Adaptive Stochastic Classifier for Noisy pH-ISFET Measurements

Sensor drift is an inevitable measurement problem and is particularly significant in the long term. The common practice is to have an auto-calibration facility (including standard buffers or accurate integrated actuators) mounted on the monitoring system. However, this approach may not be feasible when the monitoring system is miniaturized to the size of a capsule. In this paper, we develop an adaptive stochastic classifier using analogue neural computation to produce constantly-reliable classification for noisy pH-ISFET measurements. This classifier operates at the signal-level fusion and auto-calibrates its parameters to compensate the sensor drift, with simple learning rules. The ability of the classifier to operate with a drift of 85 % of the pH-ISFET’s full dynamic range is demonstrated. This sensor fusion highlights the potential of neural computation in miniaturized multisensor analytical microsystems such as Lab-in-a-Pill (LIAP) for long-term measurements.