Glucose Oxidase Biosensor Modeling by Machine Learning Methods

摘要: Biosensors are small analytical devices incorporating a biological element for signal detection. The main function of biosensor is to generate an electrical which proportional specific analyte i.e. translate into reading. Nowadays its technological attractiveness resides in fast performance, and highly sensitivity continuous measuring capabilities; however, understanding still under research. This paper focuses contribute the state art this growing field biotechnology specially on Glucose Oxidase (GOB) modeling through statistical learning methods from regression perspective. It models amperometric response GOB with dependent variables different conditions such as temperature, benzoquinone, PH glucose, by means well known machine algorithms. Support Vector Machines(SVM), Artificial Neural Networks (ANN) Partial least squares (PLS) algorithms selected do task.