A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective
methods which can select an optimal variables subset. In this study, a strategy that considers the possible
interaction effect among variables through random combinations was proposed, called iteratively retaining
informative variables (IRIV). Moreover, the variables are classified into four categories as strongly
informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both
the strongly and weakly informative variables in every iterative round until no uninformative and interfering
variables exist. Three datasets were employed to investigate the performance of IRIV coupled with
partial least squares (PLS).