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一种基于非参数先验惩罚最小二乘算法的激光诱导击穿光谱(LIBS)基线校正方法。

A LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares algorithm.

作者信息

Ma Shengjie, Xu Shilong, Chen Youlong, Dou Zhenglei, Xia Yuhao, Ding Wanying, Dong Jiajie, Hu Yihua

机构信息

State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, People's Republic of China.

Key Laboratory of Electronic Restriction of Anhui Province, National University of Defense Technology, Hefei 230037, People's Republic of China.

出版信息

Anal Methods. 2024 Jul 4;16(26):4360-4372. doi: 10.1039/d4ay00679h.

Abstract

Laser-induced breakdown spectroscopy (LIBS) has become a popular element analysis technique because of its real-time multi-element detection and non-damage advantages. However, due to factors such as laser-substance interaction and the experimental environment, the measured LIBS spectrum signal contains a continuous background, severely influencing spectrum analysis. In this paper, we propose a LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares (NPPPLS) algorithm. Compared with the traditional Penalized Least Squares (PLS) method, improvements have been made in two aspects. On the one hand, a new weight method with faster convergence is proposed. On the other hand, we combine the Adam algorithm and introduce the RMSE of the baseline correction result at the previous time to constrain the update of the balance parameter, which enables the balance parameter to be adjusted adaptively and no parameter prior is required. The simulation results show that the proposed NPPPLS algorithm can achieve excellent correction results, even with no parametric priors. In addition, the performance of the NPPPLS algorithm is not affected by the initial value of the balance parameter, and the stability and robustness are significantly improved. Finally, we conducted baseline correction of the experimental LIBS spectrum and performed univariate and multivariate analyses. The results show that the quantitative analysis accuracy is improved after baseline correction, and the correlation coefficient of different elements obtained by the extreme learning machine method of multivariate analysis can reach 0.99, demonstrating a better quantitative analysis result. The simulation and experimental results verify the excellent performance of the proposed NPPPLS algorithm, which can be effectively used to improve the accuracy of quantitative analysis. In addition, this method is also expected to be used for baseline correction of the Raman spectrum, near-infrared spectrum and so on.

摘要

激光诱导击穿光谱(LIBS)因其具有实时多元素检测和无损检测的优点,已成为一种流行的元素分析技术。然而,由于激光与物质相互作用和实验环境等因素,所测量的LIBS光谱信号包含连续背景,严重影响光谱分析。在本文中,我们提出了一种基于非参数先验惩罚最小二乘法(NPPPLS)算法的LIBS光谱基线校正方法。与传统的惩罚最小二乘法(PLS)相比,在两个方面进行了改进。一方面,提出了一种收敛速度更快的新权重方法。另一方面,我们结合了Adam算法,并引入上一次基线校正结果的均方根误差(RMSE)来约束平衡参数的更新,这使得平衡参数能够自适应调整,且无需参数先验。仿真结果表明,所提出的NPPPLS算法即使在没有参数先验的情况下也能取得优异的校正结果。此外,NPPPLS算法的性能不受平衡参数初始值的影响,稳定性和鲁棒性显著提高。最后,我们对实验LIBS光谱进行了基线校正,并进行了单变量和多变量分析。结果表明,基线校正后定量分析精度得到提高,多变量分析的极限学习机方法得到的不同元素的相关系数可达0.99,显示出较好的定量分析结果。仿真和实验结果验证了所提出的NPPPLS算法的优异性能,可有效用于提高定量分析的准确性。此外,该方法也有望用于拉曼光谱、近红外光谱等的基线校正。

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