本文選題：光譜學(xué) + 激光誘導(dǎo)擊穿光譜　； 參考：《光譜學(xué)與光譜分析》2017年08期

【摘要】：在水體重金屬激光誘導(dǎo)等離子體光譜定量分析中,一般提取光譜的多個特征變量進行濃度反演,但變量之間所包含的光譜信息可能存在重疊,回歸模型的復(fù)雜程度也隨之增大。為提取有效特征變量,研究了基于偏最小二乘法(PLS)的變量篩選方法。該方法以待測元素濃度為因變量,多個與待測元素濃度相關(guān)的LIBS光譜特征值為自變量,進行PLS建模;依據(jù)各原始變量的投影重要性指標(biāo)值進行變量篩選,提取最優(yōu)變量子集。結(jié)果表明湖庫水體中Pb元素的最優(yōu)變量子集為PbⅠ405.78nm峰值及峰值前相鄰點光譜值、內(nèi)標(biāo)校正值和信背比值,訓(xùn)練集的復(fù)相關(guān)系數(shù)R2m=0.912。以優(yōu)化變量組合進行PLS回歸分析,測試集預(yù)測結(jié)果的RSD和RE分別為10.2%和7.9%,顯著優(yōu)于內(nèi)標(biāo)法的預(yù)測結(jié)果。結(jié)果還表明,變量篩選結(jié)果對于不同元素和不同水樣具有一定適用性。研究結(jié)果為水體重金屬LIBS定量分析提供了優(yōu)質(zhì)特征數(shù)據(jù),研究方法為其他涉及變量篩選的定量分析提供了參考。
[Abstract]:In the quantitative analysis of laser induced plasma spectra of heavy metals in water, the concentration inversion is carried out by extracting several characteristic variables of the spectra, but the spectral information contained among the variables may overlap, and the complexity of the regression model also increases. In order to extract effective feature variables, the method of variable selection based on partial least square method (PLS) was studied. This method takes the concentration of tested elements as dependent variables and several LIBS spectral eigenvalues related to the concentration of tested elements as independent variables to model PLS model, and selects variables according to the projection importance index values of each original variable to extract the optimal subset of variables. The results show that the optimal subset of Pb elements in lake and reservoir water is the peak value of Pb 鈪，

本文編號：2045265