Variable selection is an essential part in spectroscopy analysis area. To overcome the problems of traditional interval selection methods, this paper proposed a novel variable selection and assessment method based on uninformative variable elimination (UVE) and ridge extreme learning machine (RELM) algorithms. Firstly, the UVE method was adopted to eliminate the uninformative wavelengths. Secondly, to solve the collinearity problem, RELM algorithm was adopted to replace the traditional modeling methods (PLS, BP neural network, etc.). Finally, the optimal combination of wavelength regions was selected by using feature selection path (FSP) plot and sparsity-error trade-off (SET) curve. The experiment results of CO gas concentration retrieval showed that (1) the UVE algorithm can select the most informative variables, which were the feature wavelengths of the CO gas transmittance spectrum; (2) the RELM algorithm has the advantage of rapid modeling, solving the collinearity problem, and high accuracy (the determined coefficient r of CO gas concentration retrieval can reach 0.995); (3) the FSP plot and SET curve were easy understanding, also intuitive to experts to find the best combination of wavelengths and extract useful domain knowledge.