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.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications.

Nondestructive Quantitative Analysis of Cofrel Medicines by Double ANN-NIR Spectroscopy

In this paper, a double artificial neural network （DANN） algorithm was used to parse near infrared （NIR） reflectance spectrum of Cofrel medicines. The contents of benproperine phosphate, which is the effective ingredient in Cofrel medicines, were accurately nondestructive quantitatively predicted. Compared the results with those of HPLC, the relative errors （RE %） were less than 0.18%. The analytical results could be applied to qualitative control of Cofrel medicines.

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.

A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy

In the spectral analysis of laser-induced breakdown spectroscopy,abundant characteristic spectral lines and severe interference information exist simultaneously in the original spectral data.Here,a feature selection method called recursive feature elimination based on ridge regression(Ridge-RFE)for the original spectral data is recommended to make full use of the valid information of spectra.In the Ridge-RFE method,the absolute value of the ridge regression coefficient was used as a criterion to screen spectral characteristic,the feature with the absolute value of minimum weight in the input subset features was removed by recursive feature elimination(RFE),and the selected features were used as inputs of the partial least squares regression(PLS)model.The Ridge-RFE method based PLS model was used to measure the Fe,Si,Mg,Cu,Zn and Mn for 51 aluminum alloy samples,and the results showed that the root mean square error of prediction decreased greatly compared to the PLS model with full spectrum as input.The overall results demonstrate that the Ridge-RFE method is more efficient to extract the redundant features,make PLS model for better quantitative analysis results and improve model generalization ability.

In Deep UV Quantitative Analysis of Multi-Element Low Alloy Steel by Laser-Induced Breakdown Spectroscopy

The multi-element components of low alloy steel were quantified by using laser-induced breakdown spectroscopy (LIBS) in deep UV. The Nd:YAG pulsed laser was used to produce plasma. The spectrum was simultaneously obtained by deep UV spectrometer. This paper studied the influence of experiment parameters on LIBS spectral intensity, such as delay, energy of laser, and the distance between the focusing lens and the surface of the sample. With the optimal expe- riment parameters, the characteristic lines of C, Ni, Si, Cr and Cu contained in low alloy steel were selected for quantit- ative analysis and the calibration curves of these elements were obtained. The linear correlation coefficient was good. Using the calibration curves to quantitative analysis for the sample 05-d, and the relative error of analytical results is less than 10% for most elements.

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.

Comparison of Calibration Curve Method and Partial Least Square Method in the Laser Induced Breakdown Spectroscopy Quantitative Analysis

The Laser Induced Breakdown Spectroscopy (LIBS) is a fast, non-contact, no sample preparation analytic technology;it is very suitable for on-line analysis of alloy composition. In the copper smelting industry, analysis and control of the copper alloy concentration affect the quality of the products greatly, so LIBS is an efficient quantitative analysis tech- nology in the copper smelting industry. But for the lead brass, the components of Pb, Al and Ni elements are very low and the atomic emission lines are easily submerged under copper complex characteristic spectral lines because of the matrix effects. So it is difficult to get the online quantitative result of these important elements. In this paper, both the partial least squares (PLS) method and the calibration curve (CC) method are used to quantitatively analyze the laser induced breakdown spectroscopy data which is obtained from the standard lead brass alloy samples. Both the major and trace elements were quantitatively analyzed. By comparing the two results of the different calibration method, some useful results were obtained: both for major and trace elements, the PLS method was better than the CC method in quantitative analysis. And the regression coefficient of PLS method is compared with the original spectral data with background interference to explain the advantage of the PLS method in the LIBS quantitative analysis. Results proved that the PLS method used in laser induced breakdown spectroscopy was suitable for simultaneous quantitative analysis of different content elements in copper smelting 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.

Energy-dispersive X-ray Spectroscopy for the Quantitative Analysis of Pyrite Thin Specimens

To explore ways to improve the accuracy of quantitative analysis of samples in the micrometer to nanometer range of magnitudes,we adopted analytical transmission electron microscopy(AEM/EDS)for qualitative and quantitative analysis of pyrite materials.Additionally,the k factor of pyrite is calculated experimentally.To develop an appropriate non-standard quantitative analysis model for pyrite materials,the experimentally calculated k factor is compared with that estimated from the non-standard quantitative analytical model of the instrument software.The experimental findings demonstrate that the EDS attached to a TEM can be employed for precise quantitative analysis of micro-and nanoscale regions of pyrite materials.Furthermore,it serves as a reference for improving the results of the EDS quantitative analysis of other sulfides.

Rapid and real-time analysis of multi-component dissolved gas in seawater by Raman spectroscopy combined with continuous gas-liquid separator

Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.

Stable and unique graphitic Raman internal standard nanocapsules for surface-enhanced Raman spectroscopy quantitative analysis

Graphitic nanomaterials have unique, strong, and stable Raman vibrations that have been widely applied in chemistry and biomedicine. However, utilizing them as internal standards （ISs） to improve the accuracy of surface-enhanced Raman spectroscopy （SERS） analysis has not been attempted. Herein, we report the design of a unique IS nanostructure consisting of a large number of gold nanoparticles （AuNPs） decorated on multilayered graphitic magnetic nanocapsules （AGNs） to quantify the analyte and eliminate the problems associated with traditional ISs. The AGNs demonstrated a unique Raman band from the graphitic component, which was localized in the Raman silent region of the biomolecules, making them an ideal IS for quantitative Raman analysis without any background interference. The IS signal from the AGNs also indicated superior stability, even under harsh conditions. With the enhancement of the decorated AuNPs, the AGN nanostructures greatly improved the quantitative accuracy of SERS, in particular the exclusion of quantitative errors resulting from collection loss and non-uniform distribution of the analytes. The AGNs were further utilized for cell staining and Raman imaging, and they showed great promise for applications in biomedicine.

Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Qualitative and semi-quantitative analysis of health-care pharmaceutical products using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) is a sensitive optical technique that is capable of rapid multi-elemental analysis. The development of this technique for elemental analysis of pharmaceutical products may eventually revolutionize the field of human health. Under normal circumstances, the elemental analysis of pharmaceutical products based on chemical methods is time-consuming and complicated. In this investigation, the principal aim is to develop an LIBS-based methodology for elemental analysis of pharmaceutical products. This LIBS technique was utilized for qualitative as well as quantitative analysis of the elements present in Ca-based tablets. All the elements present in the tablets were detected and their percentage compositions were verified in a single shot, using the proposed instrument. These elements(e.g., Ca, Mg, Fe, Zn, and others) were identified by the wavelengths of their spectral lines, which were verified using the NIST database. The approximate amount of each element was determined based on their observed peaks and the result was in exact agreement with the content specification. The determination of the composition of prescription drug for patients is highly important in numerous circumstances. For example, the exploitation of LIBS may facilitate elemental decomposition of medicines to determine the accuracy of the stated composition information. Moreover, the approach can provide element-specific, meaningful, and accurate information related to pharmaceutical products.

Application of BAIRD Photoelectric Spectrometer in Quantitative Analysis of Iron and Steel

BAIRD SPECTROVAC 2000(DV 5) consisting of new type of HR-400 high repeat rate spark spectrosource, air cooled sample stand and an annular purged tungsten counter electrode has been used continuously in the lab for many years and resulted in good economic benefits. The paper describes the application of the spectrometer in quantitative analysis of cast iron and steel products, and the experience and technique may be helpful to those who are using the same kind of instrument.

Quantitative analysis modeling for the Chem Cam 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 Chem Cam(an instrument containing a LIBS spectrometer and a Remote Micro-Imager)spectral data whose spectra are produced by the Chem Cam 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.

Quantitative Analysis of Mg in Pipeline Dirt Based on Laser-Induced Breakdown Spectroscopy

In order to maintain the pipeline better and remove the dirt more effectively, it was necessary to analyze the contents of elements in dirt. Mg in soil outside of the pipe and the dirt inside of the pipe was quantitatively analyzed and compared by using the laser-induced breakdown spectroscopy （LIBS）. Firstly, Mg was quantitatively analyzed on the basis of Mg I 285.213 nm by calibration curve for integrated intensity and peak intensity of the spectrum before and after subtracting noise, respectively. Then calibration curves on the basis of Mg II 279.553 nm and Mg II 280.270 nm were analyzed. The results indicated that it is better to use integrated intensity after subtracting noise of the spectrum line with high relative intensity to make the calibration curve.

Mathematic Models for Analysis of Quality Components in Sugarcane Juice with Fourier Transform Near Infrared Spectroscopy

With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.

Quantitative Analysis of Heavy Metals in Water Based on LIBS with an Automatic Device for Sample Preparation

Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were prepared with an automatic device, which was composed of a loading and unloading module, a quantitatively adding solution module, a rapid heating and drying module and a precise rotating module. The experimental results showed that the sample preparation methods had no significant effect on sample distribution and the LIBS signal accumulated in 20 pulses was stable and repeatable. With an increasing amount of the sample solution on the graphite flake, the peak intensity at Cu I 324.75 nm accorded with the exponential function with a correlation coefficient of 0.9963 and the background intensity remained unchanged. The limit of detection （LOD） was calculated through linear fitting of the peak intensity versus the concentration. The LOD decreased rapidly with an increasing amount of sample solution until the amount exceeded 20 mL and the correlation coefficient of exponential function fitting was 0.991. The LOD of Pb, Ni, Cd, Cr and Zn after evaporating different amounts of sample solution on the graphite flakes was measured and the variation tendency of their LOD with sample solution amounts was similar to the tendency for Cu. The experimental data and conclusions could provide a reference for automatic sample preparation and heavy metal in situ detection.

Quantitative analysis of the main components in ceramic raw materials based on the desktop LIBS analyzer

The concentrations of SiO,Al2O,KO,NaO,CaO,MgO,Fe2Oand TiO,and loss on ignition(L.O.I.) are the main inorganic components of geological samples.Concentrations of the eight oxides and L.O.I.are also the main indicators of concern in the production of building ceramics.Quantitative analysis of the eight oxides and L.O.I.was performed using fiber-laserbased laser-induced breakdown spectroscopy(LIBS).A combination of continuous background deduction,full width at half maximum(FWHM) intensity integral and spectral sum normalization was proposed for data processing.After the data processing combined the continuous background deduction,FWHM intensity integral and spectral sum normalization,the mean absolute errors(MAEs) of the calibration of L.O.I.,SiO,Al2O,KO,NaO,CaO,MgO,Fe2Oand TiOwas reduced from 2.03%,12.06%,4.84%,1.10%,0.69%,0.31%,0.11%,0.20%and 0.10% to 1.80%,9.48%,2.12%,0.36%,0.58%,0.11%,0.08%,0.19% and 0.05%,respectively.This multivariate method was further introduced and discussed to improve the analysis performance.The MAEs of L.O.I.,SiO,Al2O,KO and NaO were further reduced to1.12%,2.07%,1.38%,0.35% and 0.43%,respectively.The results show that the overall prediction error can meet the requirements for the production of building ceramics.The LIBS desktop analyzer has great potential in detection applications on geological samples.