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.

Application of near-infrared spectroscopy for fast germplasm analysis and classification in multi-environment using intact-seed peanut(Arachis hypogaea L.)

Peanut is a worldwide oilseed crop and the need to assess germplasm in a non-destructive manner is important for seed nutritional breeding.In this study,Near Infrared Spectroscopy(NIRS)was applied to rapidly assess germplasm variability from whole seed of 699 samples,field-collected and assembled in four genetic and environmentbased sets:one set of 300 varieties of a core-collection and three sets of 133 genotypes of an interspecific population,evaluated in three environments in a large spatial scale of two countries,Mbalmayo and Bafia in Cameroon and Nioro in Senegal,under rainfed conditions.NIR elemental spectra were gathered on six subsets of seeds of each sample,after three rotation scans,with a spectral resolution of 16 cm-1over the spectral range of867 nm to 2530 nm.Spectra were then processed by principal component analysis(PCA)coupled with Partial least squares-discriminant analysis(PLS-DA).As results,a huge variability was found between varieties and genotypes for all NIR wavelength within and between environments.The magnitude of genetic variation was particularly observed at 11 relevant wavelengths such as 1723 nm,usually related to oil content and fatty acid composition.PCA yielded the most chemical attributes in three significant PCs(i.e.,eigenvalues>10),which together captured 93%of the total variation,revealing genetic and environment structure of varieties and genotypes into four clusters,corresponding to the four samples sets.The pattern of genetic variability of the interspecific population covers,remarkably half of spectrum of the core-collection,turning out to be the largest.Interestingly,a PLS-DA model was developed and a strong accuracy of 99.6%was achieved for the four sets,aiming to classify each seed sample according to environment origin.The confusion matrix achieved for the two sets of Bafia and Nioro showed 100%of instances classified correctly with 100%at both sensitivity and specificity,confirming that their seed quality was different from each other and all other samples.Overall,NIRS chemometrics is useful to assess and distinguish seeds from different environments and highlights the value of the interspecific population and core-collection,as a source of nutritional diversity,to support the breeding efforts.

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.

Spectra-assisted laser focusing in quantitative analysis of laser-induced breakdown spectroscopy for copper alloys

Laser-induced breakdown spectroscopy(LIBS)is a capable technique for elementary analysis,while LIBS quantitation is still under development.In quantitation,precise laser focusing plays an important role because it ensures the distance between the laser and samples.In the present work,we employed spectral intensity as a direct way to assist laser focusing in LIBS quantitation for copper alloys.It is found that both the air emission and the copper line could be used to determine the position of the sample surface by referencing the intensity maximum.Nevertheless,the fine quantitation was only realized at the position where the air emission(e.g.O(I)777.4 nm)reached intensity maximum,and also in this way,a repeatable quantitation was successfully achieved even after 120 days.The results suggested that the LIBS quantitation was highly dependent on the focusing position of the laser,and spectra-assisted focusing could be a simple way to find the identical condition for different samples’detection.In the future,this method might be applicable in field measurements for LIBS analysis of solids.

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.

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.

Laser Induced Breakdown Spectroscopy for Comparative Quantitative Analysis of Betel Leaves from Pakistan

Laser induced breakdown Spectroscopy(LIBS)was applied for the elemental analysis and exposure of the heavy metals in betel leaves in air.Pulsed Nd∶YAG(1064 nm)in conjunction with a suitable detector(LIBS 2000+,Ocean Optics,Inc)having the optical resolution of 0.06 nm was used to record the emission spectra from 220 to 720 nm.Elements like Al,Ba,Ca,Cr,Cu,P,Fe,K,Mg,Mn,Na,P,S,Sr,and Zn were found to present in the samples.The relative abundances of the observed elements were calculated through standard calibration curve method,integrated intensity ratio method,and weight percentage LIBS approach.LIBS findings were validated by comparing its results with the results obtained using a typical analytical technique of Inductively Coupled plasma-optical emission spectroscopy(ICP-OES).Limit of detection(LOD)of the LIBS system was also estimated for heavy metals.The experience gain through this work implies that LIBS could be highly applicable for testing the quality and purity of food products.

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.

