [21,22]. The performance of MOX sensors under inert gases in terms of kinetics, reproducibility and lifetime has not been yet evaluated. This paper investigates the behaviour of MOX sensors upon exposure to wines in the presence of different gas carrier backgrounds including air (oxygen), inert gases (nitrogen and helium) and mixtures of air/inert gas. The influences of the gas on the resistance of the sensors, and on the intensity, kinetics, repeatability and reproducibility of the responses have been analysed. In the second part of the work, a purposely designed system of MOX connected to SPME has been developed where volatiles are desorbed under an inert gas. Then, air is injected in the gas line, making it possible for mixtures of air/inert gas to reach the sensor chamber. The optimised system using mixtures of air/nitrogen as a gas carrier has been used to analyse wines elaborated with the same variety of grape but aged using different types of oak woods. The possibility of monitoring the elaboration has also been evaluated by collecting samples after 3 and 6 months of ageing in oak wood barrels. 2. Experimental

Table 2 Array of sensors used in this study ID

Reference

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20

SP-31 SP-15A SB-AQ1A SB-50 SP-53 SB-AQ4A SB-95 SB-11A SP-MWO SP-19 SP-12A SB-30A SP-MW1 TGS-880 TGS-2611 TGS-822 TGS-2610 TGS-2600 TGS-826 TGS-2620

barrels of different origins and toasting levels. Samples were collected after 3 and 6 months of ageing in the oak barrel. The Oenological Centre of Castilla y Le´on characterised the wines chemically and by a human panel test following international regulations [23]. 2.2. Multisensor system

2.1. Wine samples An artificial standard wine was prepared according to ref. [23], by solving 40 organic compounds in EtOH 12%. This artificial wine of fixed composition was used as a reference to compare the responses of the array of MOX sensors under different atmospheres. Table 1 collects the wine samples analysed with the optimised system using SPME and a mixture of air/nitrogen as a carrier gas. They were purposely prepared by the Oenological Centre of Castilla-Leon (Spain) from grapes of the variety Tempranillo (from Ribera de Duero. Spain). Once the vinification was finished, wine was divided in nine aliquots and introduced in oak Table 1 Wine samples under study ID

Vintage

Ageing (months)

Oak wood origin/toastinga

V1 V2 V3 V4 V5 V6 V7 V8 V9

2002 2002 2002 2002 2002 2002 2002 2002 2002

3 and 6 3 and 6 3 and 6 3 and 6 3 and 6 3 and 6 3 and 6 3 and 6 3 and 6

American/MT French-Allier/MT+ French-Nevers/MT French-Dorean/MT French-Allier/MT French-Dorean MT+ French-Nevers/ST Lithuan/MT Lithuan/MT+

a MT: medium toasting; MT+: medium toasting plus; ST: strong toasting. Allier, Nevers and Dorean are different barrel makers.

The array of gas sensors listed in Table 2 was constructed using inorganic sensors (Figaro Inc. and FIS) that were selected according to the previous experience of our group [11]. The sensors were mounted in a stainless steel test box with an internal volume of 75 mL. The test box was kept at a constant temperature (50 ◦ C) throughout the experiments. Data collection was performed through a PC-LPM-16 data acquisition card from National Instruments interfaced to a personal computer. The sensors were polarized using a constant voltage of 5 V provided by a FAC-662B programmable power supply. The scan rate used to measure the resistance was 0.5 s. Data were monitored in real time and the graphs could be followed using Visual Basic software from Microsoft. When pure air was used as a carrier gas, a static headspace sampler (Hewlett Packard Mod HP 7694E) was used for the injection of the headspace above the standard wine to the sensor chamber. Wine (3 mL) was placed in 10 mL vials that were kept at a constant temperature (40 ◦ C) for 10 min in order to obtain a homogeneous headspace. Each vial was pressurized for 4.68 s at 1.5 bar. The pressure gradient that builds up permitted to fill a 3 mL loop in 20 s, and its content was then injected to the charge carrier gas that drove the volatiles to the sensor chamber. The flow rate was 150 mL min−1 . An injection of 1 min was performed every 30 min. For the studies carried out with inert gases and mixtures of gases, solid phase microextraction (SPME) technique was used. In this case, 3 mL of the standard wine were placed in 10 mL

