Research Of The Detection Algorithm Of Enzyme-injected Glucose Biosensors

Enzyme electrode-based sensors are less susceptible to interference due to the inherent specificity of the enzyme, thus have been widely used in biosensor techniques. Immobilized enzyme on the electrode surface exhibits good stability, and the enzyme immobilization strategy has become one of the main methods in preparing enzyme electrode. But immobilized enzyme-based biosensor cannot be used in online control process because of high-temperature steam sterilization process in fermentation practice, so the amount of glucose to be added cannot be controlled in real-time, which severely affects the fermentation performance and yield. To solve this issue, a method by using enzyme solution instead of fixed enzyme was proposed. A glucose biosensor based on enzyme injection was prepared in our laboratory. This sensor can just meet the requirements for practical application, but still inferior to well-developed enzyme membrane-type sensor in aspects of detection time, stability etc. The reason lies in the catalytic efficiency difference between immobilized glucose oxidase and enzyme in aqueous phase.In order to design sensor structure more suitable for enzyme liquid and more efficient concentration detection algorithm, we established a mathematical model of enzyme-injected glucose biosensor to describe the work process of enzyme solution in the sensor, and found the differences between enzyme in aqueous solution and immobilized enzyme in the reaction and the manifestations in the model. Based on the above results, the sensor with more rationally designed structure and more suitable detection method was fabricated. The main works of the paper are as follows:(1) A mechanism model of enzyme injection biosensor was established and the accuracy of the model was verified through experiments in this study. According to the kinetic equation of enzymatic reaction and electrochemical knowledge, the mechanism of liquid enzyme in sensor was established. The parameters of the reaction model were determined by fitting the voltage data with model and the voltage data was obtained by detecting 1mg/ml and 2mg/ml glucose solution. And the response voltage and time were fitted by cftool tool in MATLAB, the correlation parameters of the mechanism were determined. Accuracy of the model was verified by comparing the fitting curve and the actual curve. The fitting curve was obtained by taking the value of 3 mg/ml into the model and the actual data was detected by actual sensors. Experimental results showed that the liquid enzyme Michaelis constant Km was 1.97, the mathematical model was basically in agreement with the actual sensor operating model, the model could be used to describe the actual voltage variation.(2) A concentration detection algorithm was designed through the enzymatic of the linear part of the voltage curve. And this paper designed experiments to validate the accuracy of this method. The deficiency of the old detection algorithm was analyzed by the mechanism of the enzymatic reaction mechanism. In the process of modeling, the relationship between the slope of the linear partial slope of the response voltage and the function of the concentration of the analyte was found, the new detection algorithm was designed, and the detection speed was improved. In the experiments, the enzyme-injected glucose biosensor was used to detect 1mg / ml, 2mg / ml glucose solution and the voltage curve was recorded. The slope of the linear part of the voltage curve was obtained. The oxidase Michaelis constant Km can be calculated through the kinetic equation, Km was 1.59.The accuracy of this method was proved in this way, the accuracy was 98.67%.(3)The interactive platform based on LJD-eWin5 S embedded touch PC was analyzed. And some of the module functions were modified to achieve the concentration detection algorithm suitable for enzyme-injected glucose biosensor. Some variables were added to the calibration aspects of interaction platform for storing the beginning of the slope of the voltage curve and oxidase Michaelis constant. The Michaelis constant and the beginning slope of voltage curve of known concentrations were obtained by two calibrations. Original concentration detection algorithm in analysis aspects was modified. The concentration of the test solution was calculated by adding Michaelis constant and comparing the slope of the voltage change of known concentration. Finally the concentration detection algorithm was applied to the sensor.