Usefulness of information criteria for the selection of calibration curves

The reliability of analytical results obtained with quantitative analytical methods is highly dependent on the selection of the adequate model used as the calibration curve. To select the adequate response function or model the most used and known parameter is to determine the coefficient R(2). However, it is well-known that it suffers many inconveniences, such as leading to overfitting the data. A proposed solution is to use the adjusted determination coefficient R(adj)(2) that aims at reducing this problem. However, there is another family of criteria that exists to allow the selection of an adequate model: the information criteria AIC, AICc, and BIC. These criteria have rarely been used in analytical chemistry to select the adequate calibration curve. This works aims at assessing the performance of the statistical information criteria as well as R(2) and R(adj)(2) for the selection of an adequate calibration curve. They are applied to several analytical methods covering liquid chromatographic methods, as well as electrophoretic ones involved in the analysis of active substances in biological fluids or aimed at quantifying impurities in drug substances. In addition, Monte Carlo simulations are performed to assess the efficacy of these statistical criteria to select the adequate calibration curve.