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Wine classification by taste sensors made from ultra-thin films and using neural networks
Sensors and Actuators B 98 (2004) 77–82
Wine classification by taste sensors made from ultra-thin films and using neural networks Antonio Riul Jr. a,∗ , Humberto C. de Sousa b , Roger R. Malmegrim c , David S. dos Santos Jr. d , André C.P.L.F. Carvalho b , Fernando J. Fonseca e , Osvaldo N. Oliveira Jr. d , Luiz H.C. Mattoso c a
Depto de F´ısica, Qu´ımica e Biologia, FCT-UNESP, CP 467,19060-900 Presidente Prudente, SP, Brazil Instituto de Ciˆencias Matemáticas e de Computação, USP, CP 668, 13560-970 São Carlos, SP, Brazil c EMBRAPA Instrumentação Agropecuária, CP 741, 13.560-970 São Carlos, SP, Brazil d Instituto de F´ısica de São Carlos, USP, CP 369, CEP 13.560-970 São Carlos, SP, Brazil e Escola Politécnica da Universidade de São Paulo, USP, CEP 05508-900 São Paulo, SP, Brazil
b
Received 3 June 2003; received in revised form 12 September 2003; accepted 22 September 2003
Abstract This paper reports on a sensor array able to distinguish tastes and used to classify red wines. The array comprises sensing units made from Langmuir–Blodgett (LB) films of conducting polymers and lipids and layer-by-layer (LBL) films from chitosan deposited onto gold interdigitated electrodes. Using impedance spectroscopy as the principle of detection, we show that distinct clusters can be identified in principal component analysis (PCA) plots for six types of red wine. Distinction can be made with regard to vintage, vineyard and brands of the red wine. Furthermore, if the data are treated with artificial neural networks (ANNs), this “artificial tongue” can identify wine samples stored under different conditions. This is illustrated by considering 900 wine samples, obtained with 30 measurements for each of the five bottles of the six wines, which could be recognised with 100% accuracy using the algorithms Standard Backpropagation and Backpropagation momentum in the ANNs. © 2003 Elsevier B.V. All rights reserved. Keywords: Conducting polymers; Taste; Wines; Artificial neural networks; Electronic tongue
1. Introduction Taste is the less understood of the human senses [1–10]. There are several perception mechanisms involved in the taste recognition process made by humans, thus making it difficult to understand how the brain “knows” what the mouth tastes. Nevertheless, there is consensus that the biological system cannot discriminate each chemical substance present in beverages and foodstuffs. Rather, the gustatory system groups all the information received in distinctly different patterns of response produced by nerve cells that are not exclusively labelled for a particular sensation but ensemble to encode taste quality [1–10]. The development of artificial taste sensors (also called “electronic tongues”) able to differentiate tastes, mimicking the human tongue, is a feasible endeavour. This device allows a quantitative treat-
∗ Corresponding author. Tel.: +55-16-273-9825; fax: +55-16-271-5365. E-mail address: [email protected] (A. Riul Jr.).
0925-4005/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2003.09.025
ment of “tastes” and does not suffer from lack of sensitivity due to long term exposure to a certain substance, unlike the case of humans. Several examples exist in the literature based on potentiometry [11–26] and voltammetry [27–32]. These electronic tongues use arrays of non-specific sensors that can respond differently to analytes encompassing a response pattern closely related to the characteristics of the sample analysed. A comparison between potentiometric and voltammetric taste sensors is presented in [30]; despite the use of fundamentally different techniques, the ability to distinguish samples is not significantly different. We have recently reported a taste sensor that has been proven advantageous in comparison with its counterparts. The sensor array comprises ultra-thin films of conducting polymers and other materials as transducers, with ac measurements being used as the physical principle of detection [33–37]. The use of impedance spectroscopy measurements in gas detection has been shown to be more efficient than the dc technique [38–40]. It has permitted a reduction in the number of sensing units used and consequently in the whole
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Fig. 1. Block diagram of the procedures for identifying the wines.
