Electronic nose for wine ageing detection

Available online at www.sciencedirect.com

Sensors and Actuators B 133 (2008) 180–186

Electronic nose for wine ageing detection J. Lozano c,∗ , T. Arroyo b , J.P. Santos a , J.M. Cabellos b , M.C. Horrillo a a

Laboratorio de Sensores, Consejo Superior de Investigaciones Cient´ıficas (CSIC), C/Serrano 144, 28006 Madrid, Spain b Department Agroalimentaci´ on, Instituto Madrile˜no De Investigaci´on Y Desarrollo Rural, Agrario Y Alimentario (IMIDRA), Km 38.2 N-II, 28800 Alcal´a de Henares, Spain c Grupo de Clasificaci´ on de Patrones y An´alisis de Im´agenes, Universidad de Extremadura. Av. Elvas s/n, 06006 Badajoz, Spain Received 22 November 2007; received in revised form 5 February 2008; accepted 6 February 2008 Available online 14 February 2008

Abstract This paper reports a novel application of an electronic nose (e-nose) for recognition and detection of wine ageing. Two different measurements are performed with the following samples: first, in an experimental cellar the same wine is aged in different type of oak barrel (French and American oak) and during different time (0, 3, 6 and 12 months) and second, several wines made with the same grape variety and from different wine cellars aged in French and American oak. This identification has a great importance for origin denominations for control of frauds. The e-nose is home-developed and home-fabricated for this purpose: a tin oxide multisensor prepared with RF sputtering onto an alumina substrate and doped with chromium and indium is used. The sampling method employed is static headspace followed by a dynamic injection. Linear techniques like principal component analysis (PCA) and nonlinear ones like probabilistic neural networks (PNN) are used for pattern recognition. A classification success rate (correct predicted number over total number of measurements) of 97% and 84% is achieved in detection of the different ageing process experimented by the wines tested. © 2008 Elsevier B.V. All rights reserved. Keywords: Wine ageing; Odor recognition; Artificial olfactory system

1. Introduction Oak barrels are commonly used in the ageing of wine and spirits because of the positive effects they have in sensory characteristics [1,2] and in volatile compounds [3–7]. Because of that, the process of ageing is one of the fundamental and most important steps in obtaining quality wines. In this way, ageing produces wines with more elegant and stable colours, a more complex aroma and better taste due to the loss of sensations of astringency and bitterness [8]. The flavour and slight oxidation given by barrel maturation donate characteristics usually appreciated in wines [9]. Among the different factors affecting the maturation of red wines in oak barrels, the geographical origin of the wood is of special importance since it is decisive for the structure and

∗

Corresponding author. Tel.: +34 924 289 600; fax: +34 924 289 601. E-mail address: [email protected] (J. Lozano).

0925-4005/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2008.02.011

chemical composition of the wine [10,11]. Furthermore, issues like the toasting level [12] or the duration of the ageing [13] in the oak barrel also influence the composition and quality of wines. It must be taken into account that all wines are not suitable for ageing in wood. They must be balanced, high quality wines and with a high level of alcohol. As a general rule, the discrimination of the wines is not an easy task due to the complexity and heterogeneity of its headspace [9]. However, the classification of the wines is very important because of high economic value of the wine-product, to protect the quality wines, to prevent illegal adulteration of wines, to guarantee the wine quality in import–export market and to control beverage processing. Identification of wine ageing is very important for avoiding frauds. Origin denominations are very careful in detecting frauds in labelling bottles and make inspections in cellars in order to check that the elaboration processes correspond to the ones specified in the label. There is no analytical method currently available in Spain to determinate elapsed time in wine ageing.

