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• W2800958052 abstract "So far, many analytical biosensors have been introduced to determine the concentration of a wide variety of analytes in environmental and agricultural samples. However, a major part of these biosensors has not been yet developed in commercial portable devices due to the fact that implementation of these methods requires an analyst to interpret the biosensor's response for analyte concentration determination. In this study, a mobile application for iOS platform is developed to predict samples' nitrate concentration in a mediated enzyme-based three-electrode biosensor. The introduced application uses fuzzy inference system (FIS) for nitrate concentration determination. The limiting cathodic current from the cyclic voltammetry, along with the sample’ pH and mediator concentration were considered as the input variables of the FIS, whilst nitrate concentration in the sample was considered as the output variable. In order to design the FIS, fuzzy rules were defined by an expert considering the nature of the problem. Furthermore, the values of the membership function parameters were optimized using a genetic algorithm-based optimization method. The performance of the fuzzy system was acceptable for nitrate concentration prediction since the normalized R2 and MSE of the prediction in test patterns were 0.95 and 0.005, respectively. Although the FIS model has been used in an intelligent nitrate biosensor in this study, the proposed model can be used in a wide range of environmental, agricultural and food biosensors. An open source version of the software in MATLAB programming environment is available at www.plba.ir/publications.html." @default.
• W2800958052 created "2018-05-17" @default.
• W2800958052 creator A5047714390 @default.
• W2800958052 creator A5079429451 @default.
• W2800958052 date "2018-06-01" @default.
• W2800958052 modified "2023-10-17" @default.
• W2800958052 title "A fuzzy-based decision making software for enzymatic electrochemical nitrate biosensors" @default.
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• W2800958052 doi "https://doi.org/10.1016/j.chemolab.2018.04.016" @default.
• W2800958052 hasPublicationYear "2018" @default.
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