size of the device, leading to more accurate measurements [40]. Further advantages of sensors based on ac measurements are: (i) the taste sensor based on potentiometry has limited sensitivity for non-electrolyte substances [11,16] and needs to be optimised in order to achieve a sensitivity close to that of humans [13]; (ii) the use of voltammetry in complex liquid media provides complicated spectra whose interpretation is not trivial, and requires redox active compounds to be either reduced or oxidised at the working electrode; and (iii) there is no requirement of active species in the measuring system and unlike the case of the other methods no need of a standard reference electrode which might be troublesome in many practical applications because a reliable reference is a critical issue in miniaturised sensor arrays [23]. High sensitivity is achieved when interdigitated electrodes are coated with nanostructured thin films (∼2 nm thick per deposited layer), which are able to detect very small changes in conductivity and dielectric properties of the materials comprising individual sensing units in contact with a liquid medium [33–37]. It was shown that low levels of impurities in water could be detected, in addition to distinguishing tastes and beverages below the human threshold [33,35,36]. For the sake of comparison, the lowest concentrations found in the literature roughly correspond to the human limit of perception [12]. Fig. 1 brings a schematic illustration of the process involved here using taste sensors in the analysis of red wine samples.
medium these nanostructured films provide a “fingerprint” of the taste through impedance measurements, as previously reported [33–37]. The sensing units were dipped into a flask containing 15 ml of a given wine, and the ac impedance was recorded with a Solartron 1260A. The responses were analysed at 1 kHz, because this frequency region is the most sensitive to changes in the film properties [35,43], with the capacitance being the figure of merit. Briefly, the double layer effect dominates the electrical signal at low frequencies, while the presence of the film coating the electrodes is manifested between 100 and 104 Hz. At frequencies above 105 Hz, the impedance is dominated mainly by the geometric capacitance of the electrodes [43]. When the sensor units are washed with copious amounts of water, the original impedance response is fully recovered, and the sensor can be re-used for more than 1 year without loss of sensitivity [33–37]. In order to warrant reproducibility, measurements were performed according to the following procedure: they were taken for a given wine bottle within approximately 3 h after its opening, with the capacitance results possessing a standard deviation of less than 1%. For the data taken several weeks after opening the wine bottle, the standard deviation is approximately 5%. Because data were collected for several samples of the same wine and with various sensing units, we employed principal component analysis (PCA), which is suited to statistically correlate the samples, decreasing the dimensionality of the original data and avoiding redundancy of information [44].
2. Experimental details Different sensing units made from five-layer Langmuir– Blodgett (LB) films from polypyrrole (PPy), 16-mer polyaniline (16-mer), stearic acid (SA), mixtures of PPy/SA, 16-mer/SA, PPy/16mer and a layer-by-layer film from chitosan alternated with poly(styrenesulfonic acid) were deposited onto interdigitated gold electrodes in a class 10,000 clean room. The monolayers were formed onto ultrapure water supplied by a Milli-Q system, compressed at a constant barrier speed of 10 mm/min until a target pressure of 25 mN/m was reached. At this pressure, the monolayer was transferred at a dipping speed of 2 mm/min using a KSV 5000 system. A detailed analysis of these LB films can be found elsewhere [41–43]. When in contact with a liquid
3. Results and discussion A PCA plot of the wines analysed is shown in Fig. 2 indicating that clusters can be identified for the wines. For example, distinct clusters can be established for red wines: (i) of the same vintage (2000) and variety (Cabernet Sauvignon)—samples A and C; (ii) same variety, but different vintages (2000 and 1999)—samples A and B; and (iii) same vintage (2000), but different varietals (Cabernet Sauvignon and Cabernet)—samples C and D. Fig. 2 also shows two less expensive wines (table wines), referred to as samples E ( ) and F ( ), in which sugar and conservatives are added for controlling taste and other features. The
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Fig. 2. PCA plot distinguishing six red wines with the bottles stored under the same conditions: () sample A (Cabernet Sauvignon 2000 from producer 1); (䊊) sample B (Cabernet Sauvignon 1999 from producer 1); (䊐) sample C (Cabernet Sauvignon 2000 from producer 2); () sample D (Cabernet 2000 from producer 2); ( ) sample E (table wine from producer 1); ( ) sample F (table wine from producer 3).