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Generally, the sensory analysis based on the trained experts panel test is useful in the wine classification task, but it is not always feasible because of high-cost and time-consuming and sometimes without any objective estimation. Zamuz et al. [14] proposed an enzymatic study as potential markers of wine ageing for the detection of fraud. In addition, the use to other methods for wine discrimination essentially based on instrumental analytical techniques has been reported [15]. The common methods of chemical analysis such as gas and liquid chromatography, mass spectrometry, nuclear magnetic resonance and spectrophotometry have higher reliability, longer processability, low in situ measurableness and higher costs. In this scheme of analytical methods, it has been proposed to use a sensor array combined with multivariate statistical analysis techniques trying to imitate a simplified human sense of smell but removing the subjective component implicit in it. The “so-called” electronic noses or artificial olfactory systems have the advantage of high portability for in situ and on-line measurements with lower costs and good reliability. In the last years, a great deal of research towards the development of electronic noses has been carried out [16,17]. These systems have been used to analyse the headspace of several foods or beverages [18–23]. In particular, attempts have been made to discriminate wines [21,23–33] using a variety of chemical sensors, including the use of resistive metal oxide semiconductor (MOS) sensors [24–28]. But, there are no previous works about the use of e-nose in wine ageing discrimination. There are only two papers related to wine ageing measurements but they are performed with electronic tongues [34,35]. The general scheme of an e-nose is formed by four main elements: an aroma extraction technique or air flow system which switches the reference air and the tested air and carry the volatile compounds from the samples to the next step; an array of chemical sensors which transform the aroma into electrical signals; an instrumentation and control system to measure the signal of the different sensors and the control and automation of the entire system. The fourth part of the system is the pattern recognition system to identify and classify the aroma of the measured samples in several classes previously learned [36,37]. 2. Experimental 2.1. Wine samples Two different sets of samples were used in this experiment: wine samples from an experimental wine cellar and wine samples from collaborating cellars. The first samples were elaborated in the wine cellar of the “Instituto Madrile˜no de Investigacion y Desarrollo Rural Agrario y Alimentario” (IMIDRA) with grapes of Tempranillo variety. Grapes grown in the same vineyard was harvested at commercial maturity (25◦ Brix). The harvested grapes were quickly transported in plastic boxes (30 kg) to the cellar where they were processed following classic techniques of elaboration of red wines. The grapes were de-stemmed and crushed and 50 mg/l de SO2 was added. The alcoholic fermentation was carried out in steel tanks, and the fermentation temperature was

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<30 ◦ C. The end of fermentation was taken when reducing sugar concentration decreased bellow 2 g/l. Before ageing the wine, malolactic fermentation was induced by addition of a commercial Leuconostoc oenos strain until the malic acid level got under 1 g/l. Then the wine was clarified and the SO2 concentration adjusted around 50 mg/l. The wine was introduced in wood barrels (225 l) from American and French oaks of medium toast level and the ageing was conducted in a special chamber during 12 months at 14 ◦ C. Samples were taken in four stages of the wine ageing: before ageing, 3, 6 and 12 months of ageing in oak barrel. The second set of samples was got from several wine cellars of Madrid region and belonging to the “Vinos de Madrid” origin denomination. All wines were made with grapes of Tempranillo variety. The manufacturing process was the typical process used in wine industry. The wine samples used in this experiment were aged only in one type of oak barrel: American or French oak. Samples were frozen and stored at −20 ◦ C until the moment of their measurement. Table 1 shows a list of the wine samples measured with the artificial olfactory system designed. 2.2. Artificial olfactory system 2.2.1. Sensors The multisensor included 16 sensor elements distributed in circular shape onto an alumina substrate. The tin oxide thin films were grown by reactive sputtering from a SnO2 target under a 10:90 oxygen–argon mixture. More details of the complete procedure for the preparation of the sensors can be found in refs. [38,39]. Deposition conditions were fixed during the sputtering process (independently of the target used) and were as follows: substrate holder temperature 250 ◦ C, plasma pressure 0.5 Pa, acceleration voltage 500 V, radio frequency power 100 W. Some of the sensors were doped with different amounts of Cr and In, by changing the deposition time during the sputtering process. Dopants were deposited as an intermediate discontinuous layer between two layers of SnO2 (sandwich structure) or were deposited as a superficial and discontinuous layer. Table 2 shows the multisensor distribution. The multisensor is organised in five blocks and each one comprises several elements: block 1 formed by SnO2 of different thickness; blocks 2 and 3 doped with Cr and In respectively as sandwich structure, and blocks 4 and 5 doped with Cr and In respectively as a superficial layer. Doping levels were different and were expressed as sputtering time in seconds. The multisensor was thermally treated in air at 520 ◦ C for 4 h to control the material morphology (stoichiometry and grain size of the tin oxide and dopant distribution) and to stabilize the semiconductor electrical resistance before the measurement. Annealing was fundamental in order to obtain a good detection [40,41]. 2.2.2. Sampling method The sampling method employed was static headspace followed by a dynamic injection. The carrier gas used was 99.998% purity nitrogen in order to preserve the wine. Gas line tubes were of stainless steel covered with fused silica in order to minimize gas adsorption in the line. The way of carrier gas and volatile

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Table 1 Wine samples Wine cellar

