Rapid online analysis of trace elements in steel using a mobile fiber-optic laser-induced breakdown spectroscopy system

A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R~2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements' strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.     

An efficient procedure in quantitative analysis using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy has become a general-purpose technique, and internal standard calibration is a common method for quantitative analysis. Calibration models should be reconstructed for different systems and application environments. This study presents an efficient procedure in the construction and selection of calibration models for LIBS analysis. The procedure concludes data preprocess, calibration model construction, and concentration calculation. These steps can be programmed without manual intervention. Results of the quantitative analysis of Ni-based alloys using the proposed procedure are presented in this study.Ten elements are calibrated, and most have an average relative standard error of less than 10%.The proposed procedure is an effective process for constructing and selecting calibration models.     

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.     

Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means

Laser-induced breakdown spectroscopy(LIBS) combined with K-means algorithm was employed to automatically differentiate industrial polymers under atmospheric conditions.The unsupervised learning algorithm K-means were utilized for the clustering of LIBS dataset measured from twenty kinds of industrial polymers.To prevent the interference from metallic elements,three atomic emission lines(C I 247.86 nm,H I 656.3 nm,and O I 777.3 nm) and one molecular line C–N(0,0) 388.3 nm were used.The cluster analysis results were obtained through an iterative process.The Davies–Bouldin index was employed to determine the initial number of clusters.The average relative standard deviation values of characteristic spectral lines were used as the iterative criterion.With the proposed approach,the classification accuracy for twenty kinds of industrial polymers achieved 99.6%.The results demonstrated that this approach has great potential for industrial polymers recycling by LIBS.     

Evaluation of gases'molecular abundance ratio by fiber-optic laser-induced breakdown spectroscopy with a metal-assisted method

A metal-assisted method is proposed for the evaluation of gases'molecular abundance ratio in fiber-optic laser-induced breakdown spectroscopy(FO-LIBS).This method can reduce the laser ablation energy and make gas composition identification possible.The principle comes from the collision between the detected gases and the plasma produced by the laser ablation of the metal substrate.The interparticle collision in the plasma plume leads to gas molecules dissociating and sparking,which can be used to determine the gas composition.The quantitative relationship between spectral line intensity and molecular abundance ratio was developed over a large molecular abundance ratio range.The influence of laser ablation energy and substrate material on gas quantitative calibration measurement is also analyzed.The proposed metal-assisted method makes the measurement of gases'molecular abundance ratios possible with an FO-LIBS system.     

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.     

Quantitative analysis of C,Si, Mn,Ni, Cr and Cu in low-alloy steel under ambient conditions via laser-induced breakdown spectroscopy

A diode-pumped solid-state laser(DPSSL) with a high energetic stability and long service life is applied to ablate the steel samples instead of traditional Nd:YAG laser pumped by a xenon lamp,and several factors, such as laser pulse energy, repetition rate and argon flow rate, that influence laser-induced breakdown spectroscopy(LIBS) analytical performance are investigated in detail.Under the optimal experiment conditions, the relative standard deviations for C, Si, Mn, Ni, Cr and Cu are 3.3%–8.9%, 0.9%–2.8%, 1.2%–4.1%, 1.7%–3.0%, 1.1%–3.4% and 2.5%–8.5%,respectively, with the corresponding relative errors of 1.1%–7.9%, 1.0%–6.3%, 0.4%–3.9%,1.5%–6.3%, 1.2%–4.0% and 1.2%–6.4%. Compared with the results of the traditional spark discharge optical emission spectrometry technique, the analytical performance of LIBS is just a little inferior due to the less stable laser-induced plasma and smaller amount of ablated sample by the laser. However, the precision, detection limits and accuracy of LIBS obtained in our present work were sufficient to meet the requirements for process analysis. These technical performances of higher stability of output energy and longer service life for DPSSL, in comparison to the Q-switch laser pumped by xeon lamp, qualify it well for the real time online analysis for different industrial applications.     

Improvement in classification accuracy of stainless steel alloys by laser-induced breakdown spectroscopy based on elemental intensity ratio analysis

Laser-induced breakdown spectroscopy(LIBS) is a useful technique for accurate sorting of metal scrap by chemical composition analysis.In this work,a method for intensity-ratiobased LIBS classification of stainless steel applicable to highly fluctuating LIBS signal conditions is proposed.The spectral line pairs for intensity ratio calculation are selected according to elemental concentration and upper levels of emission lines.It is demonstrated that the classification accuracy can be significantly improved from that of full-spectra principal component analysis or intensity-based analysis.The proposed method is considered to be suited to an industrial scrap sorting system that requires minimal maintenance and low system price.     