dispersion among different bottles of samples E and F is smaller than those observed for the other wines, which is probably due to the addition of chemicals. Wines produced with no such additives are likely to show a larger dispersion. A cluster analysis using the Euclidean distance among the three first principal components is shown in Fig. 3. This allows a more general analysis of the data, which also include results for the wines measured on different days. For example, sample D b5 in Fig. 3 represents the normalised data
comprising the average of 30 measurements acquired from bottle number 5 of sample D. Some interesting features were observed: (i) the 1999 vintage (samples B) differs considerably from that of 2000 (samples A) for the same variety and winemaker due to the “El Niño” effect, which in 1999 caused a decrease in the rain precipitation favouring the budding and fruit ripening of delayed maturation grapes, such as Cabernet Sauvignon; (ii) wines from different grapes from the 2000 vintage (samples A and C) are closer to each other
Fig. 3. Tree diagram of the Euclidean distances considering the first three principal components. In the x-axis, the samples are labelled according to the wine (from A to F) and considering different bottles (b1–b5 for a given wine). Also represented are data for wine samples from bottles opened weeks before the measurement.
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than wines from the same grape from different vintages; (iii) 8 weeks after being opened, a good wine (sample A) was classified close to a table wine (sample E). This is because ageing of a good wine causes oxidation, thus increasing the wine acidity which is an important characteristic of the table wines investigated; and (iv) three bottles acquired in the same wine store (samples D b4, D b5 and C b5) gave unexpected results for their type of wine, probably because storage conditions were not ideal in the store. Interesting as it may be, PCA is only useful to identify clusters and distinguish a handful of samples at a time. If the number of wines to be investigated increased beyond 10 or 20, the identification of clusters in a crowded PCA plot would be very difficult, let alone tell the wines apart. For example, when the electrical response of samples from bottles stored under different conditions of temperature and humidity, as well as those aged after being opened, is also considered, the data shown in a PCA plot do not provide satisfactory differentiation, as demonstrated in Fig. 4. Yet, one would need, ideally, a complete library with the response of wines. Given a sample of any wine already studied, one could then determine its brand, variety and vintage. Therefore, we need to go beyond PCA analysis for this purpose. Here we employ artificial neural networks (ANNs) to discriminate wines based on collected data, as used in related applications [14,20,23,25,45–53]. ANNs are distributed computing systems composed by processing units connected by weighted links. These units can be assembled in one or more layers [54], simulating the structure and functioning of the brain. One of ANNs’ main advantages is their ability to learn from data, through training
algorithms. There are several ANNs models, the most popular being the multi-layer perceptron (MLP) networks [54], which were used in the experiments performed. In order to evaluate the influence of the training algorithm in the performance achieved, MLP networks with 5, 10, 15, 20 and 25 hidden nodes were trained using four different algorithms: Standard Backpropagation; Rprop; Quickprop; and Backpropagation Momentum [54]. The simulations were carried out using the Stuttgart neural network simulator (SNNS) tool [55]. The normalised data presented to the ANNs comprise 30 measurements acquired from five different bottles for each wine (wines A–F), totalling 900 samples. The experiments used cross-validation with early stop [55]. Accordingly, the experiments were conducted ten times for each network; each time the data set was shuffled and randomly divided into training, validation and test subsets, with 70, 20 and 10% of the samples, respectively. The test samples were not used during the networks training. A very good accuracy was achieved. For the algorithms Standard Backpropagation and Backpropagation momentum, accuracy was 100% for any number of neurons in the hidden layer. For the Rprop algorithm, accuracy varied from 99 to 100%, while for the Quickprop algorithm, accuracy increased from 88% for five neurons in the hidden layer to more than 99% for higher numbers of neurons. It is important to stress that the samples considered for estimating the accuracy also included those that led to a crowded plot in Fig. 4, i.e. the samples stored under different conditions were all considered and the ANN system was still able to identify the wines correctly. In order to identify aged wines, in particular, information on the time of measurements was provided to the ANN system. Note
Fig. 4. PCA plot for six red wines with the bottles stored under different conditions: () sample A (Cabernet Sauvignon 2000 from producer 1); (䊊) sample B (Cabernet Sauvignon 1999 from producer 1); (䊐) sample C (Cabernet Sauvignon 2000 from producer 2); () sample D (Cabernet 2000 from producer 2); ( ) sample E (table wine from producer 1); ( ) sample F (table wine from producer 3).
