激光诱导击穿光谱结合竞争自适应重加权采样算法对猪饲料中铜元素的定量分析

Quantitative Analysis of Copper Element in Pig Feed Using Laser Induced Breakdown Spectroscopy Combined with CARS Algorithm

饲料中添加铜元素对猪生长速度的促进效果明显,因而铜元素在猪饲料中的超标情况非常普遍,但其带来的危害也非常严重。利用共线双脉冲激光诱导击穿光谱(DP-LIBS)技术对猪饲料中的铜元素进行快速定量分析,采用竞争自适应重加权采样(CARS)算法筛选出与猪饲料中铜元素相关的22个重要变量,压缩率为1.1%;基于筛选出来的22个重要波长变量,利用偏最小二乘(PLS)回归方法建立猪饲料中铜元素含量的预测模型,并对预测集猪饲料样品中的铜元素含量进行预测。结果表明:与全光谱-PLS模型相比,CARS-PLS模型具有更高的预测精度和预测能力,模型相关系数、交叉验证均方根误差、平均相对误差分别为0.978、19.25、5.59%。CARS算法可以有效地优化猪饲料中铜元素的激光诱导击穿光谱在线检测模型,并可以提高模型的预测精度。

Feed with copper can accelerate the growth of pigs significantly, so it is common to find feed with excess copper content, but excess copper brings serious consequences. Laser induced breakdown spectroscopy （LIBS） technology is used to quantificationally analyze the copper in pig feed rapidly. Competitive adaptive reweighted sampling （CARS） algorithm screens 22 important wavelength variables which are associated with copper in pig feed with compression ratio of 1.1%. Finally, partial least squares （PLS） regression method is applied to establish the prediction model of copper content in pig feed based on the 22 important wavelength variables, and the copper content in prediction set pig feed samples is predicted. The results show that the CARS-PLS model has higher prediction accuracy and prediction ability than full spectrum-PLS model. The correlation coefficient, the root mean square error of cross validation and the relative error are 0. 978, 19.25, 5.59%, respectively. CARS algorithm can effectively optimize the LIBS online detection model of copper in pig feed and improve the prediction accuracy.