An electronic tongue for monitoring drinking waters using a fuzzy ARTMAP neural network implemented on a microcontroller

A 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 a fuzzy ARTMAP neural network algorithm. This algorithm is 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 with the weights and the map field of the network. Once it is trained, the computed weights and map field are programmed into the microcontroller, which then gives the water classification directly for new unknown water samples.

[1]  Constantin Apetrei,et al.  Voltammetric sensor array based on conducting polymer-modified electrodes for the discrimination of liquids , 2004 .

[2]  B. Tudu,et al.  Portable Electronic Nose System for Aroma Classification of Black Tea , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.

[3]  Eduardo Garcia-Breijo,et al.  A multisensor in thick-film technology for water quality control , 2005 .

[4]  Salvador Alegret,et al.  Application of a potentiometric electronic tongue as a classification tool in food analysis. , 2005, Talanta.

[5]  Eduard Llobet,et al.  Fuzzy ARTMAP based electronic nose data analysis , 1999 .

[6]  Junhua Liu,et al.  Nonlinearity Correction of the Thermocouple Based on Neural Network , 2009, 2009 WRI Global Congress on Intelligent Systems.

[7]  R. Paolesse,et al.  Development of silicon-based potentiometric sensors: Towards a miniaturized electronic tongue , 2007 .

[8]  Fabrizio Davide,et al.  Tasting of beverages using an electronic tongue , 1997 .

[9]  Bipan Tudu,et al.  Normalization techniques for gas sensor array as applied to classification for black tea , 2009 .

[10]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[11]  Ingemar Lundström,et al.  Electronic Tongues and Combinations of Artificial Senses , 2002 .

[12]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[13]  Eduardo García-Breijo,et al.  Electronic Tongue for Qualitative Analysis of Aqueous Solutions of Salts Using Thick-film Technology and Metal Electrodes , 2006 .

[14]  Atiqur Rahman,et al.  A microcontroller-based taste sensing system for the verification of Eurycoma longifolia , 2004 .

[15]  G. Pioggia,et al.  A composite sensor array impedentiometric electronic tongue Part I. Characterization. , 2007, Biosensors & bioelectronics.

[16]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[17]  Roberto Paolesse,et al.  Electronic tongue based on an array of metallic potentiometric sensors. , 2006, Talanta.

[18]  G Pioggia,et al.  A composite sensor array impedentiometric electronic tongue Part II. Discrimination of basic tastes. , 2007, Biosensors & bioelectronics.

[19]  R. Martínez‐Máñez,et al.  A model for the assessment of interfering processes in Faradic electrodes , 2008 .

[20]  E. Llobet,et al.  An electronic tongue design for the qualitative analysis of natural waters , 2005 .

[21]  B.M. Wilamowski,et al.  A Neural Network Implementation on an Inexpensive Eight Bit Microcontroller , 2008, 2008 International Conference on Intelligent Engineering Systems.

[22]  Kiyoshi Toko,et al.  Evaluation of water quality and pollution using multichannel sensors , 2000 .