Selection of a calibration sample subset by a semi

For spectroscopic measurements, representative samples are needed in the course of building a calibration model to guarantee accurate predictions. The most widely used selection method is the Kennard-Stone method, which can be used before a reference measurement is done. In this paper, a method termed semi-supervised selection is presented to determine whether a sample should be added to the calibration set. The selection procedure has two steps. First, part of the population of samples is selected using the Kennard-Stone method, and their concentrations are measured. Second, another part of the population of samples is selected based on the scalar value distribution of the net analyte signal. If the net analyte signal of a sample is distinctive compared to the existing net analyte signal values, then the sample is added to the calibration set. The analyte of interest in the sample is then measured so that the sample can be used as a calibration sample. By a validation test, it is shown that the presented method is more efficient than random selection and Kennard-Stone selection. As a result, both the time and the money spent on reference measurements are saved