A. Riul Jr. et al. / Sensors and Actuators B 98 (2004) 77–82
that related papers using ANNs in the analysis of complex liquid systems quote accuracies ranging from 75 to 95% [14,20,23,25]. Based on these results, we are confident that with the combination of a suitable set of sensing units the identifying capability of the tongue will continue to be high for a larger number of wine samples. Preliminary experiments carried out with different brands of coffee and water have also pointed to a high classification accuracy. The electrical changes observed while submitting the sensing units to liquid samples are reversible and the units may be re-used for more than a year, even when tested with different types of samples [33–37].
4. Conclusions In conclusion, an electronic tongue based on nanostructured films has been able to identify different red wines with extraordinary accuracy. The sensor could easily distinguish samples according to the varietal, vintage and producer with no need of human experts or complex and expensive laboratory analytical tools. The reasons for the successful results lie in the use of ultra-thin films of conducting polymers mixed with different materials, whose response to distinct tastes varies in such a way that a high resolving power is rendered to the tongue. The use of artificial neural networks to treat the data obtained from a set of sensing units allows high accuracy in the sample recognition process even in cases involving aged samples where a PCA plot could not provide reasonable distinction among samples. Even though the sensor is normally called “electronic tongue”, it may be used well beyond the realms of beverage quality control. It is envisaged that the high sensitivity demonstrated here may be used in blood tests and analysis of any liquid media where a quality control monitoring or classification procedure is required.
Acknowledgements The authors are grateful to FAPESP and CNPq (Brazil) for the financial support. They thank Dr. Arthur P. Azevedo (Vice-President of the Brazilian Association of Sommeliers) and Dr. Clovis C. Biscegli (EMBRAPA) for providing technical information about the wines.
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Wine classification by taste sensors made from ultra-thin films and using neural networks Antonio Riul Jr. a,∗ , Humberto C. de Sousa b , Roger R. Malmegrim c , David S. dos Santos Jr. d , André C.P.L.F. Carvalho b , Fernando J. Fonseca e , Osvaldo N. Oliveira Jr. d , Luiz H.C. Mattoso c a
Depto de F´ısica, Qu´ımica e Biologia, FCT-UNESP, CP 467,19060-900 Presidente Prudente, SP, Brazil Instituto de Ciˆencias Matemáticas e de Computação, USP, CP 668, 13560-970 São Carlos, SP, Brazil c EMBRAPA Instrumentação Agropecuária, CP 741, 13.560-970 São Carlos, SP, Brazil d Instituto de F´ısica de São Carlos, USP, CP 369, CEP 13.560-970 São Carlos, SP, Brazil e Escola Politécnica da Universidade de São Paulo, USP, CEP 05508-900 São Paulo, SP, Brazil
b
Received 3 June 2003; received in revised form 12 September 2003; accepted 22 September 2003
Abstract This paper reports on a sensor array able to distinguish tastes and used to classify red wines. The array comprises sensing units made from Langmuir–Blodgett (LB) films of conducting polymers and lipids and layer-by-layer (LBL) films from chitosan deposited onto gold interdigitated electrodes. Using impedance spectroscopy as the principle of detection, we show that distinct clusters can be identified in principal component analysis (PCA) plots for six types of red wine. Distinction can be made with regard to vintage, vineyard and brands of the red wine. Furthermore, if the data are treated with artificial neural networks (ANNs), this “artificial tongue” can identify wine samples stored under different conditions. This is illustrated by considering 900 wine samples, obtained with 30 measurements for each of the five bottles of the six wines, which could be recognised with 100% accuracy using the algorithms Standard Backpropagation and Backpropagation momentum in the ANNs. © 2003 Elsevier B.V. All rights reserved. Keywords: Conducting polymers; Taste; Wines; Artificial neural networks; Electronic tongue
1. Introduction Taste is the less understood of the human senses [1–10]. There are several perception mechanisms involved in the taste recognition process made by humans, thus making it difficult to understand how the brain “knows” what the mouth tastes. Nevertheless, there is consensus that the biological system cannot discriminate each chemical substance present in beverages and foodstuffs. Rather, the gustatory system groups all the information received in distinctly different patterns of response produced by nerve cells that are not exclusively labelled for a particular sensation but ensemble to encode taste quality [1–10]. The development of artificial taste sensors (also called “electronic tongues”) able to differentiate tastes, mimicking the human tongue, is a feasible endeavour. This device allows a quantitative treat-
∗ Corresponding author. Tel.: +55-16-273-9825; fax: +55-16-271-5365. E-mail address: [email protected] (A. Riul Jr.).
