为了在复合肥生产中对其成分进行快速检测, 达到指导生产的目的, 采用激光诱导击穿光谱技术(LIBS)与支持向量机(SVM)方法结合对复合肥中磷(P)元素进行定量分析。实验中选取磷元素的P Ⅰ 213.5nm, P Ⅰ 214.9nm和P Ⅰ 215.4nm 3条特征谱线对58个复合肥样品进行分析。采用随机选择法将58个样品划分为训练集(43个样本)和测试集(15个样本), 用网格搜索法对复合肥中P元素的定量分析模型进行参量寻优, 构建了SVM分析模型。结果表明, 所建立的训练集定标模型的相关系数R2=0.981, 说明训练集的参考值和预测值的相关性较高; 测试集中验证样本P元素的参考值与预测值的相关系数R2=0.992, 均方误差为4.95×10-5 , 说明所构建的SVM模型的适用性较强; 训练集的平均绝对误差和相对误差分别为5.9×10-4和3.99×10-3; 测试集的平均绝对误差和相对误差分别为5.6×10-4和3.28×10-3。将SVM算法与 LIBS技术结合可实现复合肥中磷元素的快速检测, 这为复合肥中元素含量快速检测提供了参考。

In order to detect its components rapidly in the production of compound fertilizer and guide the production, laser-induced breakdown spectroscopy (LIBS) and support vector machine (SVM) were used to quantitatively analyze phosphorus (P) in compound fertilizer. In the experiment, 58 compound fertilizer samples were analyzed by three characteristic spectra of PⅠ 213.5nm, PⅠ 214.9nm and PⅠ 215.4nm. 58 samples were divided into training set (43 samples) and test set (15 samples) by random selection method. The grid search method was used to optimize the parameters of the quantitative analysis model of P element in compound fertilizer. The SVM analysis model was constructed. The results show that, the correlation coefficient R2 of the calibration model of training set is 0.981. It shows that the correlation between the reference value and the predicted value of the training set is high. The correlation coefficient R2 between the reference value and the predicted value of phosphorus (P) in the samples is 0.992. The mean square error is 4.95×10-5. SVM model has strong applicability. The average absolute error and relative error of the training set are 5.9×10-4 and 3.99×10-3, respectively. The average absolute error and relative error of the test set are 5.6×10-4 and 3.28×10-3, respectively. The combination of SVM algorithm and LIBS technology can realize the rapid detection of phosphorus in compound fertilizer. This study provides a reference for rapid determination of element content in compound fertilizer.