相关向量机结合主成分分析应用于LIBS技术定量分析

Application of relevance vector machine combined with principal component analysis in quantitative analysis of LIBS

采用相关向量机(RVM)结合主成分分析(PCA)建立了激光诱导击穿光谱(LIBS)技术检测土壤中Cr元素含量的定量分析模型。配制了14个不同Cr元素浓度的土壤样品,选取其中10个作为训练样品集用于构建模型,另外4个作为测试样品集用于模型性能评估。结果表明,对于土壤中Cr元素含量的测量,PCA-RVM模型的预测精度明显优于RVM模型,整体预测均方根误差由RVM模型的8.00%减小到PCA-RVM模型的3.21%,预测精度提高了59.9%。对测试样品集中全部4个待测样品,PCA-RVM模型多次重复预测结果的相对标准偏差相较于RVM模型都显著减小,且均小于1.89%,表明其预测结果具有更好的稳定性。

A quantitative analysis model for detecting Cr in soil with laser induced breakdown spectroscopy(LIBS)was established by using correlation vector machine(RVM)combined with principal component analysis(PCA).Fourteen soil samples with different Cr concentrations were prepared,of which ten were selected as training samples for model construction,and the other four as test samples for model performance evaluation.The results show that the prediction accuracy of PCA-RVM model is significantly better than that of RVM model for the measurement of Cr content in soil.The root mean square error(RMSE)of the whole prediction is reduced from 8.00%of RVM model to 3.21%of PCA-RVM model,and the prediction accuracy is improved by 59.9%.Compared with RVM model,the relative standard deviation of repeated prediction results of PCA-RVM model for all four samples in the test sample set is significantly reduced and is less than 1.89%,indicating that the prediction results of PCA RVM model have better stability.