Statistical and probabilistic approaches to confidence intervals of linear calibration in

Abstract

The aim of this paper is to compare the reliability of two approaches to estimate the 95% confidence intervals of linear calibration in real situations. One is the statistical approach, which is well known in statistics, and the other is the probabilistic approach, which is based on a theory to predict the precision of instrumental analyses mainly from signal and noise, called FUMI (Function of Mutual Information) theory. The high-performance liquid chromatographic determination of quisalofop and maltose is taken as an example. Calibration lines obtained under the same experimental conditions are superimposed on the 95% confidence intervals to investigate whether the resulting confidence intervals can include all the calibration lines reasonably. A pair of 95% confidence intervals (upper and lower limits) can be calculated from each calibration line, but varies from calibration line to calibration line, although obtained under the same experimental conditions. The variability and reliability of the 95% intervals are also examined.