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vials. The vials were encapsulated and the SPME fiber coated with 100 ␮m of polyacrylate (PA Supelco) was placed in the gas-phase of the vial for 15 min at room temperature (25 ◦ C). Then, the SPME fibre was placed in a heated injection port of a gas chromatograph (HP4890D) and dried for 5 min at 50 ◦ C. Then, the temperature was raised up to 250 ◦ C in order to drive the volatile compounds to the test chamber. The test box was connected to the injection port of a gas chromatograph in order to keep a constant flow of 150 mL min−1 . 2.3. Statistical analysis A non-supervised technique, the principal component analysis (PCA) and a supervised technique, the partial least squares-discriminant analysis (PLS-DA) were used as discrimination and classification tools (The Unscrambler v. 9.1, CAMO ASA, Norway and Matlab v5.3., The Mathworks Inc., Natick, MA, USA). All samples were measured seven times with the array of sensors. The classification models were subjected to full cross-validation by means of the “leave-one-out” method. Previous data autoscaling was carried out in order to normalise the different units and/or ranges of the variables. 3. Results 3.1. Performance of the array of MOX sensors under different carrier gases The first step in this work was to evaluate the performance of the sensors under different gas carriers. For this purpose, the sensing units were polarised and exposed to air, nitrogen or helium (150 mL min−1 ). Table 3 collects the data of resistance

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measured for different backgrounds (data were registered after stabilisation of the signals, typically 48 h). In the presence of air, the resistances of MOX sensors ranged from 2.2 × 103 to 1.5 × 106 . These values were stable for several days. Upon exposure to nitrogen or helium, the sensors increased their conductivity drastically due to the displacement of the oxygen adsorbed on the surface. It has to be noticed that the use of helium as a carrier gas caused drastic damages to the sensors. In fact, after 24 h exposure to helium, eight sensors did not provide significant values of conductivity. Nitrogen also had an effect on the operation of the sensors, but in this case, only one sensor was damaged. The responses of the sensors towards red wines under different atmospheres were tested using a standard red wine. As observed in Table 3, in the absence of oxygen, the sensor signals (defined as the maximum %R/R0 , where R0 is the initial resistance measured in the presence of the background gas and R is the difference between the resistance measured at a certain time and R0 ) were quite similar whatever the inert gas used. The expected differences with the responses registered under oxygen according to ref. [21] were not observed here probably because the different injection method used. Table 3 also collects the repeatability of the responses expressed as the relative standard deviation (R.S.D.) of 10 consecutive measurements. Successive exposures caused a decrease of the intensity of the signals whatever the carrier gas used. However, the standard deviation for 10 replicate responses was smallest under air (%R.S.D. ranged from 2 to 10%) and largest for inert gases (up to 25%). In the case of helium the R.S.D. of all sensors was superior to 7%. Important differences were also observed in the kinetics of the responses and in particular in the reversibility of the sensing process that was faster in the

Table 3 Resistance of the sensors exposed to air, nitrogen and helium (150 mL min−1 ); response of the array of sensors towards an artificial wine and %R.S.D. of 10 consecutive measurements ID

Air Rair ()

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20

4.43 × 10+5 5.41 × 10+4 1.21 × 10+4 2.24 × 10+3 4.68 × 10+4 2.50 × 10+4 6.35 × 10+3 9.34 × 10+3 4.63 × 10+5 1.51 × 10+6 3.61 × 10+4 9.83 × 10+3 6.68 × 10+4 1.70 × 10+5 2.29 × 10+5 9.92 × 10+4 4.28 × 10+5 1.14 × 10+5 2.59 × 10+5 5.85 × 10+4