Variety

Year

Type of ageing

Time of ageing

IMIDRA IMIDRA IMIDRA IMIDRA IMIDRA IMIDRA IMIDRA Pablo Morate Pablo Morate Carlos Gosalbez Carlos Gosalbez Antonio Morate Antonio Morate Luis Saavedra Luis Saavedra Carlos Gosalbez Carlos Gosalbez

Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo Tempranillo

2002 2002 2002 2002 2002 2002 2002 2003 2003 2002 2002 2003 2003 2003 2003 2003 2003

– American Oak Barrel French Oak Barrel American Oak Barrel French Oak Barrel American Oak Barrel French Oak Barrel American Oak Barrel French Oak Barrel American Oak Barrel French Oak Barrel American Oak Barrel French Oak Barrel American Oak Barrel French Oak Barrel American Oak Barrel French Oak Barrel

0 3 months 3 months 6 months 6 months 12 months 12 months 6 months 6 months 6 months 6 months 6 months 6 months 6 months 6 months 6 months 6 months

compounds was selected with the control program using two electric valves. 10 ml of sample were kept in a 50 ml Dreschel bottle at 30 ◦ C for 30 min in order to generate a vapour phase in equilibrium with the liquid. The electric valves were switched during 20 min in order to permit nitrogen to carry the aromatic compounds to the sensor cell. After this time, the electric valves were switched again to allow the sensors to desorb. This procedure was repeated at least 8 times for each type of wine using different samples. Random order of sample measurements was used in this experiment. The sensors were calibrated once a week with a blank solution (12% (v/v) ethanol in deionised water) in order to reduce the effects of sensors drift [42,43]. All measurements were carried out at a constant gas flow of 200 ml/min.

Bronkhorst Hi-Tec) of pure nitrogen (200 ml/min). A thermocouple was placed in contact with the multisensor to measure the operating temperature, which was continuously recorded. The operating temperature of sensors was controlled to 250 ◦ C with a PID temperature controller. The resistances of the sensors were measured with a Keithley 2700 71/2 digits digital multimeter (DMM) with a 40-channels multiplexer connected to the personal computer through a GPIB interface. The control of the gas line as well as the data acquisition were carried out automatically by a personal computer and a control program. The software of control was home developed for this application and was programmed in Testpoint. More details of the system are shown in ref. [44]. Fig. 1 shows the architecture of the system.

2.2.3. Measuring setup The multisensor device was placed in a steel test chamber (20 cm3 ) and the resistance measurements were carried out under a constant flow (mass flow controllers manufactured by

2.2.4. Data processing Responses of the individual sensors are defined with respect to the minimum resistance to 12% (v/v) of ethanol for all the measurements:

Table 2 Multisensor composition

r=

Sensor

Composition

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

SnO2 200 nm SnO2 400 nm SnO2 600 nm SnO2 800 nm SnO2 (300 nm) + Cr(4 nm) + SnO2 (150 nm) SnO2 (300 nm) + Cr(8 nm) + SnO2 (150 nm) SnO2 (300 nm) + Cr(16 nm) + SnO2 (150 nm) SnO2 (300 nm) + Cr(24 nm) + SnO2 (150 nm) SnO2 (300 nm) + In(10 nm) + SnO2 (150 nm) SnO2 (300 nm) + In(20 nm) + SnO2 (150 nm) SnO2 (300 nm) + In(30 nm) + SnO2 (150 nm) SnO2 (300 nm) + In(40 nm) + SnO2 (150 nm) SnO2 (450 nm) + Cr(8 nm) SnO2 (450 nm) + Cr(16 nm) SnO2 (450 nm) + In(10 nm) SnO2 (450 nm) + In(20 nm)

Rwine Rcalibration

Fig. 1. Measurement setup: (1) nitrogen bottle, (2) mass flowmeter controller, (3) electric valves, (4) Dreschell bottle with sample in a thermostatic bath, (5) sensors cell, (6) PC and (7) DMM with multiplexer.

