However, classic statistical methods are not able to provide valid inference on the target quantity of interest due to the following difficulties in sensor calibration. First, not only the typical forward modeling but also the inverse estimation is involved. The former refers to the generation of a calibration model functionally relating the analyte concentration to the sensor response (x→y) based on experimental data; The latter concerns the use of the calibration model in practical setting to obtain from the observed response the estimation of the unknown concentration (y→x). Second, the variability of the sensor responses tends to be heterogeneous as opposed to homogeneous, which is an assumption that classic statistical methods rely on.
In light of the above, a computational resampling method was adapted in this work to address the machine learning (ML)-based inference issues (i.e., the uncertainty quantification of the estimated analyte concentrations). Built on the inference capability, a two-stage experimental design procedure was developed to guide the efficient sampling of calibration data via laboratory experiments.
The ML-based inference and experimental design methods are integrated in the chemometric calibration procedure, which was applied on a simulated biosensor to demonstrate its efficiency over traditional methods. The sensor simulator is developed from the real experimental data for a paper-based lateral flow strip (PLFS) designed to measure in blood the diagnosis biomarkers for traumatic brain injury. PLFS is finding increasing applications in detection of various diseases in point-of-care testing settings.