Nitrogen %R/R0air 82.74 12.32 75.97 59.78 50.73 53.33 45.70 40.63 57.93 61.00 38.22 47.78 31.61 65.00 55.71 63.26 47.83 79.67 88.32 71.98

%R.S.Dair

RN2 ()

4 5 4 8 8 5 7 4 4 10 3 2 5 3 4 3 4 4 3 4

1.24 × 10+3 1.56 × 10+4 4.10 × 10+2 – 2.64 × 10+3 2.82 × 10+2 2.04 × 10+3 3.02 × 10+3 1.00 × 10+5 1.79 × 10+4 2.66 × 10+4 5.94 × 10+2 3.90 × 10+4 4.14 × 10+4 3.22 × 10+4 4.22 × 10+4 9.53 × 10+3 6.15 × 10+4 9.68 × 10+3 4.63 × 10+4

Helium %R/R0N2 84.17 13.47 76.04 – 52.42 53.35 46.02 41.01 58.42 61.57 39.91 48.06 32.10 65.02 56.09 64.41 48.16 79.87 88.63 72.16

%R.S.D.N2

RHe ()

%R/R0He

%R.S.D.He

14 7 5 – 11 11 11 12 5 13 25 6 2 7 4 7 6 5 10 5

1.50 × 10+4

81.92 – 75.05 – – 52.24 – 40.61 57.50 60.50 – 47.40 – – 55.28 61.83 47.26 78.55 – 71.78

15 – 7 – – 13 – 14 8 15 – 8 – – 12 9 14 8 – 5

– 5.10 × 10+2 – – 1.20 × 10+4 – 1.70 × 10+3 7.80 × 10+4 1.50 × 10+3 – 2.50 × 10+2 – – 3.40 × 10+3 3.00 × 10+3 5.01 × 10+2 1.45 × 10+2 – 1.02 × 10+3

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Table 4 Dynamic parameters of the responses of the sensors towards the artificial wine using air, nitrogen and helium as a carrier gas ID

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20

Air

Nitrogen

Helium

t0.9 (s)

t0.1 (min)

tTOT (min)

t0.9 (min)

t0.1 (min)

tTOT (min)

t0.9 (min)

t0.1 (min)

tTOT (min)

0.0 1.5 0.0 8.0 10.0 13.0 0.1 0.1 0.8 0.1 0.3 1.0 0.7 0.1 0.2 0.3 0.3 0.1 0.1 0.1

4.1 14.6 4.3 1.8 9.5 15.9 4.4 4.3 18.6 9.1 4.9 12.1 16.7 4.0 5.0 6.2 5.4 5.4 1.1 5.1

4.1 14.6 4.3 2.0 9.7 16.1 4.4 4.3 18.6 9.1 4.9 12.1 16.7 4.1 5.2 6.5 5.6 5.4 1.2 5.2

2.4 1.1 0.1 – 0.8 1.3 1.0 0.5 1.0 0.7 0.7 3.3 2.2 0.2 0.1 0.1 0.2 0.3 0.1 0.3

59.6 72.9 57.9 – 81.2 56.3 54.8 56.2 48.3 81.2 45.7 47.2 50.0 16.5 11.6 29.7 47.1 21.7 63.7 80.4

62.0 74.0 58.0 – 82.0 57.6 55.7 56.7 49.3 81.9 46.4 50.4 35 16.7 11.6 29.9 47.3 22.0 63.8 80.7

5.5 – 5.7 – – 5.9 – 5.3 5.3 5.7 – 5.4 – – 5.7 5.4 6.2 5.6

17.9 – 41.3 – – 45.0 – 41.7 45.0 41.7 – 18.2 – – 31.6 9.5 30.7 36.9

23.4 – 46.9 – – 50.9 – 47.0 50.3 47.3 – 23.6 – – 37.3 14.8 36.8 42.5

5.5

45.0

50.5

t0.9 : time to reach 90% of the total response; t0.1 :time to recover 90% of the original signal.