Quantitative analysis modeling for the ChemCam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network

Laser-induced breakdown spectroscopy (LIBS) has been applied to many fields for the quantitative analysis of diverse materials. Improving the prediction accuracy of LIBS regression models is still of great significance for the Mars exploration in the near future. In this study, we explored the quantitative analysis of LIBS for the one-dimensional ChemCam (an instrument containing a LIBS spectrometer and a Remote Micro-Imager) spectral data whose spectra are produced by the ChemCam team using LIBS under the Mars-like atmospheric conditions. We constructed a convolutional neural network (CNN) regression model with unified parameters for all oxides, which is efficient and concise. CNN that has the excellent capability of feature extraction can effectively overcome the chemical matrix effects that impede the prediction accuracy of regression models. Firstly, we explored the effects of four activation functions on the performance of the CNN model. The results show that the CNN model with the hyperbolic tangent (tanh) function outperforms the CNN models with the other activation functions (the rectified linear unit function, the linear function and the Sigmoid function). Secondly, we compared the performance among the CNN models using different optimization methods. The CNN model with the stochastic gradient descent optimization and the initial learning rate=0.0005 achieves satisfactory performance compared to the other CNN models. Finally, we compared the performance of the CNN model, the model based on support vector regression (SVR) and the model based on partial least square regression (PLSR). The results exhibit the CNN model is superior to the SVR model and the PLSR model for all oxides. Based on the above analysis, we conclude the CNN regression model can effectively improve the prediction accuracy of LIBS.     

Detection of heavy metals in water samples by laser-induced breakdown spectroscopy combined with annular groove graphite flakes

The use of laser-induced breakdown spectroscopy(LIBS) for the analysis of heavy metals in water samples is investigated. Some factors such as splashing, surface ripples, extinction of emitted intensity, and a shorter plasma lifetime will influence the results if the water sample is directly measured. In order to avoid these disadvantages and the ‘coffee-ring effect', hydrophilic graphite flakes with annular grooves were used for the first time to enrich and concentrate heavy metals in water samples before being analyzed by LIBS. The proposed method and procedure have been evaluated to concentrate and analyze cadmium, chromium, copper, nickel, lead,and zinc in a water sample. The correlation coefficients were all above 0.99. The detection limits of 0.029, 0.087, 0.012, 0.083, 0.125, and 0.049 mgl~(-1) for Cd, Cr, Cu, Ni, Pb, and Zn,respectively, were obtained in samples prepared in a laboratory. With this structure, the heavy metals homogeneously distribute in the annular groove and the relative standard deviations are all below 6%. This method is very convenient and suitable for online in situ analysis of heavy metals.     

Experimental study on mercury content in flue gas of coal-fired units based on laser-induced breakdown spectroscopy

Accurate measurement of trace heavy metal mercury(Hg) in flue gas of coal-fired units is great significance for ecological and environmental protection.Mixed gas was used to simulate the actual flue gas of a power plant in this study.A laser-induced breakdown spectroscopy(LIBS)system for Hg measurement in mixed gas was built to study the effect of mixed gas pressure,Hg concentration in mixed gas and delay time on Hg measurement.The experimental results show that the appropriate low mixed gas pressure can obtain high Hg signal intensity and signal to noise ratio.The Hg signal intensity and signal to noise ratio increased with the increase of Hg concentration in mixed gas.The Hg signal intensity and signal to noise ratio decreased with the increase in delay time.According to the above results,the optimized measurement conditions can be determined.Different Hg concentrations in mixed gas were quantitatively analyzed by the internal standard method and traditional calibration method respectively.The relative error of prediction of the test sample obtained by the internal standard method was within 11.11%.The relative error of prediction of the traditional calibration method was less than 14.54%.This proved that the internal standard method can improve the accuracy of quantitative analysis of Hg concentration in flue gas using LIBS.     

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.     

Comparison of emission signals for different polarizations in femtosecond laser-induced breakdown spectroscopy

In this study, a femtosecond laser was focused to ablate brass target and generate plasma emission in air. The influence of lens to sample distance(LTSD) on spectral emission of brass plasma under linearly and circularly polarized pulses with different pulse energies was investigated. The results indicated that the position with the strongest spectral emission moved toward focusing lens with increasing the energy. At the same laser energy, the line emission under circularly polarized pulse was stronger compared with linearly polarized pulse for different LTSDs. Next, electron temperature and density of the plasma were obtained with Cu(Ⅰ) lines,indicating that the electron temperature and density under circularly polarized pulse were higher compared to that under linearly polarized pulse. Therefore, changing the laser polarization is a simple and effective way to improve the spectral emission intensity of femtosecond laserinduced plasma.     

Improvement of quantitative analysis of molybdenum element using PLS-based approaches for laser-induced breakdown spectroscopy in various pressure environments

An experimental setup has been designed and realized in order to optimize the characteristics of laser-induced breakdown spectroscopy system working in various pressure environments. An approach combined the normalization methods with the partial least squares(PLS) method are developed for quantitative analysis of molybdenum(Mo) element in the multi-component alloy,which is the first wall material in the Experimental Advanced Superconducting Tokamak. In this study, the different spectral normalization methods(total spectral area normalization,background normalization, and reference line normalization) are investigated for reducing the uncertainty and improving the accuracy of spectral measurement. The results indicates that the approach of PLS based on inter-element interference is significantly better than the conventional PLS methods as well as the univariate linear methods in the various pressure for molybdenum element analysis.     

