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1.
As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task.Classification methods based on laser-induced breakdown spectroscopy(LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest(RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve,as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value. 相似文献
2.
In this paper, we explore whether a feature selection method can improve model performance by using some classical machine learning models, artificial neural network, k-nearest neighbor,partial least squares-discrimination analysis, random forest, and support vector machine(SVM),combined with the feature selection methods, distance correlation coefficient(DCC), important weight of linear discriminant analysis(IW-LDA), and Relief-F algorithms, to discriminate eight species of wood(African rosewood, Brazilian bubinga, elm, larch, Myanmar padauk,Pterocarpus erinaceus, poplar, and sycamore) based on the laser-induced breakdown spectroscopy(LIBS) technique. The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis. The feature spectral lines are selected out based on the important weight assessed by DCC, IW-LDA,and Relief-F. All models are built by using the different number of feature lines(sorted by their important weight) as input. The relationship between the number of feature lines and the correct classification rate(CCR) of the model is analyzed. The CCRs of all models are improved by using a suitable feature selection. The highest CCR achieves(98.55...0.39)% when the SVM model is established from 86 feature lines selected by the IW-LDA method. The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS. 相似文献
3.
Kai RONG 《等离子体科学和技术》2020,22(7):74010-074010
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. 相似文献
4.
PAN Congyuan 《等离子体科学和技术》2015,17(8):682-686
The influence of a vacuum on the laser-induced breakdown spectroscopy(LIBS) of carbon in the ultraviolet wavelength range is studied.Experiments are performed with graphite using a LIBS system,which consists of a 1064 nm Nd:YAG laser,a vacuum pump,a spectrometer and a vacuum chamber.The vacuum varies from 10 Pa to 1 atm.Atomic lines as well as singly and doubly charged ions are confirmed under the vacuums.A temporal evolution analysis of intensity is performed for the atomic lines of C Ⅰ 193.09 nm and C Ⅰ 247.86 nm under different vacuum conditions.Both time-integrated and time-resolved intensity evolutions under vacuums are achieved.The lifetimes of the two atomic lines have similar trends,which supports the point of view of a 'soft spot'.Variations of plasma temperature and electron density under different vacuums are measured.This study is helpful for research on carbon detection using LIBS under vacuum conditions. 相似文献
5.
WANG Shaolong 《等离子体科学和技术》2015,17(8):716-720
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 MgⅡ 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. 相似文献
6.
HE Li’ao 《等离子体科学和技术》2016,18(6):647-653
Supervised learning methods(eg.PLS-DA,SVM,etc.) have been widely used with laser-induced breakdown spectroscopy(LIBS) to classify materials;however,it may induce a low correct classification rate if a test sample type is not included in the training dataset.Unsupervised cluster analysis methods(hierarchical clustering analysis,K-means clustering analysis,and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper.The results of hierarchical clustering analysis using four different similarity measuring methods(single linkage,complete linkage,unweighted pair-group average,and weighted pair-group average) are compared.In K-means clustering analysis,four kinds of choosing initial centers methods are applied in our case and their results are compared.The classification results of hierarchical clustering analysis,K-means clustering analysis,and ISODATA are analyzed.The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS. 相似文献
7.
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. 相似文献
8.
Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy(LIBS). A pulsed neodymium-doped yttrium aluminum garnet(Nd:YAG) laser(1,064 nm, 8 ns, 200 mJ) was focused onto pelletized soil samples. Emission spectra were obtained from oil-contaminated soil and clean soil. The contaminated soil had almost the same spectrum profile as the clean soil and contained the same major and minor elements. However, a C–H molecular band was clearly detected in the oil-contaminated soil, while no C–H band was detected in the clean soil. Linear calibration curve of the C–H molecular band was successfully made by using a soil sample containing various concentrations of oil. The limit of detection of the C–H band in the soil sample was 0.001 mL/g. Furthermore, the emission spectrum of the contaminated soil clearly displayed titanium(Ti) lines, which were not detected in the clean soil. The existence of the C–H band and Ti lines in oil-contaminated soil can be used to clearly distinguish contaminated soil from clean soil. For comparison, the emission spectra of contaminated and clean soil were also obtained using scanning electron microscope-energy dispersive X-ray(SEM/EDX) spectroscopy,showing that the spectra obtained using LIBS are much better than using SEM/EDX, as indicated by the signal to noise ratio(S/N ratio). 相似文献
9.