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Fig. 2. Average scores of 15 attributes identified by sensory panel of the wine samples.

where Rwine is the minimum resistance of the sensor in the measurement of wine and Rcalibration is that of the sensor exposed to a solution of 12% of ethanol. After the feature extraction, a preprocessing was performed to the data (centered and scaled). The data collected were analysed using a commercial software package (Matlab 6.1) for programming the feature extraction and the pattern recognition techniques (principal component analysis, PCA; artificial neural networks, ANNs). PCA applies a linear transformation to the data and result in a new space of variables called principal components [45]; it is an unsupervised method. Probabilistic neural networks (PNN) were used for classification purposes. PNN were composed by three layers: the input one had three neurons, corresponding with the three principal components; the hidden layer, with radial basis transfer functions, had the same number of neurons as the number of training vectors; and a competitive layer in the

output [45], leave one out (LOO) cross validation method was applied to the network in order to check the performance of the network [45]. LOO consisted of training N distinct nets (in this case, N is the number of measurements) by using N − 1 training vectors, while the validation of the trained net was carried out by using the remaining vector, excluded from the training set. This procedure was repeated N times until all vectors were validated [46,47]. 3. Results and discussion In order to validate the measurements against some objective wine ageing quality criteria, most of the wine samples were tasted by a sensory panel. A group of 30 people with previous experience in wine analysis were trained in recognizing aromatic compounds and descriptors from wine. Wines were

Fig. 3. Polar plots of the responses of the array of sensors towards wines.

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presented in random order to the panellists and no information was given to the assessors about the origin of the samples and no coloured lighting assessed panellists. The wine tasting took place in an air-conditioned room (21 ◦ C) with isolated booths. Judges assessed the aroma using a tasting evaluation sheet that included 10 sensory descriptors (herbaceous, fruity, flower-like, spicy, vegetal, phenolic, microbiologic, chemical, oxidation and woody-like) and 5 quality parameters (wine quality, aromatic intensity, alteration, persistence and off-flavours). The different terms were evaluated in a scale from 1 to 5 (1, null, very weak; 2, weak; 3, medium; 4, strong; 5, very strong). The average of all the panellists was calculated to build the prediction models. All the sensory evaluations were realized under Spanish Standardization Rules (UNE). The data obtained were processed and the average of all the panellists was calculated for each wine and descriptor. Different sensory profiles could be observed for the different wines. The sensory attributes obtained for the different wine samples are shown in Fig. 2. In these wines, the predominate descriptors are fruity, spicy and woody-like. Fig. 3 shows the radial plot of the 16 sensors responses to the first set of samples measured: wine before ageing, 3, 6 and 12 months of ageing in American (Am.) or French (Fr.) oak barrel. It can be noticed that the sensors give different signal for each different sample, illustrating the discrimination capabilities of the array. No relation among sensors response and time or type of ageing can be established according to Fig. 3. The variation in response intensity is due to different headspace composition of the wines. Each sample has its characteristic organic volatile compounds profile. For a better visualization of data, PCA was carried out using signals corresponding to ten repeated exposures collected in different days. Four components were found to be relevant. The first and second principal components captured 68 and 22% of the total variance, respectively. The corresponding plots of the first two principal components are shown in Fig. 4. It can be noticed that datasets are clearly separated, allowing an easy discrimination of wines. PCA demonstrates that the samples with the same time in barrel appear close to each other in the diagram. According to the PCA results, there is a major separation among classes of different time of ageing than between classes of different type of oak barrel (American or French). The PCA results were confirmed with the ANN analysis. A probabilistic neural network [43] was trained with the first three principal components calculated through PCA and vali-

Fig. 4. PCA score plot of measurements of wine samples from the experimental wine cellar.

Fig. 5. PCA score plot of measurements of wine samples from the collaborating wine cellars.

dated with leave-one-out method, giving a 97% classification success rate (correct predicted number over total number of measurements). In Table 3 is shown the confusion matrix (real, simulated) for the neural network trained. Each different type of wine corresponded to a different class; in this way 7 classes have been established. It can be observed there are only two wrong

Table 3 Confusion matrix for the PNN Real/predicted

Wine before ageing

3 months Am. Oak

3 months Fr. Oak

6 months Am. Oak

6 months Fr. Oak

12 months Am. Oak

12 months Fr. Oak

Wine before ageing 3 months Am. Oak 3 months Fr. Oak 6 months Am. Oak 6 months Fr. Oak 12 months Am. Oak 12 months Fr. Oak

10 0 0 0 0 0 0

0 9 1 0 0 0 0

0 1 9 0 0 0 0

0 0 0 10 0 0 0

0 0 0 0 10 0 0

0 0 0 0 0 10 0

0 0 0 0 0 0 10

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Table 4 Confusion matrix for the PNN and commercial aged wines

P.M.A. P.M.F. C.G.A.02 C.G.F.02 A.M.A. A.M.F. L.S.A. L.S.F. C.G.A.03 C.G.F.03

P.M.A.

P.M.F.

C.G.A.02

C.G.F.02

A.M.A.

A.M.F.

