Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM

According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy(LIBS) has shown a great potential for rapid analysis in steel industry.Nevertheless, the accuracy and precision may be limited by complex matrix effect and selfabsorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine(GA-KELM) is proposed for quantitative analysis of multiple elements(Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples.First, the standardized peak intensities of selected spectra lines are used as the input of model.Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer's output matrix. Finally, the least square is applied to calculate the model's output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor(R~2), Root-meansquare Errors of Calibration(RMSEC), Root-mean-square Errors of Prediction(RMSEP) of GAKELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.     

Analytical study of seashell using laser-induced breakdown spectroscopy

Seashell has been applied as an indicator for ocean research and element analysis of the seashell is used to track biological or environmental evolution.In this work,laser-induced breakdown spectroscopy(LIBS) was applied for elementary analysis of an ezo scallop-shell,and a graphite enrichment method was used as the assistance.It was found that LIBS signal intensity of Ca fluctuated less than 5%,in spite of the sampling positions,and Sr/Ca was related to the shell growth.A similar variation was also found when using a direct LIBS analysis on the shell surface,and it might be more practicable to track shell growth by investigating Sr/Ca ratio with Sr ionic line at 421.6 nm.The obtained results prove that calcium(Ca) is qualified as an internal reference for shell analysis,and LIBS is a potential analytical method for seashell study.     

Accuracy improvement of quantitative analysis of calorific value of coal by combining support vector machine and partial least square methods in laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) is a potential technology for online coal property analysis,but successful quantitative measurement of calorific value using LIBS suffers from relatively low accuracy caused by the matrix effect.To solve this problem,the support vector machine(SVM) and the partial least square(PLS) were combined to increase the measurement accuracy of calorific value in this study.The combination model utilized SVM to classify coal samples into two groups according to their volatile matter contents to reduce the matrix effect,and then applied PLS to establish calibration models for each sample group respectively.The proposed model was applied to the measurement of calorific values of 53 coal samples,showing that the proposed model could greatly increase accuracy of the measurement of calorific values.Compared with the traditional PLS method,the coefficient of determination(R2) was improved from 0.93 to 0.97,the root-mean-square error of prediction was reduced from 1.68 MJ kg~(-1) to1.08 MJ kg~(-1),and the average relative error was decreased from 6.7% to 3.93%,showing an overall improvement.     

Distinguish Fritillaria cirrhosa and nonFritillaria cirrhosa using laser-induced breakdown spectroscopy

As traditional Chinese medicines, Fritillaria from different origins are very similar and it is difficult to distinguish them. In this study, the laser-induced breakdown spectroscopy combined with learning vector quantization(LIBS-LVQ) was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa. We also studied the performance of linear discriminant analysis, and support vector machine on the same data set. Among these three classifiers, LVQ had the highest correct classification rate of 99.17%. The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.     

Quantitative analysis of uranium in electrorecovery salt of pyroprocessing using laser-induced breakdown spectroscopy

The amounts of nuclear materials in the Li Cl-KCl salt in pyroprocessing have to be analyzed to prevent the diversion of the nuclear material. An alternative method to the chemical analysis has been pursued, and laser-induced breakdown spectroscopy(LIBS) is one candidate. In the present work, an in situ and quantitative analysis method of electro-recovery(ER) salt was proposed and demonstrated by using LIBS combined with dipstick sampling. Two types of simulated salt samples were prepared: ER salt sample and salt obtained from the dipstick sampling, and pulsed neodymium-doped yttrium aluminum garnet(Nd:YAG) laser with a wavelength of 532 nm was focused on the salt to generate plasma. The plasma emission was measured by using an Echelle spectrometer with a resolution of 0.01 nm in conjunction with an Intensified Charge-Coupled Detector camera. The U and other rare earth peaks in the spectra were identified. The best Limit of Detection and Root Mean Square Error of Calibration of U were 38 ppm and 0.0203 wt%,respectively. Our work shows that the U in the pyroprocessing ER salt can be monitored with LIBS.     

Online calibration of laser-induced breakdown spectroscopy for detection of heavy metals in water

In order to reduce the fluctuation of LIBS detection spectrum of liquid sample, the full-spectrum sum method and the internal standardization method is adopted, using an equal-RSD normalization algorithm to calibrate the detection spectrum. Experiment result shows that the full-spectrum sum method reduced the RSD of parallel samples of Cd and Cr to 9.4％ and 11.06％ from 28.32％ and 31.93％ respectively, yielded better overall calibration than the singleelement internal standardization approach, thereby suggesting that the former method is convenient and effective for online calibration of LIBS for detection of aqueous heavy metals.     

