A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration

- Considers the possible interaction effect among variables through random combinations.
- Four kinds of variables were distinguished.
- It was more efficient when compared with CARS, GA-PLS and MC-UVE-PLS.