0925-4005/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2003.09.025
ment of “tastes” and does not suffer from lack of sensitivity due to long term exposure to a certain substance, unlike the case of humans. Several examples exist in the literature based on potentiometry [11–26] and voltammetry [27–32]. These electronic tongues use arrays of non-specific sensors that can respond differently to analytes encompassing a response pattern closely related to the characteristics of the sample analysed. A comparison between potentiometric and voltammetric taste sensors is presented in [30]; despite the use of fundamentally different techniques, the ability to distinguish samples is not significantly different. We have recently reported a taste sensor that has been proven advantageous in comparison with its counterparts. The sensor array comprises ultra-thin films of conducting polymers and other materials as transducers, with ac measurements being used as the physical principle of detection [33–37]. The use of impedance spectroscopy measurements in gas detection has been shown to be more efficient than the dc technique [38–40]. It has permitted a reduction in the number of sensing units used and consequently in the whole
78
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Fig. 1. Block diagram of the procedures for identifying the wines.
size of the device, leading to more accurate measurements [40]. Further advantages of sensors based on ac measurements are: (i) the taste sensor based on potentiometry has limited sensitivity for non-electrolyte substances [11,16] and needs to be optimised in order to achieve a sensitivity close to that of humans [13]; (ii) the use of voltammetry in complex liquid media provides complicated spectra whose interpretation is not trivial, and requires redox active compounds to be either reduced or oxidised at the working electrode; and (iii) there is no requirement of active species in the measuring system and unlike the case of the other methods no need of a standard reference electrode which might be troublesome in many practical applications because a reliable reference is a critical issue in miniaturised sensor arrays [23]. High sensitivity is achieved when interdigitated electrodes are coated with nanostructured thin films (∼2 nm thick per deposited layer), which are able to detect very small changes in conductivity and dielectric properties of the materials comprising individual sensing units in contact with a liquid medium [33–37]. It was shown that low levels of impurities in water could be detected, in addition to distinguishing tastes and beverages below the human threshold [33,35,36]. For the sake of comparison, the lowest concentrations found in the literature roughly correspond to the human limit of perception [12]. Fig. 1 brings a schematic illustration of the process involved here using taste sensors in the analysis of red wine samples.
medium these nanostructured films provide a “fingerprint” of the taste through impedance measurements, as previously reported [33–37]. The sensing units were dipped into a flask containing 15 ml of a given wine, and the ac impedance was recorded with a Solartron 1260A. The responses were analysed at 1 kHz, because this frequency region is the most sensitive to changes in the film properties [35,43], with the capacitance being the figure of merit. Briefly, the double layer effect dominates the electrical signal at low frequencies, while the presence of the film coating the electrodes is manifested between 100 and 104 Hz. At frequencies above 105 Hz, the impedance is dominated mainly by the geometric capacitance of the electrodes [43]. When the sensor units are washed with copious amounts of water, the original impedance response is fully recovered, and the sensor can be re-used for more than 1 year without loss of sensitivity [33–37]. In order to warrant reproducibility, measurements were performed according to the following procedure: they were taken for a given wine bottle within approximately 3 h after its opening, with the capacitance results possessing a standard deviation of less than 1%. For the data taken several weeks after opening the wine bottle, the standard deviation is approximately 5%. Because data were collected for several samples of the same wine and with various sensing units, we employed principal component analysis (PCA), which is suited to statistically correlate the samples, decreasing the dimensionality of the original data and avoiding redundancy of information [44].
