Multicomponent analysis on polluted waters by means of an electronic tongue

Natale, Corrado Di; Macagnano, Antonella; Davide, Fabrizio; D’Amico, A.; Legin, Andrey; Vlasov, Yuri; Rudnitskaya, Alisa; Selezenev, B.

doi:10.1016/s0925-4005(97)00169-x

Cited by 128 publications

(70 citation statements)

References 7 publications

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“…k is the output nodes (k: 1-7). j is the hidden nodes (j: [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]: Because the output function is a Tansig, the output has a value in the range [-1,1] so they must be made to fit among [0,1] using Eq. (2).…”

Section: Implementation Of the Multi-layer Feed-forward Neural Networkmentioning

confidence: 99%

“…In this way, a number of orthogonal linear discriminant functions equal to the number of categories minus one are obtained [16,17]. Such algorithms have been used in electronic noses [18] and electronic tongue systems [19] giving important benefits such as: simplicity of implementation of computer algorithm, speed of calculation and the attainment of good and reliable results with a small number of measures.…”

Section: Introductionmentioning

confidence: 99%

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A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

García‐Breijo

Atkinson

Gil-Sánchez

et al. 2011

Sensors and Actuators A: Physical

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a b s t r a c tA portable electronic tongue has been developed using an array of eighteen thick-film electrodes of different materials forming a multi-electrode array. A microcontroller is used to implement the pattern recognition. The classification of drinking waters is carried out by a Microchip PIC18F4550 microcontroller and is based on neural networks algorithms. These algorithm are initially trained with the multi-electrode array on a Personal Computer (PC) using several samples of waters (still, sparkling and tap) to obtain the optimum architecture of the networks. Once it is trained, the computed data are programmed into the microcontroller, which then gives the water classification directly for new unknown water samples. A comparative study between a Fuzzy ARTMAP, a Multi-Layer Feed-Forward network (MLFF) and a Linear Discriminant Analysis (LDA) has been done in order to obtain the best implementation on a microcontroller.

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Section: Implementation Of the Multi-layer Feed-forward Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

García‐Breijo

Atkinson

Gil-Sánchez

et al. 2011

Sensors and Actuators A: Physical

View full text Add to dashboard Cite

show abstract

“…The taste sensor system [5] is based on ion sensitive lipid membranes, immobilized with polyvinylchloride. Also many other electronic tongue concepts have been described based on potentiometry, using different membranes or glass electrodes [9,10].…”

Section: General Measurement Principlesmentioning

confidence: 99%

Voltammetric electronic tongues – basic principles and applications

2008

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This review examines basic principles and applications of voltammetric electronic tongues. It is introduced by a description of the concept of electronic tongues or taste sensors followed by a general overview of electrochemical measurement principles that have been used for electronic tongues. A special emphasis is given on measurement principles for voltammetric electronic tongues, also including pulse voltammetry and variable reduction. Applications of voltammetric electronic tongue are described, such as in the food industry, environmental analysis, paper and pulp industry, household appliances and agriculture. Future developments of the concept, such as self polishing or miniaturized devices are also described. Finally, a continuous measurement system for chemical oxygen demand (COD), which has been commercialized, is depicted.

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“…Thanks to the application of pattern recognition techniques [1] this array is able to identify the unknown gas or vapour. In sensor applications, artificial neural networks have already been applied to recognize multi component mixtures of gases [2,3,4,5,11].…”

Section: Introductionmentioning

confidence: 99%