为提高三维荧光光谱水质分析的精度, 提出一种选择性模型组合方法, 采用相关系数法对三维荧光光谱激发波长进行选择, 并将被选中的激发波长下的荧光发射光谱水质分析子模型采用岭回归法进行模型组合, 得到对水质指标的组合模型。 以一组总有机碳(TOC)范围在3.41～125.35 mg·L-1, 化学需氧量(COD)范围在22.80～330.60 mg·L-1的32个地表水和城市生活污水水样做为研究对象, 对其三维荧光光谱220～400 nm范围内的10个激发波长采用上述方法进行选择, 分别针对TOC和COD指标筛选出260, 280, 400 nm和220, 280, 400 nm各3个激发波长。 采用部分最小二乘方法建立上述激发波长下荧光发射光谱水质分析子模型, 根据岭回归法计算各子模型的组合系数, 分别得到对TOC和COD指标的组合模型。 实验结果表明: 采用该方法得到的组合模型对TOC和COD两种指标的预测误差均方根(RMSEP)相比精度最高的单一荧光发射光谱子模型分别减小了15.4%和17.5%, 相比未经模型选择的组合模型分别减小了6.1%和10.9%

A selective model combination method is proposed in this paper to improve the precision of water quality analysis with three dimensional fluorescence spectra. A correlation coefficient criterion was designed to select effective excitation wavelengths for sub-models building, based on which the ridge regression method was adopted to combine the selected sub-models to get the stacked model. Thirty two samples from surface water and urban wastewater were used as research objects with total organic carbon (TOC) index from 3.41 to 125.35 mg·L-1, and chemical oxygen demand (COD) index from 22.80 to 330.60 mg·L-1, and 10 excitation wavelengths in the range of 220-400 nm were adopted to generate three dimensional fluorescence spectra. Following the proposed correlation coefficient criterion, the excitation wavelengths of 260, 280 and 400 nm, and the excitation wavelengths of 220, 280 and 400 nm were selected respectively for TOC analysis and COD analysis, based on which two stacked models were built by using partial least square regression method for sub-models building and ridge regression method for sub-models combination. The experimental results show that, compared with the sub-models with the best prediction precision, the root mean square errors of prediction (RMSEP) of the stacked models decreased by 15.4% for TOC analysis, and 17.5% for COD analysis; and compared with the models without sub-models selection, the RMSEP of the stacked models decreased by 6.1% for TOC analysis and 10.9% for COD analysis