基于卡尔曼滤波算法的葡萄糖酶生物传感器高精度检测方法

The online detection of glucose, a key substrate in the fermentation process, plays a key role in improving the fermentation efficiency and assessing the fermentation status in real-time. At present, traditional offline detection has problems such as complicated operation, large errors, and long lag time, which make it difficult to meet the requirements of concentration feedback control in the fermentation process. To address the problem of online accurate and wide range detection of glucose in the fermentation process, an adaptive Kalman filter high-precision detection method was proposed based on a homemade glucose enzyme biosensor. Firstly, a detection module was built, a concentration-response characteristic equation was established for calibration, and an automatic adjustment of the feed volume strategy was proposed to achieve high accuracy detection under a wide range of concentrations. The noise interference characteristics during the 10^-6 level current acquisition process were analyzed, and the moving average filtering algorithm was combined with the high concentration detection to further extract the effective signal under the noise by partitioning the segments. The experimental results showed that the error was less than 2% for a wide range of concentrations (1—180g/L), achieving high accuracy detection of glucose concentration in the fermentation process.