Comparison of sample temperature effect on femtosecond and nanosecond laser-induced breakdown spectroscopy

In this paper, we investigated the emission spectra of plasmas produced from femtosecond and nanosecond laser ablations at different target temperatures in air. A brass was selected as ablated target of the experiment. The results indicated that spectral emission intensity and plasma temperature showed similar trend for femtosecond and nanosecond lasers, and the two parameters were improved by increasing the sample temperature in both cases. Moreover, the temperature of nanosecond laser-excited plasma was higher compared with that of femtosecond laser-excited plasma, and the increase of the plasma temperature in the case of nanosecond laser was more evident. In addition, there was a significant difference in electron density between femtosecond and nanosecond laser-induced plasmas. The electron density for femtosecond laser decreased with increasing the target temperature, while for nanosecond laser, the electron density was almost unchanged at different sample temperatures.     

Signal processing for real-time identification of similar metals by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) is regarded as a promising technique for realtime sorting of scrap metals due to its capability of fast multi-elemental and in-air analysis. This work reports a method for signal processing which ensures high accuracy and high speed during similar metal sorting by LIBS. Similar metals such as aluminum alloys or stainless steel are characterized by nearly the same constituent elements with slight variations in elemental concentration depending on metal type. In the proposed method, the original data matrix is substantially reduced for fast processing by selecting new input variables(spectral lines) using the information for the constituent elements of similar metals. Specifically, principal component analysis(PCA) of full-spectra LIBS data was performed and then, based on the loading plots, the input variables of greater significance were selected in the order of higher weights for each constituent element. The results for the classification test with aluminum alloy, copper alloy,stainless steel and cast steel showed that the classification accuracy of the proposed method was nearly the same as that of full-spectra PCA, but the computation time was reduced by a factor of 20 or more. The results demonstrated that incorporating the information for constituent elements can significantly accelerate classification speed without loss of accuracy.     

Preliminary results of in situ laser-induced breakdown spectroscopy for the first wall diagnostics on EAST

Post-mortem methods cannot fulfill the requirement of monitoring the lifetime of the plasma facing components(PFC) and measuring the tritium inventory for the safety evaluation.Laserinduced breakdown spectroscopy(LIBS) is proposed as a promising method for the in situ study of fuel retention and impurity deposition in a tokamak.In this study,an in situ LIBS system was successfully established on EAST to investigate fuel retention and impurity deposition on the first wall without the need of removal tiles between plasma discharges.Spectral lines of D,H and impurities(Mo,Li,Si,...) in laser-induced plasma were observed and identified within the wavelength range of 500-700 nm.Qualitative measurements such as thickness of the deposition layers,element depth profile and fuel retention on the wall are obtained by means of in situ LIBS.The results demonstrated the potential applications of LIBS for in situ characterization of fuel retention and co-deposition on the first wall of EAST.     

Spectral characteristics of underwater laser-induced breakdown spectroscopy under high-pressure conditions

Laser-induced breakdown spectroscopy(LIBS) has been proven to be an attractive technique for in situ oceanic applications.However,when applying LIBS into deep-sea,the pressure effect caused by different ocean depths is inescapable and could have great influence on the LIBS signals.In this work,spectral characteristics of underwater LIBS were investigated as a function of pressure in the range of 0.1-45 MPa.A high-pressure chamber built in the laboratory was used to simulate the high-pressure deep-sea environment.Optimal laser energy and detection delay were first determined under different pressure conditions and were shown to be independent of the external pressure.The increase in pressure has a significant impact both on the peak intensity and line broadening of the observed spectra.The peak intensity of Na,Li and K lines increases with the increasing pressure until a maximum intensity is reached at 12.5 MPa.Above this value,the peak intensity decreases gradually up to 45 MPa.For Ca line,the maximum intensity was observed at 30 MPa.The line broadening keeps constant at low pressures from 0.1-10 MPa,while it increases linearly at higher pressures,indicating a higher electron density caused by the compression effect of the high external pressure.We also compared the spectral data obtained from the high-pressure chamber and from the field sea trials,and the good consistency between the laboratory data and sea-trial data suggested the key role of pressure effect on underwater LIBS signals for practical deep-sea applications.     

Investigation of the factors affecting the limit of detection of laser-induced breakdown spectroscopy for surface inspection

Laser-induced breakdown spectroscopy(LIBS) was examined to detect a trace substance adhered onto Al alloys for the surface inspection of materials to be adhesively bonded. As an example of Si contamination, silicone oil was employed and sprayed onto substrates with a controlled surface concentration. LIBS measurements employing nanosecond UV pulses(λ?=?266 nm) and an off-axis emission collection system with different detecting heights were performed. Because surface contaminants are involved in the plasma formed by laser ablation of the substrates, the relative contribution of the surface contaminants and the substrates to the plasma emission could be changed depending on the conditions for plasma formation. The limit of detection(LOD) was evaluated under several detecting conditions for investigating the factors that affected the LOD. A significant factor was the standard deviation values of signal intensities obtained for the clean substrates. This value varied depending on the measurement conditions.For the Al alloy(A6061), the smallest LOD obtained was 0.529 μg?·?cm~(-2). Furthermore, an improved LOD(0.299 μg?·?cm~(-2)) was obtained for the Al alloy with a lower Si content.     

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.