Electronic nose and electronic tongue integration for improved classification of clinical and food samples

Natale, Corrado Di; Paolesse, Roberto; Macagnano, Antonella; Mantini, Alessandro; D’Amico, A.; Legin, Andrei; Lvova, Larisa; Rudnitskaya, Alisa; Vlasov, Yuri

doi:10.1016/s0925-4005(99)00477-3

Cited by 153 publications

(79 citation statements)

References 8 publications

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“…A low-level data fusion technique was used to merge the various new datasets together, considering all extracted parameters in order to increase the amount of information available for classification [8]. At low-level fusion, data matrices with the same number of vectors are combined together into a single matrix having a number of rows equal to the number of patterns (30) and a number of dimensions equal to the total number of parameters (72).…”

Section: Low-level Data Fusionmentioning

confidence: 99%

Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions

Boilot

Hines

Góngora

et al. 2003

Sensors and Actuators B: Chemical

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Intensive research and fast developments in electronic nose (EN) technologies provide the users with a wide spectrum of sensors and systems for their applications. This paper presents some of the results obtained with four different ENs on a series of collaborative tests carried out on six standard fruit samples, pure liquids and mixtures. These experiments, part of the EU ASTEQ concerted action, were designed for inter-comparison of the system's performances. Various feature extraction techniques are considered along with inter-comparison of the individual results obtained with radial basis function (RBF) and probabilistic neural networks (PNN). A low-level data fusion technique is used to merge the various datasets together, considering all extracted parameters in order to increase the amount of information available for classification. We achieve 86.7% correct classification with the fusion system, which outperforms the results obtained with individual ENs. With this fusion array, a problem of dimensionality occurs and it is possible to find an optimal array configuration of reduced dimensionality considering a subset of parameters. We report on various parameter selection methods: principal component analysis (PCA) as a mathematical transformation and two types of genetic algorithms (GAs) optimisation as search methods. Various subsets of parameters are selected and all techniques return improved classification rates, 80% with PCA, 96.7% with 6-integer gene GAs and 93.3% with 72-binary gene GAs. In order to overcome cost and technology limitations, optimisation techniques can be used to create application specific arrays selecting the best sensors or the correct parameters. #

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Section: Low-level Data Fusionmentioning

confidence: 99%

Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions

Boilot

Hines

Góngora

et al. 2003

Sensors and Actuators B: Chemical

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“…Besides, it is impractical and very hard to compare the results of instrumental analysis to biological sensing [1]. The advent of 'Disposable Taste Sensor' opened the way to a new kind of analytical approach of complex samples based on synthesis of global chemical information [2]. A preliminary study towards the development of a simple but reliable, one-shot, disposable 'taste sensor' which functions on a new concept of global selectivity is currently under development by Universiti Sains Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Milk Quality With Disposable Taste Sensor

Sim

Shya

Ahmad

et al. 2003

Sensors

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A disposable screen-printed multi channel taste sensor composed of several types of lipid as transducers and a computer as data analyzer could detect taste in a manner similar to human gustatory sensation. The disposable taste sensor was used to measure the electrical potential resulted from the interaction between lipid membranes and taste substances. In the present study, two types of packaged commercial milk, the ultra high temperature (UHT) and the pasteurized milk were tested. It was found that the disposable taste sensor is capable to discriminate reliably between fresh and spoiled milk and to follow the deterioration of the milk quality when it is stored at room temperature based on a pattern recognition principle namely Principle Component Analysis (PCA). This research could provide a new monitoring method ideally for simple and cheap decentralized testing for controlling the quality of milk, which may be of great use in the dairy industries.

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“…This is the case for hybrid arrays (different sensor technologies in the same array) and when chemical sensors are fused with other instruments, e.g. the fusion of electronic noses and electronic tongues [11].…”

Section: Data Pre-processing: Scalingmentioning

confidence: 99%

Data Analysis for Chemical Sensor Arrays

Natale

Martinelli

Pennazza

et al.

NATO Security Through Science Series

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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.It is worth remarking that a gas sensor array is a mere mathematical construction where the sensor outputs are arranged as components of a vector. Arrays can also be utilized to investigate the properties of chemical sensors, or even better, the peculiar behaviour of a sensor as a component of an array. In this chapter, the more common sensor array methodologies are critically reviewed, including the most general steps of a multivariate data analysis. The application of such methods to the study of sensor properties is also illustrated through a practical example.

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Electronic nose and electronic tongue integration for improved classification of clinical and food samples

Cited by 153 publications

References 8 publications

Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions

Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions

Monitoring of Milk Quality With Disposable Taste Sensor

Data Analysis for Chemical Sensor Arrays

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