Classification biosensors

Monosik, R., Stierfansky, M., Sturdik, E., 2012. Biosensors—classification, characterization and new trends. Acta Chim. Slov. 5, 109-120. 

Table 1 summarizes the international classification of enzymes. Classes EC1 and EC3 are the most widely used for the development of optical biosensors. Sometimes different enzymes and transducing schemes can be applied to the analysis of a single analyte and the best combination should be selected depending on the application. 

Thevenot D.R., Toth K., Durst R.A., Wilson G.S., Electrochemical biosensors Recommended definitions and classification, Pure Appl. Chem. 1999 71 2333-2348. 

D.R. Thevenot, K. Toth, R.A. Durst and F.S. Wilson, Electrochemical biosensors recommended definitions and classifications, Biosens. Bioelectron., 16 (2001) 121-131. 

Thevenot D, Toth K, Durst R, Wilson G. Electrochemical biosensors recommended definitions and classification. Biosensors Bioelectronics 2001, 16, 121-131. 

Tab. 4.9 Drug classification data and biosensor results from POPC and POPC/GM1 liposomes. (Reprinted from Tab. 1 of ref. 40 with permission from the American Chemical Society)...

Recognition Element Classification and Biosensors for Environmental Analysis... 

Compared to the investigation of enzymes, that of the structure and function of membrane-bound receptors and their biotechnological application is only at the very beginning. An analogous classification, e.g., according to recognition mechanism or specificity has not yet been attempted. However, any progress in this field will provide impetus to the development of new biosensors based on receptors. 

Of all types of biosensors, metabolism sensors based on the molecular analyte recognition and conversion have been most intensively studied. According to the degree of integration of the biocomponents they can be classified into monoenzyme sensors, biosensors using coupled enzyme reactions, organelle, microbial, and tissue-based sensors. The sequence of the following sections corresponds to this classification. 

We believe that data mining techniques will find utility in the future in determining accurate outputs from complex piezoelectric sensors yet to be developed. In the future, ever more complex biosensors and chemical sensors will be created on piezoelectric platforms. The accurate analyses of complex multidimensional inputs from such sensors may critically depend upon the use of machine learning algorithms. Such algorithms will learn to identify characteristic non-linear features of the inputs and associate them accurately with particular outputs (classification activity), such as an analyte concentration, that are then reported to the end-user. 

Strong ZA, Wang AW, McConaghy CF (2002) Hydrogel-actuated capacitive transducer for wireless biosensors. Biomed Microdevices 4(2) 97-103 Thevenot DR, Toth K, Durst RA, Wilson GS (2001) Electrochemical biosensors recommended definitions and classification. Biosens Bioelectron 16(1-2) 121-131 Tirrell M, Kokkoli E, Biesalski M (2002) The role of surface science in bioengineered materials. Surf Sci 500 61-83... 

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