Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

Estimating the yield stress of soft materials via laser-induced breakdown spectroscopy

Taking three typical soft samples prepared respectively by loose packings of 77-,95-,and 109-μm copper grains as examples,we perform an experiment to investigate the energy-dependent laser-induced breakdown spectroscopy(LIBS)of soft materials.We discovered a reversal phenomenon in the trend of energy dependence of plasma emission intensity:increasing initially and then decreasing separated by a well-defined critical energy.The trend reversal is attributed to the laser-induced recoil pressure at the critical energy just matching the sample's yield strength.As a result,a one-to-one correspondence can be well established between the samples'yield stress and the critical energy that is easily obtainable from LIBS measurements.This allows us to propose an innovative method for estimating the yield stress of soft materials via LIBS with attractive advantages including in-situ remote detection,real-time data collection,and minimal destructive to sample.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy(LIBS)is investigated experimentally.The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness,ranging from 0 to 4.4 mm,by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm.It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases,implying that the method investigated here is feasible.It is also found that,for achieving the inspection of surface flatness within such a wide range,when univariate analysis is applied,a piecewise calibration model must be constructed.This is due to the complex dependence of plasma formation conditions on the surface flatness,which inevitably complicates the inspection procedure.To solve the problem,a multivariate analysis with the help of Back-Propagation Neural Network(BPNN)algorithms is applied to further construct the calibration model.By detailed analysis of the model performance,we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy(LIBS).In this situation,a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size.In the present work,we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes.Specifically,two unified multivariate calibration models are constructed based on back-propagation neural network(BPNN)algorithms using feature selection strategies with and without considering prior information.By detailed analysis of the performances of the two multivariate models,it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers.It was also found that the model constructed with a priorguided feature selection strategy had better prediction performance.This study has practical significance in developing the technology for material analysis using LIBS,especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy

Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy(LIBS).Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance,a data selection method based on plasma temperature matching(DSPTM)was proposed to reduce both matrix effects and signal uncertainty.By selecting spectra with smaller plasma temperature differences for all samples,the proposed method was able to build up the quantification model to rely more on spectra with smaller matrix effects and signal uncertainty,therefore improving final quantification performance.When applied to quantitative analysis of the zinc content in brass alloys,it was found that both accuracy and precision were improved using either a univariate model or multiple linear regression(MLR).More specifically,for the univariate model,the root-mean-square error of prediction(RMSEP),the determination coefficients(R^(2))and relative standard derivation(RSD)were improved from 3.30%,0.864 and 18.8%to 1.06%,0.986 and 13.5%,respectively;while for MLR,RMSEP,R^(2)and RSD were improved from 3.22%,0.871 and 26.2%to 1.07%,0.986 and 17.4%,respectively.These results prove that DSPTM can be used as an effective method to reduce matrix effects and improve repeatability by selecting reliable data.

Self-absorption effects of laser-induced breakdown spectroscopy under different gases and gas pressures

The self-absorption effect is one of the main factors affecting the quantitative analysis accuracy of laser-induced breakdown spectroscopy.In this paper,the self-absorption effects of laserinduced 7050 Al alloy plasma under different pressures in air,Ar,and N2have been studied.Compared with air and N2,Ar significantly enhances the spectral signal.Furthermore,the spectral self-absorption coefficient is calculated to quantify the degree of self-absorption,and the influences of gas species and gas pressure on self-absorption are analyzed.In addition,it is found that the spectral intensity fluctuates with the change of pressure of three gases.It can also be seen that the fluctuation of spectral intensity with pressure is eliminated after correcting,which indicates that the self-absorption leads to the fluctuation of spectral intensity under different pressures.The analysis shows that the evolution of optical thin spectral lines with pressure in different gases is mainly determined by the gas properties and the competition between plasma confinement and Rayleigh–Taylor instability.

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 that carbonization can remove a significant amount of the contributions of organic elements C,H,N and O;meanwhile,the signals of the metallic elements such as Cu,Pb,Sr,Ca,Cr and Mg are enhanced by3–6 times after carbonization,and further enhanced by 5–9 times after ashing.Such enhancement is not only due to the more concentrated metallic elements in the sample compared to the dried ones,but also the unifying of the matter in carbonized and ashed samples from which higher plasma temperature and electron density are observed.This condition favors the detection of trace elements.According to the calibration curves with univariate and multivariate analysis,the ashing method is considered to be the best choice.The limits of detection of the ashing method are 0.52 mg kg-1 for Pb and0.08 mg kg-1 for Cr,which can detect the presence of heavy metals in the oysters exceeding the maximum limits of Pb and Cr required by the Chinese national standard.This method provides a promising application for the heavy metal contamination monitoring in the aquatic product industry.

