Machine Olfaction: Pattern Recognition for the

Machine Olfaction: Pattern Recognition for the identification of aromas A. Perera*, A. Gomez-Baena, T. Sundic, T. Pardo, S. Marco CEMIC- Instrumentation and Communication Systems, University of Barcelona, Spain [email protected]

Abstract This paper describes a portable Pattern Recognition System (PRS) based on embedded technology for intelligent volatile detection (Electronic Nose). This instrument is designed to hold advanced signal processing and digital communications services in a contained size. A summary of the hardware is presented followed by an application to the identification of extra virgin olive oils. The instrument of the example is able to classify eleven different classes of Spanish olive oil with a 79% of accuracy and relatively simple pattern recognition techniques. Figure 1. Electronic nose schematic

1. Introduction An electronic nose is defined as an instrument that using non-specific gas sensors is able to discriminate different compounds by means of a pattern recognition system[1]. The aim of such an artificial olfaction machine design is to imitate the information processing within the biological olfactory system[2]. The olfactory system in humans is composed of three elements: olfactory receptors, olfactory bulb and olfactory cortex. Odor is sampled and taken to the central nasal cavity where it diffuses through a layer of mucus to bind with the chemical sensitive membranes of the olfactory receptor cells in the epithelium. The area covered by the epithelium is about 5 cm2 where 107-108 receptors are distributed along the tissue [3]. It is important to note that about 103 different olfactory receptors have been identified and humans can distinguish about 104 different smells. Moreover it is known that this 103 receptors are not selective, but they have are overlapped partial specificity. It is clear that data fusion is performed by the olfactory system. We may imitate the biological olfaction by means of a process including an odor delivery system (sampling system), an array of chemically sensitive sensors with partial specificity to a range of odorants and a PARC (pattern recognition engine)[4]. The pattern recognition stage usually comprises feature extraction/selection, a dimensionality reduction stage and a classifier. Although there are many sensing

materials available, semiconductor based gas sensors are quite common in this approach[5]. Pattern recognition in electronic noses is a difficult task because of the high number of factors that affects the variance in sensor data. This variance is usually caused by different nature factors like long-term drift in sensors, sampling procedure variations, low signals of the components responsible of odor discrimination, variations in ambient humidity and temperature, etc… Datasets trend to contain a high variance not related with our odor recognition problem. Furthermore, experimental procedures are time consuming and costly, and training databases are usually small with high dimensionality. It is the purpose of this paper to present a PRS, working as an electronic nose, based on Embedded PC technology and GNU/Linux operating system. While this instrument still features simple headspace sampling, it holds many smart features in a low size instrument taking advantage of the state of the art computer platforms. This paper is divided as follows. Section 2 will give a detailed overview of the hardware and software implementation object of this work. Section 3 will focus on the olive oil application of finally some conclusions will be presented.

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2. System description Regarding instrument intelligence and smart operation it is important to review the software issues concerning this basic part of the instrument. Software solution and implementation form will depend in a strong way on the computing resources available. We can consider many possibilities concerning the system implementation depending on the required instrument size and final application. These scenarios sweep from desk systems to portable systems and limit the available resources for recognition algorithms. For platforms with limited computing power some algorithms should be trained off-line, usually in a host computer, and parameters delivered to the system using appropriate digital communications. For more powerful platforms the same instrument will be able to adapt their data processing scheme to the problem of interest. If some learning engine has to be implemented in a portable device, computing complexity is an issue to be taken in account in the system design. We will have limitations in speed and often in memory. Our implementation uses an embedded computer system based on the PC/104 standard. This consortium defines compact size self-stacking modules (3.6 x 3.8 in) and a PCI bus across the stack. A small data-acquisition and relay board provides sufficient I/O to control the enose and acquire the signals. An embedded version of GNU/Linux was selected as operating system (OS). The OS is held in a solid-state hard drive. This device allows the system to be used as a portable intelligent volatile detector, as well as a data-acquisition instrument for further processing in laboratory. A picture of the system is shown in fig 2. The electronics can interface various commercial sensors, including FIS Inc. (Japan), FIGARO Inc. (Japan), MICROSENS (Switzerland) , MICS (Switzerland) or CAPTEUR (UK) via configuration jumpers. In the configuration for this work only seven FIS sensors are used (SB-15, SB-19, SB31, SB-95, SB95 (without filter), SP-31, SP-32) plus a temperature sensor and a capacitive humidity sensor RH1 from Philips. The SB series present an internal structure based on a micro-bead of sensing material deposited over a coil. This structure provides the sensors with a fast thermal response to a modulating heater voltage. The instrument design, either in hardware, software and signal processing, is aimed to allow sensor parameter modulation for better discrimination[6]. In current configuration we modulate the voltage applied to SB sensors heaters, which can be considered as a pseudo-temperature modulation. This is very important since the sensor response depends strongly on temperature. This temperature sweep delivers more information from every sensor but provides high dimensionality patterns. The flow injection system consists of a programmable eight-channel manifold with corresponding electro-

Figure 2. Electronic nose schematic valves for each intake port. A reference channel is defined and includes a zero-filter for air reference.