L.S.A.

L.S.F.

C.G.A.03

C.G.F.03

3 0 0 0 0 0 0 0 0 1

1 4 0 0 0 0 0 0 0 0

0 0 3 0 0 0 0 0 0 0

0 0 2 5 0 0 0 0 0 0

0 0 0 0 5 0 0 0 0 0

1 1 0 0 0 5 0 0 0 0

0 0 0 0 0 0 4 0 0 1

0 0 0 0 0 0 0 5 0 0

0 0 0 0 0 0 0 0 5 0

0 0 0 0 0 0 1 0 0 3

classifications of samples with 3 months ageing in oak barrel; the other samples are correctly classified. A total number of 70 samples have been used (ten samples per class). Fig. 5 shows the PCA results of the second set of samples. It can be observed that all dataset are clearly separated. It can also be noticed that several zones of wines aged in American and French oak barrel can be established except wines from Carlos Gos´albez 2002 that are located near and separated from the other samples. Each different type of wine corresponds to a different class, in this way 10 classes have been established. A total number of 50 samples have been used (five samples per class). These results are similar to that obtained with PNN classification. Table 4 shows the confusion matrix in which several errors in classification can be observed between samples located close. A classification success rate of 84% was achieved in this experiment. 4. Conclusions A system combining a multisensor of SnO2 with headspace sampling technique and a pattern recognition machine has been optimized to increase the discrimination capability with respect to ageing red wines. We have demonstrated the possibility of application of an artificial olfactory system for the detection and identification of the ageing process of the same and different wines. Both pattern recognition techniques are satisfactory for detection, i.e. PCA shows datasets clearly separated and PNN shows 97% and 84% success rate. Acknowledgment This work is being supported by the Spanish Science and Technology Ministry under the project TIC2002-04588-C02-01 and Comunidad de Madrid under the project OLFATOSENSE (S-SEM-0255-2006) into the Bioscience activities program. Authors also want to thank collaborating cellars for wine samples. References [1] L. Perez-Prieto, M.L. De la Hera-Orts, J.M. L´opez-Roca, J.I. Fern´andezFern´andez, E. G´omez-Plaza, Oak-matured wines: influence of the characteristics of the barrel on wine colour and sensory characteristics, J. Sci. Food Agric. 83 (2003) 1445–1450.

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Biographies ´ Lozano received the BSc degree in electronic engineering in 2001 and PhD Jesus degree in 2005 from Universidad Complutense de Madrid, Spain. He has worked in instrumentation systems at Electronics Department of the University Complutense of Madrid, in chemical sensors and electronic noses at the Laboratorio de Sensores, Consejo Superior de Investigaciones Cient´ıficas (CSIC), Madrid, in control, modelling and simulation at Naval Engineering School of Universidad Polit´ecnica de Madrid. Presently, he works as professor at Escuela de Ingenier´ıas Industriales of Universidad de Extremadura, Badajoz. His research interests include pattern recognition techniques, aroma extraction techniques applied to electronic noses, instrumentation and measurement systems, and chemical sensors. Teresa Arroyo received her PhD in biology in 2000. She is a researcher of the Instituto Madrile˜no de Investigaci´on y Desarrollo Rural Agrario y Alimentario (IMIDRA). Her research interests are in the field of quality and composition of vineyard and wine. Jos´e Pedro Santos received his BSc (1987) and PhD (1995) in physics from the University Complutense of Madrid. He has worked at the University of Milan (Italy), at the Institute of Advanced Materials of the European Commission’s Joint Research Centre (Ispra, Italy) and at the Electronics Department of the University Complutense of Madrid. Presently, he works at the Instituto de F´ısica Aplicada (IFA-CSIC) on several projects related to the development of sensors for volatile-compounds and pollutants detection. Juan Mariano Cabellos received his PhD in Chemistry in 2002. He is a researcher of the Instituto Madrile˜no de Investigaci´on y Desarrollo Rural Agrario y Alimentario (IMIDRA). His research interests are in the field of quality and composition of vineyard and wine. Ma Carmen Horrillo received her PhD in Chemistry from the University Complutense of Madrid in 1992. From 1993 to 1995 she was working at the Institute for Advanced Materials of the European Commission’s Joint Research Centre (Ispra, Italy). Since then she has been working at the Instituto de F´ısica Aplicada (CSIC) on I + D of chemical microsensors and electronic noses for environmental protection and quality control of foods. Since 1999 is head of the Department of Tecnolog´ıa de Gases y Superficies.