Acta Univ. Agric. Silvic. Mendelianae Brun. 2008, 56, 73-80
Published online 2014-11-14

Use of artificial neural networks in biosensor signal classification

Vlastimil Dohnal1, Lenka Podloucká2, Zuzana Grosmanová3, Jiří Krejčí3

1Ústav technologie potravin, Mendelova zemědělská a lesnická univerzita v Brně, Zemědělská 1, 613 00 Brno, Česká republika
2Ústav mikroelektroniky, Fakulta elektrotechniky a komunikačních technologií, Vysoké učení technické v Brně, Údolní 53, 602 00 Brno, Česká republika
3BVT Technologies, a. s., Hudcova 532/78, 612 00 Brno, Česká republika

Biosensors are analytical devices that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytical signal and that utilizes a biochemical mechanism for the chemical recognition. The complexity of biosensor construction and generation of measured signal requires the development of new method for signal eva­luation and its possible defects recognition. A new method based on artificial neural networks (ANN) was developed for recognition of characteristic behavior of signals joined with malfunction of sensor. New algorithm uses unsupervised Kohonen self-organizing neural networks. The work with ANN has two phases – adaptation and prediction. During the adaptation step the classification model is build. Measured data form groups after projection into two-dimensional space based on theirs similarity. After identification of these groups and establishing the connection with signal disorders ANN can be used for evaluation of newly measured signals. This algorithm was successfully applied for 540 signal classification obtained from immobilized acetylcholinesterase biosensor measurement of organophosphate and carbamate pesticides in vegetables, fruits, spices, potatoes and soil samples. From six different signal defects were successfully classified four – low response after substrate addition, equilibration at high values, slow equilibration after substrate addition respectively low sensitivity on syntostigmine.


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