Abstract

Laser-induced breakdown spectroscopy (LIBS) is a technology of content analysis and composition analysis based on the atomic excitation and emission spectrum of materials. It has been intense activity in the field because of its advantages such as fast detection speed, no environmental limitation and no sample pretreatment. The low accuracy of LIBS is a primary problem in current applications, and the better data analysis methods is the key to solve this problem. Recently, machine learning algorithms significantly improve the accuracy of LIBS compared with traditional analysis methods. Therefore, the researchers gradually begin to pay attention to the application of machine learning algorithms in the LIBS data analysis. It is a programming method to study how computers simulate the learning process of human beings to acquire new knowledge and skills and continuously improve their performance. It is widely used in data analysis, pattern recognition, artificial intelligence and other fields. Here, we introduce the basic principle of LIBS and machine learning algorithms, review the research situation and progress of the application of machine learning algorithms to LIBS, and put forward the problems and challenges of its application.

摘要

激光诱导击穿光谱（LIBS）是一种基于材料原子激发和发射光谱的含量分析和成分分析技术。由于其检测速度快、不受环境限制、无需样品预处理等优点，在该领域一直备受关注。LIBS的低准确率是当前应用中的一个主要问题，而更好的数据分析方法是解决这个问题的关键。最近，与传统分析方法相比，机器学习算法显着提高了 LIBS 的准确性。因此，研究人员逐渐开始关注机器学习算法在LIBS数据分析中的应用。它是研究计算机如何模拟人类的学习过程以获取新的知识和技能并不断提高其性能的一种编程方法。广泛应用于数据分析、模式识别、人工智能等领域。在此，我们介绍了LIBS的基本原理和机器学习算法，回顾了机器学习算法在LIBS中应用的研究现状和进展，并提出了其应用中存在的问题和挑战。