A Resampling Approach for the Data-Based Optimization of Nanosensors

Nanostructured sensors are a promising alternative for substance detection from biological samples in food and clinical industries. Linear regression is often used for the design and calibration, relating the sensor response with known concentrations of the substance to be detected. The linear regression slope is then used to estimate the sensor sensitivity and the limits of detection and quantification. These estimates, however, are reported without measures of uncertainty, like confidence intervals. Here we used nonparametric bootstrapping to compute the estimates and their confidence intervals using the data from cyclic voltammetry and compare two classes of sensors.