Electronic nose components

Keywords electronic nose principal component analysis pattern recognition chemical sensors sensor arrays olfaction system multivariate data analysis. 

When applied to electronic nose data the presence of various sources of correlated disturbances has to be considered. As an example, sample temperature fluctuations induce correlated disturbances, which may be described by principal components of highest order. When these disturbances are important the first principal component has to be eliminated in order to emphasize the relevant data properties. A set of algorithms called Minor Component Analysis (MCA) was introduced to take into account these phenomena mainly in image analysis [17]. 

The hypothesis of a normal distribution is a strong limitation that should be always kept in mind when PCA is used. In electronic nose experiments, samples are usually extracted from more than one class, and it is not always that the totality of measurements results in a normally distributed data set. Nonetheless, PCA is frequently used to analyze electronic nose data. Due to the high correlation normally shown by electronic nose sensors, PCA allows a visual display of electronic nose data in either 2D or 3D plots. Higher order methods were proposed and studied to solve pattern recognition problems in other application fields. It is worth mentioning here the Independent Component Analysis (ICA) that has been applied successfully in image and sound analysis problems [18]. Recently ICA was also applied to process electronic nose data results as a powerful pre-processor of data [19]. 

A generalised structure of an electronic nose is shown in Fig. 15.9. The sensor array may be QMB, conducting polymer, MOS or MS-based sensors. The data generated by each sensor are processed by a pattern-recognition algorithm and the results are then analysed. The ability to characterise complex mixtures without the need to identify and quantify individual components is one of the main advantages of such an approach. The pattern-recognition methods maybe divided into non-supervised (e.g. principal component analysis, PCA) and supervised (artificial neural network, ANN) methods also a combination of both can be used. 

Common electronic noses are so called as they are often aimed at detection of odorous compounds it is generally not clear that discriminations are based on odorous rather than non-odorous, and possibly incidental, components of the headspace. In the headspace of a food sample, odorants contributing to the flavour may be present in low concentrations, whereas non-odorous molecules can be present in much larger numbers and higher concentration. In such cases. 

Although common electronic noses are generally not suitable for odour assessment, they can be successfully used in applications where the main components in the headspace are directly correlated with the property to be determined (e.g. quality of spice mixtures) or in cases where substances are formed and released into the headspace, for example owing to oxidation processes, fermentation, microbial contamination, thermal treatment, etc. 

In amperometry, we measure the electric current between a pair of electrodes that are driving an electrolysis reaction. One reactant is the intended analyte and the measured current is proportional to the concentration of analyte. The measurement of dissolved 02 with the Clark electrode in Box 17-1 is based on amperometry. Numerous biosensors also employ amperometry. Biosensors8-11 use biological components such as enzymes, antibodies, or DNA for highly selective response to one analyte. Biosensors can be based on any kind of analytical signal, but electrical and optical signals are most common. A different kind of sensor based on conductivity—the electronic nose —is described in Box 17-2 (page 360). 

Titanium dioxide has also found profitable use as a component of an electronic nose or tongue by different types of treatments useful for the... 

The data processing of the multivariate output data generated by the gas sensor array signals represents another essential part of the electronic nose concept. The statistical techniques used are based on commercial or specially designed software using pattern recognition routines like principal component analysis (PCA), cluster analysis (CA), partial least squares (PLSs) and linear discriminant analysis (LDA). 

The first two principal components on electronic nose data clearly separated the Rhododendron honey from the three others. The third component explained 13.2% of the total variance, and the fourth 4.4% (Fig. 31.3B). The third and fourth components separated the other three types of honey the Citrus honey was located in the right part of the plot, the Hungarian Robinia in the upper central part and the Italian Robinia in the lower left part of the plot. 

In Fig. 19.1, the electronic nose fingerprint of ripened (Asiago d Allevo) cheese samples of the winter period (26 samples) and of summer period (24 samples) is shown. On examining the score plot in the space defined by the first two principal components (98.0% of the total variability), the major part of the samples was found in an area of the plot close to the intersection of the axes, denoting that the samples have an equal gaseous... 