2. Experimental details Different sensing units made from five-layer Langmuir– Blodgett (LB) films from polypyrrole (PPy), 16-mer polyaniline (16-mer), stearic acid (SA), mixtures of PPy/SA, 16-mer/SA, PPy/16mer and a layer-by-layer film from chitosan alternated with poly(styrenesulfonic acid) were deposited onto interdigitated gold electrodes in a class 10,000 clean room. The monolayers were formed onto ultrapure water supplied by a Milli-Q system, compressed at a constant barrier speed of 10 mm/min until a target pressure of 25 mN/m was reached. At this pressure, the monolayer was transferred at a dipping speed of 2 mm/min using a KSV 5000 system. A detailed analysis of these LB films can be found elsewhere [41–43]. When in contact with a liquid
3. Results and discussion A PCA plot of the wines analysed is shown in Fig. 2 indicating that clusters can be identified for the wines. For example, distinct clusters can be established for red wines: (i) of the same vintage (2000) and variety (Cabernet Sauvignon)—samples A and C; (ii) same variety, but different vintages (2000 and 1999)—samples A and B; and (iii) same vintage (2000), but different varietals (Cabernet Sauvignon and Cabernet)—samples C and D. Fig. 2 also shows two less expensive wines (table wines), referred to as samples E ( ) and F ( ), in which sugar and conservatives are added for controlling taste and other features. The
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Fig. 2. PCA plot distinguishing six red wines with the bottles stored under the same conditions: () sample A (Cabernet Sauvignon 2000 from producer 1); (䊊) sample B (Cabernet Sauvignon 1999 from producer 1); (䊐) sample C (Cabernet Sauvignon 2000 from producer 2); () sample D (Cabernet 2000 from producer 2); ( ) sample E (table wine from producer 1); ( ) sample F (table wine from producer 3).
dispersion among different bottles of samples E and F is smaller than those observed for the other wines, which is probably due to the addition of chemicals. Wines produced with no such additives are likely to show a larger dispersion. A cluster analysis using the Euclidean distance among the three first principal components is shown in Fig. 3. This allows a more general analysis of the data, which also include results for the wines measured on different days. For example, sample D b5 in Fig. 3 represents the normalised data
comprising the average of 30 measurements acquired from bottle number 5 of sample D. Some interesting features were observed: (i) the 1999 vintage (samples B) differs considerably from that of 2000 (samples A) for the same variety and winemaker due to the “El Niño” effect, which in 1999 caused a decrease in the rain precipitation favouring the budding and fruit ripening of delayed maturation grapes, such as Cabernet Sauvignon; (ii) wines from different grapes from the 2000 vintage (samples A and C) are closer to each other
Fig. 3. Tree diagram of the Euclidean distances considering the first three principal components. In the x-axis, the samples are labelled according to the wine (from A to F) and considering different bottles (b1–b5 for a given wine). Also represented are data for wine samples from bottles opened weeks before the measurement.