3. Application: identification of aromas of extra virgin Spanish olive oils In order to illustrate the instrument capabilities here we present the application of the instrument to the recognition of different olive oil samples from its aroma. A similar problem has been studied by Pardo et al. [7]. In the last decade a great effort has been developed in Spain to the development of high quality extra virgin olive oils. While the use of olive oil is traditional in Spain, only recently the general public has enough awareness about the existence of different olive varieties, and the corresponding differences in flavor and aroma. The particular olive oils that have been selected for the present study are summarized in table 1. Table 1. Summary of olive oil types Class 1 2 3 4 5 6 7 8 9 10 11

Olive Variety Arbequina Picual Arbequina Picual Hojiblanca Picual ? Hojiblanca Arbequina Cornicabra Empeltre

Comercial brand Moncel (SP) Borges (SP) Germanor (SP) Sierra Mágina(SP) Borges (SP) 50 Siglos (SP) De Cecco (I) Selección (SP) Sabor d’abans(SP) Hacienda Guzmán (SP) Reales Almazaras (SP)

Ten of them are coming from Spain and are of monovarietal character while one of them is a well-known italian brand.

3.1. Experimental In order to analyze the aroma of these samples, 40 cm3 of each samples was located in a 90 cm3 glass bottle.

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Every bottle has an inlet and an outlet. The inlet is connected to a charcoal filter while the outlet is directly connected to the sensor chamber. The sampling procedure consists on 45 min cycles. The programmable electro valve manifold selects every 45 min a randomly chosen bottle. A filtered air stream carries the headspace to the sensor chamber provoking a sensor transient response. This 45 min sampling is divided in 5 min sampling the reference channel, 7 min sampling the bottle headspace and 33 min purging with filtered air. In most cases, after 12 min purging the sensor signals had recovered his baseline value. During the whole experiment the gas flow through the sensor chamber was fixed to 100 cm3/min. Sensor signals were sampled at 4 Hz. The database consists of 208 measurements: 19 measurements per class. Typical sensor waveforms can be observed in figure 4.

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3.2. Processing Basic preprocessing consisted on signal decimation a factor 200 after filtering the signal with a 4th order Butterworth IIR filter. The new equivalent sampling frequency is 20 mHz. To build the input pattern we truncate the sensor response from 300s to 950s to a total of 14 samples per channel. This interval comprises the sampling of the bottle and the initial part of the purging phase, where it can be argued that the oil volatiles are still present in the sensor chamber. Adding the mean temperature during the measurement cycle as an additional feature the feature input space has dimension 99. Note that the response of the humidity sensor has been disregarded because no appreciable signal has been observed as expected when sampling oils volatiles. Conventional PCA has been applied for feature extraction as well as for visualization purposes. Fig. 3 presents the data projected to the PCA place. The first two principal components explain 89% of the total variance. As it can be observed in the PCA place clusters appear completely confused indicating the necessity of pattern recognition for reliable class identification. In order to obtain 99% of the input variance 8 principal components are necessary. However, leave-one-out cross-validation (loo) indicates that the optimum number of principal components in terms of minimum error variance is 21. Despite a factor five reduction in the number of principal components, this seems still a large dimensionality for the input space taking into account that we have only 19 patterns per class. Alternatively, we have used a simple 1-NN and a quadratic classifier to select the optimum number principal components. Based on loo validation the optimum number is 15 for 1-NN and 12 for the quadratic classifier. However, this simple classifiers on the projected PCA space deliver quite poor classification performances: 45% and 44% success respectively. If we use, Linear Discriminant Analysis directly over the input space, the optimum