Matrix effect suppressing in the element analysis of soils by laser-induced breakdown spectroscopy with acoustic correction

Laser-induced breakdown spectroscopy(LIBS)has been used for soil analysis,but its measurement accuracy is often influenced by matrix effects of different kinds of soils.In this work,a method for matrix effect suppressing was developed using laser-induced plasma acoustic signals to correct the original spectrum,thereby improving the analysis accuracy of the soil elements.A good linear relationship was investigated firstly between the original spectral intensity and the acoustic signals.The relative standard deviations(RSDs)of Mg,Ca,Sr,and Ba elements were then calculated for both the original spectrum and the spectrum with the acoustic correction,and the RSDs were significantly reduced with the acoustic correction.Finally,calibration curves of MgⅠ285.213 nm,CaⅠ422.673 nm,SrⅠ460.733 nm and BaⅡ455.403 nm were established to assess the analytical performance of the proposed acoustic correction method.The values of the determination coefficient(R~2)of the calibration curves for Mg,Ca,Sr,and Ba elements,corrected by the acoustic amplitude,are improved from 0.9845,0.9588,0.6165,and 0.6490 to 0.9876,0.9677,0.8768,and 0.8209,respectively.The values of R~2 of the calibration curves corrected by the acoustic energy are further improved to 0.9917,0.9827,0.8835,and 0.8694,respectively.These results suggest that the matrix effect of LIBS on soils can be clearly improved by using acoustic correction,and acoustic energy correction works more efficiently than acoustic amplitude correction.This work provides a simple and efficient method for correcting matrix effects in the element analysis of soils by acoustic signals.

Analysis of Pulverized Coal by Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy （LIBS） has been used to detect atomic species in various enviromnents. The quantitative analysis （C, H, O, N and S） of representative coal samples are being carried out with LIBS, and the effects of particle size are analyzed. A powerful pulse Nd：YAG laser is focused on the coal sample at atmosphere pressure, and the emission spectra from laser-induced plasmas are measured by time-resolved spectroscopy, and the intensity of analyzed spectral lines is obtained through observing the laser plasma with a delay time of 0.4 #s. The experimental results show that the slope of calibration curve is nearly 1 when the concentration of the analyzed element is relatively low, and the slope of curve is nearly 0.5 when the concentration of C is higher than other elements. In addition, using the calibration-free model without self-absorption effect, the results show that the decreasing of particle size leads to an increase of the plasma temperature.

Rock and Soil Classification Using PLS-DA and SVM Combined with a Laser-Induced Breakdown Spectroscopy Library

Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.

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.

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,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.

A comparison of single shot nanosecond and femtosecond polarization-resolved laser-induced breakdown spectroscopy of Al

Aluminum samples have been analyzed by femtosecond polarization-resolved laser-induced breakdown spectroscopy （fs-PRLIBS）. We compare the obtained spectra with those obtained from nanosecond PRLIBS （ns-PRLIBS）. The main specific features of fs-PRLIBS are that a lower plasma temperature leads to a low level of continuum and no species are detected from the ambient gas. Furthermore, signals obtained by fs-PRLIBS show a higher stability than those of ns-PRLIBS. However, more elements are detected in the ns-PRLIBS spectra.

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.

Determination of Iron in Water Solution by Time-Resolved Femtosecond Laser-Induced Breakdown Spectroscopy

The influence of the energy of femtosecond laser pulses on the intensity of Fe I （371.99 nm） emission line and the continuous spectrum of the plasma generated on the surface of Fe^3＋ water solution by a Ti： sapphire laser radiation with pulse duration 〈45 fs and energies up to 7 mJ is determined. A calibration curve was obtained for Fe3＋ concentration range from 0.5 g/L to the limit of detection in water solution, and its saturation was detected for concentrations above 0.25 g/L, which is ascribed to self-absorption. The 3σ- limit of detection obtained for Fe in water solution is 2.6 mg/L in the case of 7 mJ laser pulse energy. It is found that an increase of laser pulse energy insignificantly affects on LOD in the time-resolved LIBS and leads to a slight improvement of the limit of detection.

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.

A Rising Force for the World-Wide Development of Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy （LIBS） has attracted many academic and industrial interests world-wide due to its unique advantages, such as little or no sample preparation requirement, in-situ/online and multi-elemental analysis, and remote sensing etc., and it has been regarded as a ＂future super star＂ for chemical analysis for many years . In China,

Improved Measurement Performance of Inorganic Elements in Coal by Laser-Induced Breakdown Spectroscopy Coupled with Internal Standardization

Laser-induced breakdown spectroscopy was employed to determine the inorganic elements in coal. To improve the measurement＇s accuracy and precision, a new internal standardization scheme, which we named changed internal standardization, was compared with the traditional internal standardization and no internal standardization for the analysis of inorganic elements. The new internal standardization scheme used the atomic line of carbon at 247.86 nm and the molecular band of CN at 388.34 nm and C2 at 516.32 nm to normalize the lines of inorganic elements that were distributed in the same spectral channel. The performance of the utilization of the new internal standardization scheme was evaluated using a set of coal samples, including twenty calibration samples and five validation samples. The results show that the coefficients of determination R2 and the slope of calibration models coupled with changed internal standard- ization are better than that of the calibration models coupled with fixed internal standardization and no internal standardization. Moreover, the measurement accuracy and reproducibility of changed internal standardization for the analysis of five validation samples also yielded further improvement. The results that we obtained suggest that changed internal standardization could compensate for the matrix effects, as well as the influence of the difference in the spectral response of the light collection system.