presence of oxygen. Table 4 collects the dynamic characteristics of the responses expressed as t0.9 (the time to reach 90% of the total response), t0.1 (the time to recover 90% of the original signal). The responses and recovery times were particularly long under helium gas. These results indicate that there is a competitive surface adsorption between the carrier gas and the volatiles present in wines. O2 is easily displaced from the sensor surface than nitrogen giving rise to different sensing mechanisms. In good accordance with this idea, the reversibility is

more difficult when nitrogen and helium are used as a carrier gas. The influence of the gas carrier on the drift of the baseline was analysed by registering the resistance value R at the end of every working day (average of 20 injections per day) during 5 days. The ratio R/R0 (where R0 was the resistance value measured at the beginning of the first working day) is shown in Fig. 1. Helium caused important drifts of the baseline, particularly after the third day of continued use. In contrast, the sensors working

Fig. 1. Drift of the baseline during 5 days of continued use of (a) S1; (b) S6; (c) S15; and (d) S20 under () air; (×) nitrogen and (䊉) helium.

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under oxygen or nitrogen maintained their baseline resistances almost constant (6% R.S.D. maximum). Under the light of the above results it is evident that MOX sensors show a slower kinetics and decrease their stability when using pure inert gases as a carrier gas. In addition, the use of helium causes important damages in the MOX sensors, shortening their lifetime drastically. These problems caused by inert gas carriers are an important shortcoming for the use of injection systems such as SPME (where the use of inert gases for the desorption of volatiles is compulsory) coupled with arrays of MOX sensors. 3.2. Performance of the array of MOX sensors using mixtures of air/nitrogen or air/helium as a carrier gas In order to couple efficiently the SPME with an array of MOX sensors our group has designed a system that allows mixing air with inert gas carriers in the following manner. In a first step, an inert carrier gas (100 mL min−1 ) is used to desorb thermally the SPME fiber. The presence of the inert gas avoids the occurrence of undesirable combustions during desorption. In a second step, the gas flow containing the mixture of VOCs with inert gas is mixed with a flow of air (50 mL min−1 ). This second step ensures the presence of oxygen in the chamber where MOX sensors are placed. Fig. 2 illustrates the gas circuits used in this work. When mixtures of helium–air or nitrogen–air were injected in the test chamber, resistances measured after stabilisation of the signals were higher than those observed when using the respective inert gases (Table 5). Also in this case, helium damaged some of the sensors only few minutes after being exposed to the carrier gas. In turn, when the volatiles of a standard red wine were

Fig. 2. Gas circuits for SPME desorption and gas mixing with oxygen.

analysed using mixtures of air/nitrogen or air/helium, the percentage changes of the resistances were similar to those observed for pure gases previously shown in Table 3. The repeatability of 10 consecutive measurements ranged from 1 to 14% when using a mixture of air/nitrogen and from 7 to 17% when using a mixture of air/helium. The kinetics of the signals in the presence

Table 5 Resistance of the sensors exposed to mixtures of inert gas (100 mL min−1 ) and air (50 mL min−1 ); dynamic parameters and %R.S.D. of 10 consecutive measurements towards the standard red wine ID

N2 + air R ()

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20

5.18 × 10+4 6.54 × 10+4 9.12 × 10+3 8.17 × 10+2 4.37 × 10+3 5.70 × 10+3 9.98 × 10+3 4.41 × 10+3 2.79 × 10+5 2.70 × 10+5 3.71 × 10+4 8.22 × 10+2 4.81 × 10+4 6.39 × 10+4 5.02 × 10+4 8.36 × 10+4 4.91 × 10+4 9.03 × 10+4 1.05 × 10+4 1.78 × 10+5

He + air %R.S.D. 2 6 1 4 2 8 5 9 8 9 14 5 2 5 1 2 4 4 4 2

t0.9 (min)

t0.1 (min)

R ()

