Sha Wen, Li Jiangtao, Xiao Wubing, Ling Pengpeng, Lu Cuiping
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electric Engineering and Automation, Anhui University, Hefei 230061, China.
Laboratory of Intelligent Decision, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China.
Sensors (Basel). 2019 Jul 25;19(15):3277. doi: 10.3390/s19153277.
The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard-Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares-support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers.
快速检测氮（N）、磷（P）和钾（K）元素有利于复合肥生产过程的控制，在肥料工业中具有重要意义。本工作的目的是比较激光诱导击穿光谱（LIBS）结合支持向量回归（SVR）的检测能力，并获得一种准确可靠的快速检测这三种元素的方法。安徽惠龙集团提供了总共58个肥料样品。采用肯纳德-斯通（KS）法将样品集分为校准集（43个样品）和预测集（15个样品）。通过元素浓度和特征线变量强度，使用四种不同的参数优化方法构建SVR校准模型，即传统网格搜索法（GSM）、遗传算法（GA）、粒子群优化（PSO）和最小二乘法（LS）。分析了所有参数优化方法的训练时间、决定系数和均方根误差。结果表明，LIBS技术结合最小二乘支持向量回归（LS-SVR）方法在复合肥等复杂基质中氮、磷、钾元素的定量测定中可能是一种可靠且准确的方法。
