Application of electronic nose to beer recognition using supervised artificial neural networks Maryam Siadat and Etienne Losson
Mahdi Ghasemi-Varnamkhasti
Laboratoire de Conception, Optimisation et Modélisation des Systèmes, LCOMS, Université de Lorraine-Metz, 7 rue Marconi, 57070 Metz, France Email:
[email protected]
Department of Mechanical Engineering of Biosystems, Shahrekord University, Shahrekord, Iran Email:
[email protected]
Seyed Saeid Mohtasebi Department of Agricultural Machinery Engineering, University of Tehran, Karaj, Iran Email:
[email protected]
Abstract— Employment of electronic nose is drawing many attentions in brewery because of its unique capability in assessing multi-component analytes, which is largely feasible for traditional single-sensor devises. This study was aimed to recognize between alcoholic and non alcoholic beers by use of a MOS-based electronic nose system coupled with artificial neural networks (ANN) to evaluate the capability of the system for a binary discrimination. The PCA score plot of the two first principal components accounted for 78% of variance and clearly discrimination was observed. This observation was confirmed by ANN in such as way radial basis function (RBF) and Backpropagation (BP) showed satisfactory results to binary discrimination between two types of beer as 100 % of classification accuracy for both training and testing data sets. This result confirms the ability of the electronic nose to be used in future for other applications to beer evaluation in our project.
structure of the human nose. Fig. 1 shows the analogy between both systems. In both, the first step is the interaction between volatile compounds (usually a complex mixture) with the appropriate receptors: olfactory receptors in the biological nose and a sensor array in the case of the e-nose. One odorant receptor is sensible to multiple odorants and one odorant is detected by multiple odorant receptors. The next step is the storage of the signal generated by the receptors in the brain or in a pattern recognition database (learning stage) and later the identification of one of the odor stored (classification stage) [1]. An accepted definition of e-nose was given by Gardner and Bartlett [2]: An electronic nose is an instrument which includes an array of chemical sensors with partial specificity combined with an appropriate pattern recognition system for recognizing simple or complex odors.
Keywords—Electronic nose; Beer; Artificial neural networks; Food
I.
INTRODUCTION
Nowadays, volatile analyses in the food industry rely on two traditional techniques: conventional GC-MS and quality sensory panel analysis. Unfortunately, both techniques are too time consuming, expensive, and labor intensive for routine food quality control application. When thinking in applications of industrial quality controls, the necessity for the fast and high throughput analysis appreciably affects the type of analytical test and the instrumentation which can be used. Electronic nose system (machine olfaction) could be an alternative for the conventional systems to food authentication. The electronic nose (e-nose) is an electronic system that tries to imitate the
Fig. 1. Analogy between biological and artificial electronic nose.
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An e-nose uses currently a number of individual sensors (typically 5-100) whose selectivities towards different molecules overlap. The response from a chemical sensor is usually measured as the change of some physical parameter, e.g. conductivity or current. The response times for these devices range from seconds up to a few minutes. This is a significant drawback for these devices, and thus one of the main research topics in this field is to reduce the response time. A simple flow chart of the typical structure of an electronic nose is shown in Fig. 2. It generally consists of an aroma extraction system, that carries the aromatic compounds from the beer samples to the sensor chamber [3], a sensor array, capable of converting a chemical change into an electric signal, a control and measurement system that includes all electronic circuits needed for the measurements of signals generated by the sensors such as interface circuits, signal conditioning and A/D converters, and a pattern recognition method in order to perform a classification or prediction. As given in literature, many studies have shown the benefits and impact of electronic nose employment in monitoring the quality of food products [4-7]. Consumption of beer has been increasing trend in recent years, even in countries where alcoholic beverages are not habitual; therefore, there is a large demand for the fast and reliable methods to evaluate organoleptic characteristics such as the aroma and the flavor of beers [8]. The aroma of beer is important as a characteristic feature and for the quality of the product. Therefore, this fact is of great interest to the brewers to evaluate the aroma of beer in different stages of brewery line. During the last years, many attempts have been made about using the electronic nose in beer quality evaluation but because of many challenges arisen in this field of research, more studies are needed [9]. Advanced computational methods such as artificial neural networks is one the solutions to increase the capability of electronic nose in brewery. Neural network computing can be used if the problem is going to predict either response recorded on a continuous scale, or to do classifications. A neural network may be considered as a function mapping tool. It may also be employed to be a type of pattern recognition memory which can generalize on unknown samples. The design of the transfer function is crucial in the design of what kind of problems the user would like to solve. The most important advantage is that a possible solution to a non linear data problem has good chance of being a success.
