中文摘要：

针对近红外光谱数据的内在特点，提出了一种基于稳定性竞争自适应重加权采样（stability compet-itive adaptive reweighted sampling，SCARS）策略的近红外特征波长优选方法。该方法以PLS模型回归系数的稳定性作为变量选择的依据，其过程包含多次循环迭代，每次循环均首先计算相应变量的稳定性，而后通过强制变量筛选以及自适应重加权采样技术（ARS）进行变量筛选；最后对每次循环后所得变量子集建立PLS模型并计算交互验证均方根误差（RMSECV），将RMSECV值最小的集合作为最优变量子集。利用饲料蛋白固态发酵过程近红外光谱数据集对所提方法进行了验证，并与基于PLS的蒙特卡罗无信息变量消除法（MC-UVE）和竞争自适应重加权采样（CARS）方法所得结果进行了比较。试验结果显示：建立在SCARS方法优选的21个特征波长变量基础上的 PLS 模型预测效果更好，其预测均方根误差（RMSEP）和相关系数（Rp ）分别为0．0543和0．9908；该优选策略能有效地增强固态发酵光谱数据特征波长变量选择的准确性和稳定性，提高了模型的预测精度，具有一定的应用价值。

英文摘要：

According to the characteristics of near infrared spectral（NIR）data,a new tactic called stability competitive adaptive reweighted sampling （SCARS）is employed to select characteristic wavelength variables of NIR data to build PLS model.This method is based on the stability of variables in PLS model.SCARS algorithm consists of a number of loops.In each loop,the stability of each corresponding variable is computed at first.Then enforced wavelength selection and adaptive reweighted sam-pling （ARS）is used to select important variables according to the stability of variables.The selected variables are kept as a vari-able subset and further used in the next loop.After the running of all loops,a number of subsets of variables are obtained and root mean squared error of cross validation （RMSECV）of PLS models is computed.The subset of variables with the lowest RMSECV is considered as the optimal variable subset.Validated by NIR data set of protein fodder solid-state fermentation process,the SCARS-PLS prediction model is better than PLS models based on wavelengths selected by competitive adaptive re-weighted sampling （CARS）and Monte Carlo uninformative variable elimination （MC-UVE）methods.As a result,twenty one wavelength variables are selected by SCARS method to build the PLS prediction model with the predicted root mean square error （RMSEP）valued at 0. 054 3 and correlation coefficient （Rp ）0. 990 8.The results show that SCARS tactic can efficiently im-prove the accuracy and stability of NIR wavelength variables selection and optimize the precision of prediction model in solid-state fermentation process.The SCARS method has a certain application value.