Uranium measurements using laser-induced breakdown spectroscopy in lithium chloride-potassium chloride salt of pyroprocessing

In pyroprocessing,uranium(U) is recovered from molten LiCl-KCl salt,and,for safeguard purposes,it is important to analyze the U and Plutonium(Pu) concentrations in a timely manner.In the present work,salt samples containing U were fabricated.The laser used in the present work was an Nd:YAG laser with a wavelength of 532 nm,a laser energy on the sample of11.5 mJ,and a pulse repetition rate of 10 Hz.The plasma emission light was measured with an Echelle spectrometer.A total of 100 points on the sample surface were measured as the laser incident position was changed.The U and potassium(K) peaks in the spectrum were identified.Univariate and multivariate analyzes were conducted to determine the accuracy and limit of detection(LOD) of the laser-induced breakdown spectroscopy.     

Elemental and proximate analysis of coal by x-ray fluorescence assisted laser-induced breakdown spectroscopy

Although laser-induced breakdown spectroscopy (LIBS), as a fast on-line analysis technology, has great potential and competitiveness in the analysis of chemical composition and proximate analysis results of coal in thermal power plants, the measurement repeatability of LIBS needs to be further improved due to the difficulty in controlling the stability of the generated plasmas at present. In this paper, we propose a novel x-ray fluorescence (XRF) assisted LIBS method for high repeatability analysis of coal quality, which not only inherits the ability of LIBS to directly analyze organic elements such as C and H in coal, but also uses XRF to make up for the lack of stability of LIBS in determining other inorganic ash-forming elements. With the combination of elemental lines in LIBS and XRF spectra, the principal component analysis and the partial least squares are used to establish the prediction model and perform multi-elemental and proximate analysis of coal. Quantitative analysis results show that the relative standard deviation (RSD) of C is 0.15%, the RSDs of other elements are less than 4%, and the standard deviations of calorific value, ash content, sulfur content and volatile matter are 0.11 MJ kg−1, 0.17%, 0.79% and 0.41% respectively, indicating that the method has good repeatability in determination of coal quality. This work is helpful to accelerate the development of LIBS in the field of rapid measurement of coal entering the power plant and on-line monitoring of coal entering the furnace.     

Detection and quantification of Pb and Cr in oysters using laser-induced breakdown spectroscopy

The quantitative determination of heavy metals in aquatic products is of great importance for food security issues. Laser-induced breakdown spectroscopy(LIBS) has been used in a variety of foodstuff analysis, but is still limited by its low sensitivity when targeting trace heavy metals. In this work, we compare three sample enrichment methods, namely drying, carbonization, and ashing, for increasing detection sensitivity by LIBS analysis for Pb and Cr in oyster samples. The results demonstrate t...     

Identification of heavy metal-contaminated Tegillarca granosa using laser-induced breakdown spectroscopy and linear regression for classification

Tegillarca granosa (T. granosa) is susceptible to heavy metals, which may pose a threat to consumer health. Thus, healthy and polluted T. granosa should be distinguished quickly. This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy (LIBS) coupled with linear regression classification (LRC). Five types of T. granosa were studied, namely, Cd-, Zn-, Pb-contaminated, mixed contaminated, and control samples. Threshold method was applied to extract the significant variables from LIBS spectra. Then, LRC was used to classify the different types of T. granosa. Other classification models and feature selection methods were used for comparison. LRC was the best model, achieving an accuracy of 90.67%. Results indicated that LIBS combined with LRC is effective and feasible for T. granosa heavy metal detection.     

Quantitative analysis and time-resolved characterization of simulated tokamak exhaust gas by laser-induced breakdown spectroscopy