1.4 1.5 0.1 0.4 0.4 0.4 0.6 0.7 0.8 0.7 0.2 0.7 1.4 0.1 0.1 0.2 0.3 0.1 0.1 0.1

16.6 17.0 25.1 17.5 17.5 14.0 26.4 18.6 37.4 40.3 19.1 19.5 20.0 11.0 10.0 14.0 12.0 16.0 16.0 14.0

6.70 × 10+4 – 1.90 × 10+4 – – 1.20 × 10+4 – 1.40 × 10+4 3.90 × 10+5 8.70 × 10+4 – 2.50 × 10+3 – – 5.00 × 10+3 3.80 × 10+4 1.74 × 10+5 2.40 × 10+4 – 1.34 × 10+5

%R.S.D. 10 7 2 – – 10 – 7 10 11 – 8 9 – 12 9 6 3 – 7

t0.9 (min)

t0.1 (min)

2.0 – 1.0 – – 1.0 – 1.0 1.0 1.0 – 1.0 2.0 – 0.0 0.0 0.0 0.0 – 0.0

16.0 – 40.0 – – 40.0 – 39.0 38.0 41.0 – 15.0 32.0 – 23.0 41.0 36.0 21.0 – 29.0

Performance of the sensors using mixtures of inert gas (100 mL min−1 ) and air (50 mL min−1 ): resistance, dynamic parameters and RSD% of 10 consecutive measurements towards the standard red wine.

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Fig. 3. Response of S2 towards the standard red wine under nitrogen (solid line) and a mixture air/nitrogen (dotted line).

Fig. 5. Average values of the responses of sensors S1, S3, S19 and S20 exposed to the standard red wine repeatedly during 4 months.

of mixtures containing oxygen and inert gas were faster than those observed under pure N2 or He. This is illustrated in Fig. 3, where the responses of S2 towards the artificial red wine using pure nitrogen or a mixture air/nitrogen are presented. The drifts of the baselines measured as R/R0 at the end of every working day (Fig. 4) were less important than in the absence of oxygen. These results allow concluding that the presence of a certain percentage of oxygen improves the performance (mainly stability, kinetics and reproducibility) of the SPME-MOX system. This improvement is in good agreement with the fact that oxygen is easily displaced from the sensor surface than nitrogen or helium. However, it is important to remind that the presence of helium, even mixed with air, produced important damages in some of the sensors and the use of nitrogen is preferred. The concentration of oxygen in the mixture of air/nitrogen has a relevant importance in the behaviour of the sensors. Results obtained under different proportions of air/nitrogen are collected in Table 6. Table 6 includes the resistance after stabilisation, t0.1 and %R.S.D. of 10 consecutive measurements. The data indicate that increasing concentrations of oxygen produce an improvement of the kinetics and of the repeatability of the responses. In fact, at least a ratio of air/nitrogen = 50:100 mL min−1 is required to obtain acceptable results in terms of repeatability and reversibility.

In the following experiments the carrier gas used has been air/nitrogen of 50:100 mL min−1 since this mixture contains the sufficient concentration of oxygen to have good responses with good stability and enough concentration of nitrogen to ensure an appropriate desorption of the volatiles retained in the adsorbent material, also increasing the lifetime of the fibre. The long-term stability is crucial if arrays of sensors have to be used in practical or industrial applications and in particular for monitoring of the ageing of wines. For this reason, the stability of the sensors under a mixture of air/nitrogen 50:100 mL min−1 was evaluated over longer periods of time Fig. 5 shows the average %R/R0 values measured during 1 month (25 injections per day) for sensors S1, S6, S15 and S20. After 3 months of use, the intensity of the signals decreased by 10–35%. Signals decreased drastically during the fourth month. The reason of the loss of the sensitivity can be attributed to ageing processes occurring at the sensor surface. This is illustrated in Fig. 6 where SEM images of S6 registered before and after 4 months of continuous exposure to the standard red wine are shown. The sensor surface is modified by deposition of carbonaceous particles that poison the sensor surface decreasing the number of active sites. According to these results the array of MOX sensors can be used safely coupled with SPME using mixtures of air/nitrogen during 3 months of continued use. After this period, the sensors loose sensitivity and must be replaced.

