Electronic nose sensor array

Masila M., Breimer M., Sadik O. A, Strategies for Improving the Analysis of Volatile Organic Compounds Using GC-Based Electronic Nose, in Electronic Noses Sensor Array based Systems, Hurst, W. J. Ed. Technomic Publishing Co., Inc., Lancaster, PA, 1999,27-42. 

D. Hodgins, The electronic nose sensor array-based instruments that emulate the human nose, Techniques for Analyzing Food Aromas (R. MarsUi, ed.), Marcel Dek-ker, New York, 1997, p. 331. 

Given their non-specific nature, the electronic nose and electronic tongue sensor arrays can only perform yes or no tests inside the set of product. Contrary to traditional analytical methods, the electrochemical sensor responses do not need and do not provide information on the nature of the compounds under investigation, but only on digital fingerprint of the typical food products. 

Electronic noses use arrays of sensors to detect different chemicals (Hines et al., 1999). Has Uttle application in ATI at present. 

The electronic nose is an example of an area in which the complexity of the analysis may make it difficult to replace a human with an ES. Electronic noses combine a sensor array with a neural network to make judgments about the composition of complex mixtures, such as fuels, wines, and natural oils. In such tasks, they often beat human noses in accuracy (Figure 7.8). 

Using the principles of biological olfaction, electronic nose systems contain arrays of different types of cross-reactive vapor-sensitive sensors. While it is difficult to discriminate analytes entirely by their responses to a single type of sensor, using an array of sensors yields response patterns that can readily distinguish many different vapors. Ideally, the response mechanisms of the sensors are highly varied and encompass both physical and chemical phenomena1. 

A sensor array named the electronic nose is a rapid and relatively simple technique that can be used for monitoring wastewater odors (Stuetz et al., 2000). The electronic nose uses sensors of varying affinities to characterize an odor without reference to its chemical composition. 

Another way for BOD estimation is the use of sensor arrays [37]. An electronic nose incorporating a non-specific sensor array of 12 conducting polymers was evaluated for its ability to monitor wastewater samples. A statistical approach (canonical correlation analysis) showed a linear relationship between the sensor responses and BOD over 5 months for some subsets of samples, leading to the prediction of BOD values from electronic nose analysis using neural network analysis. 

A chemical sensor array (consisting of eight conducting polymer sensors) derived from an electronic nose [62], for the characterization of headspace gas from a sparged liquid sample... 

Arrays were introduced in the mid-eighties as a method to counteract the cross-selectivity of gas sensors. Their use has since become a common practice in sensor applications [1], The great advantage of this technique is that once arrays are matched with proper multivariate data analysis, the use of non-selective sensors for practical applications becomes possible. Again in the eighties, Persaud and Dodds argued that such arrays has a very close connection with mammalian olfaction systems. This conjecture opened the way to the advent of electronic noses [2], a popular name for chemical sensor arrays used for qualitative analysis of complex samples. 

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

Zuppa et al.60 have used SOMs in the assessment of data from an electronic nose. Six chemicals—water, propanol, acetone, acetonitrile, butanol, and methanol—were presented at varying concentrations to a 32-element conducting polymer gas sensor array. The output was used to train a group of SOMs, rather than a single SOM, to avoid the problems of parameter drift. One SOM was associated with each vapor, and with suitable use of smoothing filters, the SOM array was found to perform effectively. 

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 performance of common multisensor arrays is ultimately determined by the properties of their constituent parts. Key parameters such as number, type and specificity of the sensors determine whether a specific instrument is suitable for a given application. The selection of an appropriate set of chemical sensors is of utmost importance if electronic nose classifications are to be utilised to solve an analytical problem. As this requires time and effort, the applicability of solid-state sensor technology is often limited. The time saved compared with classic analytical methods is questionable, since analysis times of electronic nose systems are generally influenced more by the sampling method utilised than the sensor response time [185]. 

Korel, E, Luzuriaga, D.A. Balaban, M.O., (1999) Microbial, sensory and electronic nose evaluation of pasteurized whole milk. In Hurst, W.J. (ed) Electronic noses and sensor array based systems. Lancaster, Basel, pp 154-161. 

N. (2004) Maturity discrimination of snake frmt (Salacca edulis Reinw.) cv. Pondoh based on volatiles analysis using an electronic nose device eqmpped with a sensor array and fingerprint mass spectrometry. Flavour Fragrance J. 19 44-50. 

Garcia-Gonzalez, D.L., Barie, N. Rapp, M., Aparicio, R. (2004) Analysis of virgin olive oil volatiles by a novel electronic nose based on a miniaturized SAW sensor array coupled with SPME enhanced headspace enrichment. J. Agric. Food Chem. 52 7475-7479. 

Horner, G. (1999) Qualitative and quantitative evaluation methods for sensor arrays. In Proceedings of the 6th International Symposium Olfaction and Electronic Nose, Tubingen, 20-22 September 1999. 

This strategy was first proposed for analysis in the gas phase employing an array of gas sensors [4], where it is known as electronic nose [5]. The approach receives the biomimetic qualifier, given it is inspired in the physiological basis of animal olfaction. Information obtained from... 

With respect to the type of sensors that can be used in an electronic tongue, practically all the main families of chemical sensors have been used to form the sensor array, viz. potentiometric, voltammetric, resistive, gravimetric and optical, if main sensor families have to be quoted [11], Table 30.1 sketches a survey of different approaches that can be recorded when the specialized literature is inspected. Even hybrid systems have been proposed, mainly those combining potentiometric and voltammetric sensors [3,12], The combination of electronic noses and electronic tongues to improve detection or identification capabilities, in a sensor fusion approach, has also been proposed [13,14],... 

The key principle involved in the electronic nose concept is the transfer of the total headspace of a sample to a sensor array that detects the presence of volatile compounds in the headspace and a pattern of signals is provided that are dependent on the selectivity and sensitivity of sensors and the characteristics of the volatile compounds in the headspace [2]. 

The electronic nose consists of an array of gas sensors with different selectivity, a signal collecting unit and a pattern recognition software. It is particularly useful for the analysis of headspace of liquid or solid food samples [3], and numerous attempts of using the electronic nose have been reported [4,5]. 

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