Tokamak exhaust is an important part of the deuterium-tritium fuel cycle system in fusion reactions. In this work, we present a laser-induced breakdown spectroscopy (LIBS)-based method to monitor the gas compositions from the exhaust system in the tokamak device. Helium (He), a main impurity in the exhaust gas, was mixed with hydrogen (H2) in different ratios through a self-designed gas distribution system, and sealed into a measurement chamber as a standard specimen. A 532 nm wavelength laser pulse with an output power of 100 mJ was used for plasma excitation. The time-resolved LIBS is used to study the time evolution characteristics of the signal strength, signal-to-background ratio (SBR), signal-to-noise ratio (SNR) and relative standard deviation (RSD) of the helium and hydrogen characteristic lines. The Boltzmann two-line method was employed to estimate the plasma temperature of laser-induced plasma (LIP). The Stark-broadened profile of He I 587.56 nm was exploited to measure the electron density. From these studies, an appropriate time was determined in which the low RSD% was consistent with the high signal-to-noise ratio. The He I 587.56 nm and Hα emission lines with good signal-to-noise ratio were extracted from the spectrum and used in the external standard method and internal standard method for quantitative analysis. The test results for mixed gas showed that the average relative error of prediction was less than 11.15%, demonstrating the great potential of LIBS in detecting impurities in plasma exhaust gas.     

Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

In this paper, we explore whether a feature selection method can improve model performance by using some classical machine learning models, artificial neural network, k-nearest neighbor,partial least squares-discrimination analysis, random forest, and support vector machine(SVM),combined with the feature selection methods, distance correlation coefficient(DCC), important weight of linear discriminant analysis(IW-LDA), and Relief-F algorithms, to discriminate eight species of wood(African rosewood, Brazilian bubinga, elm, larch, Myanmar padauk,Pterocarpus erinaceus, poplar, and sycamore) based on the laser-induced breakdown spectroscopy(LIBS) technique. The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis. The feature spectral lines are selected out based on the important weight assessed by DCC, IW-LDA,and Relief-F. All models are built by using the different number of feature lines(sorted by their important weight) as input. The relationship between the number of feature lines and the correct classification rate(CCR) of the model is analyzed. The CCRs of all models are improved by using a suitable feature selection. The highest CCR achieves(98.55...0.39)% when the SVM model is established from 86 feature lines selected by the IW-LDA method. The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.     

Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks

One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy (LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem, this paper investigated a combination of time-resolved LIBS and convolutional neural networks (CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R²_c=0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network (ANN), showing R²_v=0.6318 and the root mean square error of validation (RMSEV)=0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R²_v=0.7366 and RMSEV=0.7855. These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K. However, due to limited calibration samples, the two-dimensional models presented over-fitting. The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R²_v=0.9968 and RMSEV=0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.     

Application of laser-induced breakdown spectroscopy for characterization of material deposits and tritium retention in fusion devices

Laser-induced breakdown spectroscopy (LIBS) is discussed as a possible method to characterize the composition, tritium retention and amount of material deposits on the first wall of fusion devices. The principle of the technique is the ablation of the co-deposited layer by a laser pulse with P (power density) ≥ 0.5 GW/cm² and the spectroscopic analysis of the light emitted by the laser induced plasma. The typical spatial extension of the laser plasma plume is in the order of 1 cm with typical plasma parameters of n_e ≈ 3 × 10²² m^?3 and T_e ≈ 1–2 eV averaged over the plasma lifetime which is below 1 μs. In this study “ITER-Like” mixed deposits with a thickness of about 2 μm and consisting of a mixture of W/Al/C and D on bulk tungsten substrates have been analyzed by LIBS to measure the composition and hydrogen isotopes content at different laser energies, ranging from about 2 J/cm² (0.3 GW/cm²) to about 17 J/cm² (2.4 GW/cm²) for 7 ns laser pulses. It is found that the laser energies above about 7 J/cm² (1 GW/cm²) are needed to achieve the full removal of the deposit layer and identify a clear interface between the deposit and the bulk tungsten substrate by applying 15–20 laser pulses while hydrogen isotopes decrease strongly after the first laser pulse. Under these conditions, the evolution of the spectral line intensities of W/Al/C/hydrogen can be used to evaluate the layer composition.     

Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine

In recent years, a laser-induced breakdown spectrometer (LIBS) combined with machine learning has been widely developed for steel classification. However, the much redundant information of LIBS spectra increases the computation complexity for classification. In this work, restricted Boltzmann machines (RBM) and principal component analysis (PCA) were used for dimension reduction of datasets, respectively. Then, a support vector machine (SVM) was adopted to process feature information. Two models (RBM-SVM and PCA-SVM) are compared in terms of performance. After optimization, the accuracy of the RBM-SVM model can achieve 100%, and the maximum dimension reduction time is 33.18 s, which is nearly half of that of the PCA model (53.19 s). These results preliminarily indicate that LIBS combined with RBM-SVM has great potential in the real-time classification of steel.     