Fig. 4. Drift of the baseline during 5 days of continued use of (a) S1; (b) S6; (c) S15; and (d) S20 under (䊉) air/nitrogen and (×) air/helium.

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Table 6 Resistance of the sensors exposed to different ratios of air:inert gas; dynamic parameters of the responses towards an artificial red wine and %R.S.D. of 10 consecutive measurements towards the standard red wine ID

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20

Air–N2 100:50 (mL min−1 )

Air–N2 50:100 (mL min−1 )

Air–N2 10:140 (mL min−1 )

R ()

t0.1 (min)

%R.S.D.

R ()

t0.1 (min)

%R.S.D.

R ()

t0.1 (min)

%R.S.D.

5.12 × 10+4 6.49 × 10+4 1.07 × 10+4 9.60 × 10+2 1.23 × 10+4 5.44 × 10+3 6.53 × 10+3 5.65 × 10+3 2.82 × 10+5 1.21 × 10+6 3.69 × 10+4 3.26 × 10+4 1.36 × 10+5 1.93 × 10+5 8.94 × 10+4 3.48 × 10+5 7.20 × 10+4 2.22 × 10+5 9.64 × 10+4 1.82 × 10+5

16 15 22 14 14 25 22 15 39 33 16 15 7 15 9 9 9 8 6 9

1 5 1 1 1 8 2 4 4 4 8 3 1 4 4 5 8 9 8 1

5.18 × 10+4 6.54 × 10+4 9.12 × 10+3 8.17 × 10+2 4.37 × 10+3 5.70 × 10+3 9.98 × 10+3 4.41 × 10+3 2.79 × 10+5 2.70 × 10+5 3.71 × 10+4 8.22 × 10+2 4.81 × 10+4 6.39 × 10+4 5.02 × 10+4 8.36 × 10+4 4.91 × 10+4 9.03 × 10+4 1.05 × 10+4 1.78 × 10+5

17 17 25 18 18 14 26 19 37 40 19 20 20 11 10 14 12 16 16 14

2 6 1 4 2 8 5 9 8 9 14 5 2 5 1 2 4 4 4 2

1.24 × 10+3 1.56 × 10+4 4.10 × 10+2 – 2.64 × 10+3 2.82 × 10+2 2.04 × 10+3 3.02 × 10+3 1.00 × 10+5 1.79 × 10+4 2.66 × 10+4 5.94 × 10+2 3.90 × 10+4 4.14 × 10+4 3.22 × 10+4 4.22 × 10+4 9.53 × 10+3 6.15 × 10+4 9.68 × 10+3 4.63 × 10+4

60 73 58 – 81 56 55 56 48 81 46 47 50 17 12 30 47 22 64 80

14 7 5 – 11 11 11 12 5 13 25 6 2 7 4 7 6 5 10 5

3.3. Multisensor system coupled with a pattern-recognition software—deiscrimination of red wines It can be expected that the improvement of the stability of the sensors can improve the monitoring of foods and beverages. After optimising the working conditions of the SPME coupled with an array of MOX sensors, the performance of the system was tested towards wine samples of industrial interest. For this purpose, the optimised system using SPME with polyacrylate fiber and a mixture of air:nitrogen = 50:100 as a carrier gas was repeatedly exposed to nine wines elaborated using the same variety of grape and aged in oak barrels with different degrees of toasting. Samples were measured after 3 and 6 months

of ageing in the barrel. The discrimination capability of the multisensor system was analysed by means of principal component analysis (PCA). The PCA score plot obtained using the array of MOX sensors is shown in Fig. 7. The first two components explain the 64% and 20% of the captured information. As observed in the figure the clusters corresponding to the wines are well separated from each other, indicating that the nine wines could be clearly discriminated. The relative positions of the clusters are related to the length of the ageing, and wines aged during 3 months appear on the left side of the graph, whereas wines aged during 6 month appear on the right part. It has to be noticed that the wines aged during a longer period of time are more separated

Fig. 6. SEM images (650× at 20 kV), of sensor S10; (a) new sensor; (b) after 4 months of continued exposures to the standard wine.

