将竞争自适应重加权采样（CARS）与区间偏最小二乘回归（iPLS）相结合的变量筛选建模方法CARSiPLS，用于烟煤中水分与挥发分的近红外光谱测定。以CARS逐步筛选出每个区间与待测量相关的变量，建立烟煤中水分与挥发分近红外光谱测定的偏最小二乘回归模型。结果表明：与PLS、iPLS相比，CARSiPLS可以显著减少变量数，同时提高模型预测性能；挥发分建模变量从1557个减少至15个，水分建模变量从1557个减少至317个；挥发分、水分的预测平均绝对百分误差分别从0.031 5降至0.018 4、从0.188 4降至0.094 6；挥发分、水分的预测均方差分别从0.010 8降至0.006 7、从0.005 0降至0.002 8。

A improved modeling method for selection of variables, i.e., CARSiPLS was proposed by combining the methods of competitive adaptive reweighted sampling (CARS) and internal partial least square regression (iPLS) and applied to the modelling in NIRS determination of moisture and volatiles in bitumite. PLS regression models for NIRS determination of moisture and volatiles in bitumite were established by stepwise selection of those variables related to the measurements from each interval by CARS. It was shown that as compared with PLS and iPLS, the number of variables was significantly reduced in CARSiPLS and the prediction performances of the models were also improved. In establishment of the models for moisture and volatiles, the number of variables was reduced from 1557 to 317 and 15 respectively. Values of MAPE and RMSEP were reduced remarkably from 0.031 5 to 0.018 4 and from 0.010 8 to 0.006 7 for volatile determinations, and from 0.188 4 to 0.094 6 and from 0.005 0 to 0.002 8 for moisture determinations.