Electronic nose data processing

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]. 

S. Balasubramanian, S. Panigrahi, C.M. Logue, C. Doetkott, M. MarcheUo, J.S. Sherwood, Independent component analysis-processed electronic nose data for predicting SalmoneUa typhimurium populations in contaminated beef. Food Control 19(3), 236-246 (2008)... 

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. 

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). 

A bioprocess system has been monitored using a multi-analyzer system with the multivariate data used to model the process.27 The fed-batch E. coli bioprocess was monitored using an electronic nose, NIR, HPLC and quadrupole mass spectrometer in addition to the standard univariate probes such as a pH, temperature and dissolved oxygen electrode. The output of the various analyzers was used to develop a multivariate statistical process control (SPC) model for use on-line. The robustness and suitability of multivariate SPC were demonstrated with a tryptophan fermentation. 

Figure 12. Overview of data processing within a sensor-based electronic nose.

Figure 13. Multivariate data processing techniques employed by electronic noses. See reference [2] for definition of acronyms.

Flavors are widely used in pharmaceutical solutions to mask drug bitterness. Zhu s group [48] has used an MOS electronic nose to perform headspace analysis of these formulations. The method was able to qualitatively distinguish six common flavors (raspberry, red berry, strawberry, pineapple, orange, and cherry) in placebo mixtures. The instrument was also able to identify unknown flavors. It was also indicated that the instrument could be used to identify different flavor raw materials. Moreover, the electronic nose was used for quantitative analysis of flavors in an oral solution. Data processing and identification were done by PCA, discriminant factorial analysis (DFA), and partial least squares. 

An essential step in the analysis with an electronic nose, is the high performance of statistical elaboration. The electronic nose provides multivaiiated results that need to be processed using chemometric techniques. Even if the best performing programs are sophisticated and, consequently, require the operation of skilled personnel, most companies have implemented user-friendly software for data treatment in commercially available electronic noses (Ampuero Bosset, 2003). 

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... 

ENs— used as a black box—and classical analytical techniques which aim to quantify individual volatile components. Nonetheless, it is unrealistic to envisage a universal electronic nose that is able to cope with every odor type, conversely data processing and instrumentation must be specifically designed for each application. 

Nowadays, electronic noses have an increasingly prominent role in the field of perfume analysis. These instruments are devices composed of an array of nonselective gas sensors that can act in a manner similar to that of real biological noses. In this way, after exposure to analyte vapors, the analyte molecules diffuse and pass over the detectors, producing characteristic signal patterns, which are conveniently processed using multivariate data analysis (principal... 

An older general review by Stefan et al. [2] considers mathematical modeling for data processing (including a variety of chemometric methods such as linear and nonlinear partial least squares, fuzzy neural networks, and multivariate analysis of variance), designs for electrochemical sensor arrays as well as applications in environmental, food and clinical analysis. Arrays of potentiometric ion-selective electrodes, piezoelectric crystal sensors, and voltammetric biosensors, as well as the electronic nose gas-phase sensor arrays are reviewed. 

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