Fig. 2. Typical block diagram of an electronic nose.
In recent years, artificial neural network (ANN) has been of interest to the researchers working on application of electronic nose to beverage aroma evaluation in such a way some papers on this subject have been reported in literature [10-13]. To date, no research is reported concerning employment of ANN to binary discrimination of beer (alcoholic and non alcoholic beers) showing the originality of this research. So, this study was aimed to use radial basis function (RBF) and backpropagation (BP) techniques for this purpose.
II.
MATERIALS AND METHODS
As illustrated in Fig. 3, a test chamber (corresponding to the sensor array block in Fig. 2 including five metal oxide semiconductor sensors provided by Figaro Engineering Inc. and FIS Inc. has been developed as sensing system. SPMW0, SPAQ1, TGS2620, TGS825, and TGS880 were used at the array. The sensors are placed in a half bridge and are supplied with a 10 V circuit voltage. A 5 V heating voltage to meet the operating temperature according to the Figaro Engineering and FIS data sheets is supplied. Also, a humidity sensor (Humirel Inc.) and a temperature sensor (National Semiconductor) were used in the array to control and monitor the ambient conditions. More detailed descriptions about the electronic nose system used in our laboratory have been presented previously [14]. Two different beer types were used in this research: alcoholic beers (4 brands) and non alcoholic beers (2 brands). The beer samples were prepared for the experiments according to the literature [15] and dynamic injection after static headspace sampling was considered in this study. The carrier gas was synthetic air for preserving the beer samples. The amount of 50 ml of beer sample were kept in a 250 ml bottle at 25 oC for 45 min in order to provide a vapor phase in equilibrium with the liquid. In this system, the synthetic air is brought into a sample container based on the bubbler principle and then mixed with the beer headspace and the mixture portion is, therefore, controlled by a mass flow controller.
Fig. 3. Test chamber including sensor array of the electronic nose used.
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These portions were determined as 100 and 80+20 ml/min respectively in purging and injection phases. For purging the sensor array, the electric valves were switched during 1300 seconds and dynamic injection of the beer headspace was then carried out for 360 seconds (Fig. 4). This procedure was performed randomly in 7 replicates for each beer brand. To remove the effects of sensors drift, sensor array were calibrated with a blank solution (4-11 % (v/v) (for different alcoholic beer brands) ethanol in deionised water). This approach has been used for wine in bibliography [16,17]. All the sensor output signals were collected by use of a data acquisition board (LabView, National Instruments). A fingerprint sample has been illustrated in Fig. 5.
Fig. 5. The sensor fingerprints of TGS 825 to alcoholic and non alcoholic brands.
Afterwards, the features corresponding to each sensor were extracted and the steady state of the signals was addressed in feature extraction [18]. Then, the following equation was used.
F where during
F
Rsample
(1)
Rcalibration
Rsample is the minimum resistance of the sensor performing
Rsample Rcalibration
the
measurement
protocol
and
is that of the sensor exposed to an ethanol
solution. Autoscaling as a data preprocessing technique was considered and the software of Matlab (The Mathworks Inc., Natick, MA, USA) was used to analyze the data collected and performing artificial neural network as well. In this work, principal components analysis (PCA) as a data reduction methodology is used to reduce the number of variables of the dataset and retaining most of the information in the data. Score and loading plots of the data are illustrated and the PCA results are then confirmed by artificial neural networks.
Fig. 4. The signals of the electronic nose studied: a) One cycle of injection and purging signal related to SPMW0 sensor, b) Stability of the instrumentation systems in successive measurements
A probabilistic neural network (PNN) is used for prediction purposes. The PNN is included three layers: the input one has three neurons, corresponding to the three principal components; the hidden layer, with radial basis transfer functions, has the same number of neurons that number of training vectors and a competitive layer in the output. For checking the performance of the network, leave one out (LOO) cross validation method is employed to the network. LOO consists of training N distinct nets (in this case, N is number of measurements) by using N−1 training vectors; while the validation of the trained net is performed by using the remaining vector, excluded from the training set. This procedure is repeated N times until all vectors are validated. Also, a backpropagation (BP) network topology is formed by
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three layers: the input layer has two neurons related to the first two components, a variable number in hidden layer, and two neurons in the output layer relevant to the two beer types. The network considers the inputs and compares its outputs in opposition to the desired outputs. Errors are then propagated back through the system, causing the system to adjust the weights that control the network. This process takes place over and over as the weights are repeatedly tweaked. During the network training, the same set of data is processed many times as the connection weights are always refined. III.
