针对衰减全反射-傅里叶变换红外（ATR-FTIR）光谱仪用于测量结晶过程溶液浓度时， 因光谱谱线维度高、 无关变量多， 导致的标定模型预测精度低、 可解释性差等问题， 提出了一种变量稳定加权混合收缩的新方法。 首先提出对光谱变量进行随机二进采样， 将建立的优秀子模型中变量被选频率与所有子模型中变量回归系数的稳定性指标进行加权评价的稳定加权变量种群分析法（SWVCPA）。 通过对变量的重要性进行排序， 采用指数递减函数在迭代过程中逐渐强制滤除重要性低的变量， 实现了对光谱变量空间的初步收缩， 并大幅提高了收缩的稳定性。 然后在收缩后的子空间继续使用一种新的动态麻雀算法（DSSA）， 以最小化训练预测均方根误差（RMSEC）为适应度函数进一步优化变量组合。 这种混合优化方式融合了两类变量选择算法的优点， 通过子模型竞争的方法确保了前期变量收缩的稳定性， 防止算法陷入局部最优; 通过智能优化算法避免了对剩余变量组合的遍历寻优， 允许保留更多的变量进行精准选择。 为了验证新方法的性能， 使用L-谷氨酸溶液冷却结晶过程中6种不同浓度下采集到的ATR-FTIR光谱数据进行测试。 结果表明， 新方法将光谱变量数从613个减少到46个， 与原始光谱相比， 使用选择后变量建立的偏最小二乘法（PLSR）模型其预测均方根误差（RMSEP）为从1.727 9降低到0.165 4， 预测决定系数（R2）从0.973 7提高到0.999 7。 另外相比于特征谱段、 遗传算法（GA）以及变量种群组合分析法（VCPA）选择变量建立的模型， 使用新方法建立的溶液浓度预测模型具有更高的准确性和稳定性， 说明该方法对提高使用ATR-FTIR光谱法测量冷却结晶过程溶液浓度准确性和可靠性具有一定的实际应用价值。

In this paper, a new method for stable weighted mixture contraction of variables is proposed to address the problems of low prediction accuracy and poor interpretability of calibration models due to high spectral line dimensionality and many irrelevant variables when using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometers for measuring solution concentrations in crystallization processes. The method first proposes a stable weighted variable population analysis (SWVCPA) with a random binary sampling of the spectral variables and a weighted evaluation of the selected frequencies of the variables in the established superior sub-models and the stability indicators of the regression coefficients of the variables in all sub-models. By ranking the importance of variables and using an exponentially decreasing function to gradually force the filtering out of variables of low importance during the iterative process, an initial contraction of the spectral variable space is achieved, and the stability of the contraction is substantially improved. Then a new Dynamic Sparrow Search Algorithm (DSSA) is continued on the shrunken subspace to optimize the combination of variables further using the minimization of the root mean square error of training prediction (RMSEC) as the fitness function. This hybrid optimization approach combines the advantages of both types of variable selection algorithms, ensuring the stability of the prior variable contraction through a sub-model competition approach, preventing the algorithm from falling into a local optimum, and avoiding the traversal search for the remaining variable combinations through an intelligent optimization algorithm, allowing more variables to be retained for accurate selection. ATR-FTIR spectral data collected at six different concentrations during the cooling and crystallization of L-glutamic acid solutions were tested. The results showed that the new method reduced the number of spectral variables from 613 to 46 and that the root mean square error of prediction (RMSEP) was reduced from 1.727 9 to 0.165 4, and the coefficient of determination of prediction (R2) improved from 0.973 7 to 0.999 7 for the partial least squares (PLSR) model built using the selected variables compared to the original spectra. Genetic algorithm (GA) and variable population combination analysis (VCPA) for selecting variables, the solution concentration prediction model developed using the new method has higher accuracy and stability, indicating the practical application of the method to improve the accuracy and reliability of measuring solution concentration in cooling crystallization processes using ATR-FTIR spectroscopy.