Portable fiber-optic laser-induced breakdown spectroscopy system for the quantitative analysis of minor elements in steel

In this paper, we developed a portable laser-induced breakdown spectroscopy(LIBS) using an optical fiber to deliver laser energy and used it to quantitatively analyze minor elements in steel.The R^2 factors of calibration curves of elements Mn, Ti, V, and Cr in pig iron were 0.9965,0.9983, 0.9963, and 0.991, respectively, and their root mean square errors of cross-validation were 0.0501, 0.0054, 0.0205, and 0.0245 wt%, respectively. Six test samples were used for the validation of the performance of the calibration curves established by the portable LIBS. The average relative errors of elements Mn, Ti, V, and Cr were 2.5%, 11.7%, 13.0%, and 5.6%,respectively. These results were comparable with most results reported in traditional LIBS in steel or other matrices. However, the portable LIBS is flexible, compact, and robust, providing a promising prospect in industrial application.

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.

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 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(H_(2))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 twoline 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 Hαemission lines with good signalto-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.

A Semi-Quantitative Analysis of Essential Micronutrient in Folium Lycii Using Laser-Induced Breakdown Spectroscopy Technique

Abstract In this paper, the capabilities of laser-induced breakdown spectroscopy （LIBS） for rapid analysis to multi-component plant are illustrated using a 1064 nm laser focused onto the surface of folium lycii. Based on homogeneous plasma assumption, nine of essential micronutrients in folium lycii are identified. Using Saha equation and Boltzmann plot method electron density and plasma temperature are obtained, and their relative concentration （Ca, Mg, A1, Si, Ti, Na, K, Li, and Sr） are obtained employing a semi-quantitative method.

Change of choline compounds in sodium selenite-induced apoptosis of rats used as quantitative analysis by in vitro 9.4T MR spectroscopy

AIM: To study liver cell apoptosis caused by the toxicity of selenium and observe the alteration of choline compounds using in vitro 9.4T high resolution magnetic resonance spectroscopy. METHODS: Twenty male Wistar rats were randomly divided into two groups. The rats in the treatment group were intraperitoneally injected with sodium selenite and the control group with distilled water. All rats were sacrifi ced and the livers were dissected. 1H-MRS data were collected using in vitro 9.4T high resolution magnetic resonance spectrometer. Spectra were processed using XWINNMR and MestRe-c 4.3. HE and TUNEL staining was employed to detect and confi rm the change of liver cells. RESULTS: Good 1H-MR spectra of perchloric acid extract from liver tissue of rats were obtained. The conventional metabolites were detected and assigned. Concentrations of different ingredient choline compounds in treatment group vs control group were as follows: total choline compounds,5.08 ± 0.97 mmol/L vs 3.81 ± 1.16 mmol/L (P = 0.05); and free choline,1.07 ± 0.23 mmol/L vs 0.65 ± 0.20 mmol/L (P = 0.00). However,there was no statistical signif icance between the two groups. The hepatic sinus and cellular structure of hepatic cells in treatmentgroup were abnormal. Apoptosis of hepatic cells was confi rmed by TUNEL assay. CONCLUSION: High dose selenium compounds can cause the rat liver lesion and induce cell apoptosis in vivo. High resolution 1H-MRS in vitro can detect diversified metabolism. The changing trend for different ingredient of choline compounds is not completely the same at early period of apoptosis.

Quantitative analysis of impurities in aluminum alloys by laser-induced breakdown spectroscopy without internal calibration

To develop a fast and sensitive alloy elemental analysis method,a laser-induced breakdown spectroscopy(LIBS)system was established and used to carry out quantitative analysis of impurities in aluminum alloys in air at atmospheric pressure.A digital storage oscilloscope was used as signal recording instrument,instead of traditional gate integrator or Boxcar averager,to reduce the cost of the whole system.Linear calibration curves in the concentration range of 4×10-510-2 are built for Mg,Cr,Mn,Cu and Zn using absolute line intensity without internal calibrations.Limits of detection for these five elements in aluminum alloy are determined to be(2-90)×10-6.It is demonstrated that LIBS can provide quantitative trace elemental analysis in alloys even without internal calibration.This approach is easy to use in metallurgy industries and relative research fields.