RESULTS AND DISCUSSIONS
As seen in PCA results (Fig. 6), alcoholic and non alcoholic types of beers are well identified. The maximum amount of variance in the original dataset (information) is accounted in the first two principal components. The components that account for a large variation in the data are considered as the new axis to obtain plots of the beer samples that this plot is known as score plot as given in Fig. 6a&b. The PCA score plot of the two first principal components accounts for 78% of variance. Clearly discriminated groups could be observed. The first group which appears in the left side of the score plot is related to the non alcoholic beer brands. Another group that appears on the right side of the score plot corresponds to alcoholic beer brands. Apart from the beer samples, the sensors (variables) could be displayed in the same plot by the values of their coefficients of the eigenvector equations, named loadings. As seen in Fig. 6c, the loadings plot shows the relative contribution of the sensors used in electronic nose system to each principal component: the higher the loading of a certain sensors (e.g TGS 880 and TGS 825) on a principal component, the more the sensor has in common with this component. Selecting the most important sensors contributing in clearly discriminated groups of beer is helpful while we want to consider the transient state of the sensors instead of steady state. For this goal, many variables depending on the technique used can be extracted and it is obvious that having the most important variables could have a significant role in computation stage of the data because sometimes considering many variables to data analysis maybe led to some problems like over fitting in analysis; for instance neural networks are normally simple to implement using a standard program with a user friendly interface. One problem is often that networks based on many input variables need a long computing time. Also, the loading plot of this data could give us this information that which temperature operation should be changed or which sensors could be removed from the array while we want to reduce the fabrication cost of the sensor array of the electronic nose system. The PCA results were confirmed with the ANN studied. In the training of the BP network, different number of neurons in the hidden layer has been tested in 5 proofs. Fig. 6. PCA plots of binary discrimination of beer by electronic nose: a) score plot of whole data b) score plot of training and testing data c) loading plot.
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Moreover, several activation functions (pureline, tansig and hard-limit) have been tested for the output layer. The optimal number turned out to be 11 neurons by several times tested and better results have been obtained by using the pureline activation function. The classification success was found to be 100 % for the training set in total and 100% for the testing set in total. The same results were obtained for RBF as full classification accuracy. The results obtained in this study showed the capability of the electronic system to recognize beer in terms of alcoholic and non alcoholic beers. Alcohol has a significant contribution in flavor attributes of beer in such a way the aroma compounds not only in terms of volatile compounds quality but also from concentration point of view are different. This means the electronic nose used in the current study could recognize these beer fingerprints. Signal changes caused by alcoholic beer brands were more than those of non alcoholic beer that this is in close agreement with the findings of Bartolome, Pena-Neira, & Gomez-Cordoves [19] who suggested that non alcoholic beer has a weaker aroma than alcoholic beer. They underscored that it seems reasonable to suspect that dealcoholization processes might also affect the phenolic composition of the beers. Getting discrimination capability with highest accuracy is more important when the electronic nose would be used to recognize among the beer samples with low difference in aroma. For instance, in brewery fermentation stage and after that, aging is very critical stage influencing in final quality of beer. In these stages and even after packaging, the changes in aroma of beer may be very little but important in generation of off-flavor in beer. One of the deep concerns in brewery for beer quality control is off flavor detection. An unexpected off flavor in beer is always a critical problem in brewery marketing and the governing commercial rules is that beers have to be free of defect [20]. Detection of off flavor may involve detection of presence of one or several compounds normally absent in beer such as 1-octen-3ol which brings mushroom odor, or the presence of normal flavor components in excessive concentrations such as diacetyl (2,3 butanedione) producing a buttery flavor, which leads to be produced if valine levels are low in the wort in brewing line. Having electronic nose systems with highest capability in classification could be helpful to detect these changes, subsequently monitoring beer in brewery and after that will be satisfactory. Aging fingerprint detection of beer after beer packaging is under consideration in our project that the relevant results will be published in near future. IV.
CONCLUSIONS
It is not surprising that significant efforts are being directed to the development of instrumental methods for routine analysis of aroma attributes of foodstuffs and beer in particular. An electronic nose was used to binary discrimination of aroma of beers. PCA analysis showed clearly separated groups of beers confirmed with ANN in such a way RBF and BP methods revealed 100% accuracy in both training and testing to binary discrimination. This study was a part of a project in our laboratory in which the aging fingerprint of alcoholic and non alcoholic beer is going to be detected by use of electronic nose system. Identification of aroma for these beer brands
encourages us to employ this system for other parts of the project in our laboratory in near future. ACKNOWLEDGMENTS The authors would like to thank Université de LorraineMetz and the staffs for all supports. The helps of Dr. Jesus Lozano are also appreciated. REFERENCES [1]
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