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the wine with the oak barrel. After 6 months of ageing, a goodquality model performance (slope near 1, offset near 0 and large correlation between sensors and categorised variables) were found. In addition, low RMSEC (root mean square error of calibration) and RMSEP (root mean square error of prediction) values were obtained. Moreover, PLS-DA (using five latent variables) has established three classes of wines according to the origin of the cask wood. 4. Conclusions Fig. 7. PCA score plot of the measurements of nine wines measured after 3 and 6 months of ageing.

Fig. 8. PCA loading plot of the measurements of nine wines measured after 3 and 6 months of ageing.

from each other. This result is in good agreement with chemical analysis and panel test analysis and is due to the fact that longer periods of contact of wines with the barrel allow the wines to acquire the particular characteristics associated by the type of wood. In fact, the relative positions of the clusters are related to the degree of tasting of the oak used to construct the barrel. Fig. 8 shows the contribution of the variables (peak height) on PC1 and PC2. All the sensors have shown a high contribution to the two principal components PC1 and PC2 (>75%). The different location of loadings associated with different sensors is the consequence of the complementary information brought by each variable. The second approach applied to the data analysis was the partial least squares-discriminant analysis (PLS-DA). Table 7 collects the quantitative data derived from the PLS-DA regression model. As expected, both the calibration and the validation values were improved when increasing the time of contact of Table 7 Results of PLS-DA in calibration and validation Ageing

Tosting level Rcalibration

RMSEC

3 Months

American French Lithuan

0.6618 0.7990 0.6399

3.73 × 10−1

Rvalidation

RMSEP

0.6600 2.83 × 10−1 0.7659 2.41 × 10−1 0.5612

3.99 × 10−1 3.04 × 10−1 2.61 × 10−1

6 Months

American French Lithuan

0.9864 0.9871 0.9900

8.17 × 10−2 0.9779 7.55 × 10−2 0.9799 4.43 × 10−2 0.9834

1.04 × 10−1 9.42 × 10−2 5.70 × 10−2

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Biographies Sonia Villanueva received her BSc in Chemistry in 2001 and his PhD in 2005 from the University of Valladolid, Spain. She is currently a post-doc fellow at the R&D Laboratory of the company Matarromera, devoted to the research in the field of characterisation of wines. Alberto Guadarrama received his PhD from the University of Valladolid (Spain) in 2000. He is currently a senior researcher at the R&D Laboratory of the company Matarromera, devoted to the research in the field of characterisation of wines. Mar´ıa Luz Rodr´ıguez-M´endez is Professor in the Department of Inorganic Chemistry at the University of Valladolid (Spain). She received the degree in Chemistry from the University Complutense of Madrid (Spain) in 1984 and the PhD in Chemistry from the University of Valladolid (Spain) in 1990. She has been working in the preparation and the structural characterisation of thin films of organic semiconductors and their potential applications. Her main current interest is in the development of sensors based on phthalocyanines and on conducting polymers. At the present moment she is involved in a UE Project devoted to the development of an electronic nose, an electronic tongue and an electronic eye for the assessment of the organoleptic characteristics of wines and olive oils. Jos´e Antonio de Saja is a Professor and Head of the Department of Condensed Matter Physics at the University of Valladolid. His present research interest is at the intersection of materials science, physics, physical chemistry and device engineering and focus on novel sensing materials. He is the head of the Group of Sensors at the University of Valladolid. At the present moment he is coordinating a UE Project devoted to the development of an electronic nose, an electronic tongue and an electronic eye for the assessment of the organoleptic characteristics of wines and olive oils.