Remote open-path laser-induced breakdown spectroscopy for the analysis of manganese in steel samples at high temperature

A remote open-path laser-induced breakdown spectroscopy(LIBS) system was designed and studied in the present work for the purpose of combining the LIBS technique with the steel production line. In this system, the relatively simple configuration and optics were employed to measure the steel samples at a remote distance and a hot sample temperature. The system has obtained a robustness for the deviation of the sample position because of the open-path and alloptical structure. The measurement was carried out at different sample temperatures by placing the samples in a muffle furnace with a window in the front door. The results show that the intensity of the spectral lines increased as the sample temperature increased. The influence of the sample temperature on the quantitative analysis of manganese in the steel samples was investigated by measuring ten standard steel samples at different temperatures. Three samples were selected as the test sample for the simulation measurement. The results show that, at the sample temperature of 500 ℃, the average relative error of prediction is 3.1% and the average relative standard deviation is 7.7%, respectively.     

Effect of distances between lens and sample surface on laser-induced breakdown spectroscopy with spatial confinement

Spatial confinement can significantly enhance the spectral intensity of laser-induced plasma in air.It is attributed to the compression of plasma plume by the reflected shockwave.In addition,optical emission spectroscopy of laser-induced plasma can also be affected by the distance between lens and sample surface.In order to obtain the optimized spectral intensity,the distance must be considered.In this work,spatially confined laser-induced silicon plasma by using a Nd:YAG nanosecond laser at different distances between lens and sample surface was investigated.The laser energies were 12 mJ,16 mJ,20 mJ,and 24 mJ.All experiments were carried out in an atmospheric environment.The results indicated that the intensity of Si (I) 390.55 nm line firstly rose and then dropped with the increase of lens-to-sample distance.Moreover,the spectral peak intensity with spatial confinement was higher than that without spatial confinement.The enhancement ratio was approximately 2 when laser energy was 24 mJ.     

A novel strategy for quantitative analysis of soil pH via laser-induced breakdown spectroscopy coupled with random forest

pH is one of the significant properties of soil,and is closely related to the decomposition of soil organic matter,anion-cation balance,growth of plants and many other soil processes.In the present work,laser-induced breakdown spectroscopy(LIBS) technique coupled with random forest(RF) was proposed to quantify the pH of soil.First,LIBS spectra of soil was collected,and some common elements in soil were identified based on the National Institute of Science and Technology database.Then,in order to obtain a better predictive result,the influence of different input variables(full spectrum,different spectral ranges,the intensity of characteristic bands and characteristic lines) on the predictive performance of RF calibration model was explored with the evaluation indicators of root mean square error(RMSE) and coefficient of determination(R2),the characteristic bands of four elements(AI,Ca,Mg and Si) were determined as the optimal input variables.Finally,the predictive performance of RF calibration model was compared with partial least squares calibration model with the optimal input variables and model parameters,and RF calibration model showed a better predictive performance,and the four evaluation indicators of R_p~2,RMSEP,mean absolute error and mean relative error were 0.9687,0.1285,0.1114 and 0.0136,respectively.It indicates that LIBS technique coupled with RF algorithm is an effective method for pH determination of soil.     

A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm

As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task.Classification methods based on laser-induced breakdown spectroscopy(LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest(RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve,as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value.     

Development of a fiber-coupled laser-induced breakdown spectroscopy instrument for analysis of underwater debris in a nuclear reactor core

To inspect the post-accident nuclear core reactor of the TEPCO Fukushima Daiichi nuclear power plant (F1-NPP), a transportable fiber-coupled laser-induced breakdown spectroscopy (LIBS) instrument has been developed. The developed LIBS instrument was designed to analyze underwater samples in a high-radiation field by single-pulse breakdown with gas flow or double-pulse breakdown. To check the feasibility of the assembled fiber-coupled LIBS instrument for the analysis of debris material (mixture of the fuel core, fuel cladding, construction material and so on) in the F1-NPP, we investigated the influence of the radiation dose on the optical transmittance of the laser delivery fiber, compared data quality among various LIBS techniques for an underwater sample and studied the feasibility of the fiber-coupled LIBS system in an analysis of the underwater sample of the simulated debris in F1-NPP. In a feasible study conducted by using simulated debris, which was a mixture of CeO₂ (surrogate of UO₂), ZrO₂ and Fe, we selected atomic lines suitable for the analysis of materials, and prepared calibration curves for the component elements. The feasible study has guaranteed that the developed fiber-coupled LIBS system is applicable for analyzing the debris materials in the F1-NPP.