变量选择是光谱分析领域一个重要的组成部分。 为了克服传统区间选择法的缺点与不足, 基于无信息变量消除法和岭极限学习机提出一种新型的变量选择与评价方法。 首先, 利用无信息变量消除法剔除整个光谱区间中无信息的波长点; 其次, 为了解决传统建模方法（偏最小二乘法、 BP神经网络等）存在的共线性问题, 采用岭极限学习机方法建立回归模型; 最后, 最佳的特征光谱波长点组合利用特征选择路径图和稀疏度-误差折中曲线进行确定。 CO气体的浓度反演实验结果表明: （1）利用无信息变量消除法可以有效筛选出最能表征CO气体透过光谱的特征波长点; （2）岭极限学习机方法具有快速建模、 避免共线性和高精度等优点（CO气体浓度反演模型的决定系数可达0.995）; （3）特征选择路径图和稀疏度-误差折中曲线可以直观地帮助用户寻找出最佳的特征波长点组合。

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.