Electronic noses provide new possibilities for monitor the state of a cultivation non-in-vasively in real-time. The electronic nose uses an array of chemical gas sensors that monitors the off-gas from the bioreactor. By taking advantage of the off-gas components different affinities towards the sensors in the array it is possible with the help of pattern recognition methods to extract valuable information from the culture in a way similar to the human nose. For example, with artificial neural networks, metabolite and biomass concentration can be predicted, the fermentability of a medium before starting the fermentation estimated, and the growth and production stages of the culture visualized. In this review these and other recent results with electronic noses from monitoring microbial and cell cultures in bioreactors are described. 

The visualization method also worked with a 500-L perfusion reactor system for production of recombinant human coagulation factor VIII (hFVIII) in Chinese hamster ovary (CHO) cells [36,37]. Despite the diluted concentration of CHO cells and low titer of hFVIII in the medium, the nose could differentiate between the batch phase, medium replacement phase, and the high and low productivity phases during the five-week long cultivations (Fig. 10). The low concentration of hFVIII makes it credible to believe that there are other components associated with the product formation that the electronic nose responds to. 

Conducting polymer sensors can be operated either to quantitatively measure the concentration of a target vapor species or to qualitatively analyze a complex mixture of vapors. For single vapors, the detection limits can be in the low-ppm region. Exposure to a mixture of vapors results in a unique pattern of responses, which is usually deciphered using standard chemometric techniques. The pattern can be used like a fingerprint to identify certain products, or to establish the quality of foodstuffs, wines, perfumes, etc. The electronic nose has similar components as the natural nose this is illustrated in Figure 1.15. 

In previous work we have discussed and analyzed how Fisher Information can be used to quantify the performance of an electronic nose (Sanchez-Montands and Pearce 2001, Pearce and Sdnchez-Montanes 2003). Basically, the Fisher Information Matrix (FIM) F is a square and symmetric matrix of i x i components, where s is the number of individual compounds whose concentration we are interested to estimate. In order to calculate F we should first calculate the individual FIMs for each sensor f. 

The responses were evaluated by PCA and ANN and it was claimed that good results were obtained and that the electronic nose was useful for the characterization of honey. In addition, the technique does not require isolation of the volatile components. 

However, several problems still exist. These include sensor drift, which leads to the inability to provide proper calibration. This is of special concern to quality control laboratories and is one of the reasons for the general absence of these instruments in these laboratories [3]. Limitations to the use of the electronic nose include loss of sensitivity in the presence of water vapor and high concentrations of individual components such as alcohol, relatively short life of some sensors, and the inability to obtain quantitative data for aroma differences [72]. Each device also still needs considerable method development, but progress is being made at a rapid rate. Einally, sensor arrays and PR tend to predict the quality of a sample without providing hard data with respect to composition and concentration [74]. 

Mertens, B. Thompson, M. Fearn, T. (1994). Principal component outlier detection and SIMCA a synthesis. Analyst. Vol. 119, pp. 2777-2784. ISSN 0003-2654 Miller, J.N. Miller, J.C. (2005). Statistics and Chemometrics for Analytical Chemistry. 4 edition. Prentice-Hall, Pearson. ISBN 0131291920. Harlow, UK Naes, T. Isaksson, T. Fearn, T. Davies, T. (2004). A user-friendly guide to multivariate calibration and classification. NIR Publications, ISBN 0952866625, Chichester, UK Pardo, M. Sberveglieri, G. (2005). Classification of electronic nose data with support vector machines. Sensors and Actuators. Vol. 107, pp. 730-737. ISSN 0925-4005 Pretsch, E. Wilkins, C.L. (2006). Use and abuse of Chemometrics. Trends in Analytical Chemistry. Vol. 25, p. 1045. ISSN 0165-9936... 

The principle of working which operates the electronic nose is distinctly different from that of commonly used analytical instruments (e.g. gas chromatograph). The e-nose gives an overall assessment of the volatile fraction of the foodstuff that is, in large p>art responsible for the perception of the aroma of the investigated sample, without the need to seprarate and identify the various components. All the responses of the sensors resulted from the electronic nose creates a "map" of non-sp>ecific signals that constitute the profile of the food product, also called olfactory fingerprints. 

The idea of artificial reproduction of human responses to external stimuli was first published in 1943 [17]. Later on, this concept was extended to build an electronic brain based on neural computing. The first analytical device on these concepts was an electronic nose capable of analyzing gases [18]. Electronic tongue was built few years later and very soon it proved itself as a promising device in both quantitative and qualitative analysis of multi-component matrices [19, 20]. Thereafter numerous types of sensors, devices and data processing methodologies have been developed... 

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