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than wines from the same grape from different vintages; (iii) 8 weeks after being opened, a good wine (sample A) was classified close to a table wine (sample E). This is because ageing of a good wine causes oxidation, thus increasing the wine acidity which is an important characteristic of the table wines investigated; and (iv) three bottles acquired in the same wine store (samples D b4, D b5 and C b5) gave unexpected results for their type of wine, probably because storage conditions were not ideal in the store. Interesting as it may be, PCA is only useful to identify clusters and distinguish a handful of samples at a time. If the number of wines to be investigated increased beyond 10 or 20, the identification of clusters in a crowded PCA plot would be very difficult, let alone tell the wines apart. For example, when the electrical response of samples from bottles stored under different conditions of temperature and humidity, as well as those aged after being opened, is also considered, the data shown in a PCA plot do not provide satisfactory differentiation, as demonstrated in Fig. 4. Yet, one would need, ideally, a complete library with the response of wines. Given a sample of any wine already studied, one could then determine its brand, variety and vintage. Therefore, we need to go beyond PCA analysis for this purpose. Here we employ artificial neural networks (ANNs) to discriminate wines based on collected data, as used in related applications [14,20,23,25,45–53]. ANNs are distributed computing systems composed by processing units connected by weighted links. These units can be assembled in one or more layers [54], simulating the structure and functioning of the brain. One of ANNs’ main advantages is their ability to learn from data, through training
algorithms. There are several ANNs models, the most popular being the multi-layer perceptron (MLP) networks [54], which were used in the experiments performed. In order to evaluate the influence of the training algorithm in the performance achieved, MLP networks with 5, 10, 15, 20 and 25 hidden nodes were trained using four different algorithms: Standard Backpropagation; Rprop; Quickprop; and Backpropagation Momentum [54]. The simulations were carried out using the Stuttgart neural network simulator (SNNS) tool [55]. The normalised data presented to the ANNs comprise 30 measurements acquired from five different bottles for each wine (wines A–F), totalling 900 samples. The experiments used cross-validation with early stop [55]. Accordingly, the experiments were conducted ten times for each network; each time the data set was shuffled and randomly divided into training, validation and test subsets, with 70, 20 and 10% of the samples, respectively. The test samples were not used during the networks training. A very good accuracy was achieved. For the algorithms Standard Backpropagation and Backpropagation momentum, accuracy was 100% for any number of neurons in the hidden layer. For the Rprop algorithm, accuracy varied from 99 to 100%, while for the Quickprop algorithm, accuracy increased from 88% for five neurons in the hidden layer to more than 99% for higher numbers of neurons. It is important to stress that the samples considered for estimating the accuracy also included those that led to a crowded plot in Fig. 4, i.e. the samples stored under different conditions were all considered and the ANN system was still able to identify the wines correctly. In order to identify aged wines, in particular, information on the time of measurements was provided to the ANN system. Note
Fig. 4. PCA plot for six red wines with the bottles stored under different conditions: () sample A (Cabernet Sauvignon 2000 from producer 1); (䊊) sample B (Cabernet Sauvignon 1999 from producer 1); (䊐) sample C (Cabernet Sauvignon 2000 from producer 2); () sample D (Cabernet 2000 from producer 2); ( ) sample E (table wine from producer 1); ( ) sample F (table wine from producer 3).
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that related papers using ANNs in the analysis of complex liquid systems quote accuracies ranging from 75 to 95% [14,20,23,25]. Based on these results, we are confident that with the combination of a suitable set of sensing units the identifying capability of the tongue will continue to be high for a larger number of wine samples. Preliminary experiments carried out with different brands of coffee and water have also pointed to a high classification accuracy. The electrical changes observed while submitting the sensing units to liquid samples are reversible and the units may be re-used for more than a year, even when tested with different types of samples [33–37].
4. Conclusions In conclusion, an electronic tongue based on nanostructured films has been able to identify different red wines with extraordinary accuracy. The sensor could easily distinguish samples according to the varietal, vintage and producer with no need of human experts or complex and expensive laboratory analytical tools. The reasons for the successful results lie in the use of ultra-thin films of conducting polymers mixed with different materials, whose response to distinct tastes varies in such a way that a high resolving power is rendered to the tongue. The use of artificial neural networks to treat the data obtained from a set of sensing units allows high accuracy in the sample recognition process even in cases involving aged samples where a PCA plot could not provide reasonable distinction among samples. Even though the sensor is normally called “electronic tongue”, it may be used well beyond the realms of beverage quality control. It is envisaged that the high sensitivity demonstrated here may be used in blood tests and analysis of any liquid media where a quality control monitoring or classification procedure is required.
Acknowledgements The authors are grateful to FAPESP and CNPq (Brazil) for the financial support. They thank Dr. Arthur P. Azevedo (Vice-President of the Brazilian Association of Sommeliers) and Dr. Clovis C. Biscegli (EMBRAPA) for providing technical information about the wines.
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