number of dimensions is 10 that is the maximum attainable since we have 11 classes. The classification rate improves up to 70% and 61% respectively. A hierarchical procedure using both kinds of feature extraction could deliver better results. In fact using PCA followed to a LDA projection to 10 dimensions, leads to a 79% classification success for 25 principal components retained, using a simple 1-NN classifier. To illustrate the importance of the feature extraction stage we have performed the sample processing to three different datasets as initial feature set. Each set case is built by using three types of features extracted as the signal value for all sensors at given times. Examples of the three methods are shown in fig 4. For each set the significance of each principal component analysis, in classification terms, is explored by means of sequential floating selection algorithm (SFFS). This is done while considering as input set the scores resulting of a PCA analysis. Table 2. Performance of each feature extraction method. Method Selected Principal PCA- PCA-SFFSComponents SFFS- LDA Post 1NN Processing a) 3, 1, 9, 11, 8, 7, 10, 12 72% 77% b) 5, 10, 12, 4, 9, 8, 7, 76% 77% 13, 15, 17, 14, 16 c) 3, 5, 9, 13, 10, 7, 11, 73% 78% 8, 17, 20 The dimensions applied to an 1-NN classifier are selected via an SFFS parse. In the a) method only an air measurement and the last headspace sampling measure are used. In the b) method some information of chamber filling transients is included by heuristically chosen samples in the filling process, in the c) method 33 waveform samples are included. The resulting PCA dimensions

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Classification rate rises up to 73% in this later case. The selected times are shown in the curve in fig 4. These results points out that better recognition is achieved in dilution mode rather than when the sample is actually in the chamber. This could be caused because the sensor gets a saturation mode in the sample cycle.

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Figure 4. Example of sensor signals and feature extraction methods. In a), b) and c) methods, features are choosen in a manual form. In the last figure, the result of SFFS parse is shown. selected by SFFS are summarized in the table 2. The resulting 1-NN performance (using leave one out validation procedure) is included in the last column.In methods a), first choices done by SFFS are the 3rd and 1st principal components. In method b), mostly transient information in the feature set is included and the firsts components chosen are rather high (5th and 10th). This suggests that transient information introduces a strong variance not related to classification problem but probably due to sampling variance. On the other hand higher components in b) case seems to help the classifier. Further LDA processing after selected principal components gives similar behavior in the three methods. Indicating that similar information is contained. The effect of the three different feature extraction techniques is to move the significant variance among different principal components in feature space. Thus, a feature extraction related with the classification problem helps to move significant variance to lower principal components. The selection of the features to extract from the waveform was heuristic in the a) and b) methods. An SFFS parse can be applied in order to select which time-samples are to be used in the classifier. In this case all sensor signals at a given time are considered as a feature and we let SFFS to select which best time-snapshots are better to build a classifier. Four samples plus temperature are selected. Two of this samples belong to the reference cycle and surprisingly the two last values selected are not in the filling stage but in the early samples of the cleaning stage.

A portable electronic nose has been developed based on metal oxide gas sensors and embedded pattern recognition. The instrument has been applied to the classification of extra virgin olive oils based on its aroma. Good results have been obtained for this difficult aroma recognition problem. The effect of manual and automatic feature extraction procedure has been explored. Automatic selection techniques surprisingly chose signals in the cleaning stage and is revealed as a necessary tool in feature extraction techniques for machine olfaction. The classification results improve by suitable dimensionality reduction with simple classifiers. Further work, will try to refine the design of the pattern recognition system using feature selection, NN editing and advanced feature selection techniques. Additional improvements can come from the optimization of the sensors operation mode using temperature modulation.

References [1] J. W. Gardner, P.N. Bartlett, “A brief history of electronic noses”, Sensors and Actuators B, 18-19 (1994) 211-220 [2] T.C. Pearce “Computational parallels between the biological olfactory pathway and its analogue ‘The Electronic Nose’: Part I. Biological olfaction” BioSystems 41 (1997) 43-67 [3] Lancet, D “The strong scent of success” Nature 351, (1991) 275-276 [4] T.C. Pearce “Computational parallels between the biological olfactory pathway and its analogue ‘The Electronic Nose’: Part II. Sensor-based machine olfaction” BioSystems 41 (1997) 69-90 [5] N. Yamazoe, N. Miura “Some basic aspects of Semiconductor Gas Sensors” Chemical Sensor Technology, 4 (1992) 19-42 [6] A.P. Lee “Temperature modulation in semiconductor gas sensing” Sensors and Actuators B 60 (1999) 35-42 [7] M. Pardo, G. Sberveglieri, S. Gardini, E. Dalcanale, “Sequential classification of 14 olive oils types by an Electronic Nose”, Proc. Int. Symp. Olfaction and Electronic Noses’ 99, pp. 255-258.

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