人工神经网络在激光诱导击穿光谱数据分析中的应用进展

激光诱导击穿光谱(L IBS)具有实时、远程、多元素同时分析的优点,近年来在工业在线分析领域逐渐受到关注,发挥着重要作用.但基于发射光谱本身的特性,L IBS存在光谱噪声、基线漂移、自吸收和重叠峰等现象;又由于环境变化、激光能量波动、基体效应、样品表面形貌等因素,造成光谱稳定性和重现性差.这些问题导致光谱信息与定性、定...

Application Progress of Artificial Neural Network in Laser-Induced Breakdown Spectral Data Analysis

Laser-induced breakdown spectroscopy (LIBS) has the advantages of real-time, rapid, and multi-element simultaneous detection. It has attracted more and more attention in recent years and played an essential role in online industrial analysis. However, based on the emission spectrum characteristics, LIBS has spectral noise, baseline drift, self-absorption, and overlapping peaks, etc. In addition, spectral stability and reproducibility are poor due to environmental changes, laser energy fluctuations, matrix effects, and samples’ surface topography. These result in the nonlinear relationship between spectral information and qualitative and quantitative analysis, limiting the analysis’s sensitivity and accuracy. With the gradual improvement of LIBS devices’ stability, LIBS spectral data analysis methods are also changing with each new day. Artificial neural networks (ANN) can track and identify nonlinear characteristics, adaptive learning of LIBS spectral characteristics, screening out interference information, and its application in LIBS data analysis has been rapidly developed. This paper introduces the principle, instrument structure, and working process of LIBS and common neural network model in the field of LIBS spectrum analysis, summed up the LIBS in 2015-2020 in combination with the common ANN model in geological, alloy and organic polymer, coal, soil and biological areas such as the specific application. It pointed out that ANN’s super ability in the field of data analysis can effectively improve the LIBS analysis accuracy and improve the utilization rate of spectrum data, reducing the spectrum collection and environmental requirements. Given the technical difficulties that still required broken through, ANN’s development prospect in LIBS spectral depth information mining, portable special equipment development, technology combination, and other aspects has prospected. LIBS is becoming more and more mature, but data analysis of this technology still has a broad space for development. This review can provide a reference for the application of machine learning in LIBS data analysis.