当前位置：
X-MOL 学术
›
J. Anal. At. Spectrom.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
A hybrid random forest method fusing wavelet transform and variable importance for the quantitative analysis of K in potassic salt ore using laser-induced breakdown spectroscopy
Journal of Analytical Atomic Spectrometry ( IF 3.1 ) Pub Date : 2020-02-15 , DOI: 10.1039/d0ja00010h Yu Ding 1, 2, 3, 4, 5 , Wan Zhang 1, 2, 3, 4, 5 , Xingqiang Zhao 1, 2, 3, 4, 5 , Liwen Zhang 1, 2, 3, 4, 5 , Fei Yan 1, 2, 3, 4, 5
Journal of Analytical Atomic Spectrometry ( IF 3.1 ) Pub Date : 2020-02-15 , DOI: 10.1039/d0ja00010h Yu Ding 1, 2, 3, 4, 5 , Wan Zhang 1, 2, 3, 4, 5 , Xingqiang Zhao 1, 2, 3, 4, 5 , Liwen Zhang 1, 2, 3, 4, 5 , Fei Yan 1, 2, 3, 4, 5
Affiliation
|
Potash is the main raw material for the production of agricultural fertilizers. Herein, random forest (RF) models fusing variable importance and wavelet transform were proposed to determine the K content in a potassic salt ore. Specifically, 53 potassic salts samples were analyzed, of which 37 were treated as the calibration set. An original RF model was developed for regression with the optimized parameters ntree and mtry. However, RP2 (0.7399) and the modeling time (251.8 s) of the RF model were not satisfactory. Thus, we initially explored the effect of different variable importance (VI) thresholds on the quantitative results. When the VI threshold was set to 0.090, the variable number of the VIRF model was reduced from 27 620 to 3355. There were no significant improvements for VIRF in the other model performance parameters such as RMSEP and RP2. Then, wavelet transform was adopted to screen the input variables of the RF model (defined as WTRF). Their promotion ratios were 16% (RP2 from 0.7399 to 0.8555), 38% (RMSEP from 0.1798 to 0.1106), 62% (MRE from 0.2740 to 0.1032), and 11% (MRSD from 0.0686 to 0.0613). In the case of modeling time, it was promoted by about three orders of magnitude. Upon further using the variable importance for the WTRF model (defined as WT-VIRF), because all the selected input variables filtered by wavelet transform contributed significantly to the quantitative results, no more variables were removed and then, the WT-VIRF model achieved the exact result with the WTRF model. Thus, all the results demonstrate that the RF model combined with WT is a promising methodology for the quantitative analysis of the K content in potassic salt ores.
中文翻译：
融合小波变换和变量重要性的混合随机森林方法，用于激光诱导击穿光谱法定量分析钾盐矿石中的钾
钾肥是生产农业肥料的主要原料。在此，提出了融合变量重要性和小波变换的随机森林（RF）模型来确定钾盐矿中的K含量。具体来说，分析了53个钾盐样品，其中37个被作为校准组。开发了一个原始的RF模型进行回归，并使用了优化的参数n tree和m try。但是，R PRF模型的2（0.7399）和建模时间（251.8 s）不令人满意。因此，我们最初探讨了不同的变量重要性（VI）阈值对定量结果的影响。当VI阈值设置为0.090时，VIRF模型的可变数量从27 620减少到3355。VIRF在其他模型性能参数（如RMSE P和R P 2）上没有显着改善。然后，采用小波变换筛选RF模型（定义为WTRF）的输入变量。他们的晋升率为16％（R P 2从0.7399降至0.8555），38％（RMSE P从0.1798到0.1106），62％（MRE从0.2740到0.1032）和11％（MRSD从0.0686到0.0613）。在建模时间的情况下，它被提升了大约三个数量级。在进一步将变量重要性用于WTRF模型（定义为WT-VIRF）后，由于所有通过小波变换过滤的选定输入变量均对定量结果有重大贡献，因此不再删除任何变量，然后WT-VIRF模型实现了WTRF模型的确切结果。因此，所有结果表明，RF模型与WT相结合是一种定量分析钾盐矿石中K含量的有前途的方法。
更新日期：2020-02-15
中文翻译：
融合小波变换和变量重要性的混合随机森林方法，用于激光诱导击穿光谱法定量分析钾盐矿石中的钾
钾肥是生产农业肥料的主要原料。在此，提出了融合变量重要性和小波变换的随机森林（RF）模型来确定钾盐矿中的K含量。具体来说，分析了53个钾盐样品，其中37个被作为校准组。开发了一个原始的RF模型进行回归，并使用了优化的参数n tree和m try。但是，R PRF模型的2（0.7399）和建模时间（251.8 s）不令人满意。因此，我们最初探讨了不同的变量重要性（VI）阈值对定量结果的影响。当VI阈值设置为0.090时，VIRF模型的可变数量从27 620减少到3355。VIRF在其他模型性能参数（如RMSE P和R P 2）上没有显着改善。然后，采用小波变换筛选RF模型（定义为WTRF）的输入变量。他们的晋升率为16％（R P 2从0.7399降至0.8555），38％（RMSE P从0.1798到0.1106），62％（MRE从0.2740到0.1032）和11％（MRSD从0.0686到0.0613）。在建模时间的情况下，它被提升了大约三个数量级。在进一步将变量重要性用于WTRF模型（定义为WT-VIRF）后，由于所有通过小波变换过滤的选定输入变量均对定量结果有重大贡献，因此不再删除任何变量，然后WT-VIRF模型实现了WTRF模型的确切结果。因此，所有结果表明，RF模型与WT相结合是一种定量分析钾盐矿石中K含量的有前途的方法。
京公网安备 11010802027423号