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. 相似文献
10.
Laser-induced breakdown spectroscopy(LIBS) is a promising analytical spectroscopy technology based on spectroscopic analysis of the radiation emitted by laser-produced plasma.However, for quantitative analysis by LIBS, the so-called self-absorption effects on the spectral lines, which affect plasma characteristics, emission line shapes, calibration curves, etc, can no longer be neglected. Hence, understanding and determining the self-absorption effects are of utmost importance to LIBS research. The purpose of this review is to provide a global overview of self-absorption in LIBS on the issues of experimental observations and adverse effects,physical mechanisms, correction or elimination approaches, and utilizations in the past century.We believe that better understanding and effective solving the self-absorption effect will further enhance the development and maturity of LIBS. 相似文献
11.
YANG Chun 《等离子体科学和技术》2015,17(8):671-675
In this paper,two types of comparison analyses,bulk analysis and defect analysis,were carried out for marine steel.The results of laser-induced breakdown spectroscopy(LIBS)were compared with those of spark optical emission spectrometry(Spark-OES) and scanning electron microscopy/energy dispersion spectroscopy(SEM/EDS) in the bulk and defect analyses.The comparison of the bulk analyses shows that the chemical contents of C,Si,Mn,P,S and Cr obtained from LIBS agree well with those determined using Spark-OES.The LIBS is slightly less precise than Spark-OES.Defects were characterized in the two-dimensional distribution analysis mode for Al,Mg,Ca,Si and other elements.Both the LIBS and SEM/EDS results show the enrichment of Al,Mg,Ca and Si at the defect position and the two methods agree well with each other.SEM/EDS cannot provide information about the difference in the chemical constituents when the differences between the defect position and the normal position are not significant.However,LIBS can provide this information,meaning that the sensitivity of LIBS is higher than that of SEM/EDS.LIBS can be used to rapidly characterize marine steel defects and provide guidance for improving metallurgical processes. 相似文献
12.
One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy (LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem, this paper investigated a combination of time-resolved LIBS and convolutional neural networks (CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R2c=0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network (ANN), showing R2v=0.6318 and the root mean square error of validation (RMSEV)=0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R2v=0.7366 and RMSEV=0.7855. These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K. However, due to limited calibration samples, the two-dimensional models presented over-fitting. The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R2v=0.9968 and RMSEV=0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil. 相似文献
13.
Duixiong SUN 《等离子体科学和技术》2022,24(8):84008
Laser-induced breakdown spectroscopy-assisted glow discharge (LIBS-GD) for analysis of elements in liquid was proposed, and it was applied to detect heavy metals in highly sensitive mixed solutions of Cu and Cr. During the experiments of GD and LIBS-GD, the experimental parameters have been optimized and the optimal voltage is 450 V, laser energy is 60 mJ, and the delay time is 4000 ns. Furthermore, the calibration curves of Cu and Cr under GD and LIBS-GD experiments have been established, and the limits of detection (LODs) of Cu and Cr were obtained with the method of GD and LIBS-GD, respectively. The LOD of Cu decreased from 3.37 (GD) to 0.16 mg l−1 (LIBS-GD), and Cr decreased from 3.15 to 0.34 mg l−1. The results prove that the capability of elemental detection under LIBS-GD has improved compared with the GD method. Therefore, LIBS-GD is expected to be developed into a highly sensitive method for sewage detection. 相似文献
14.
LIN Xiaomei 《等离子体科学和技术》2015,17(11):933-937
Our recent work has determined the carbon content in a melting ferroalloy by laser-induced breakdown spectroscopy (LIBS). The emission spectrum of carbon that we obtained in the laboratory is suitable for carbon content determination in a melting ferroalloy but we cannot get the expected results when this method is applied in industrial conditions: there is always an unacceptable error of around 4% between the actual value and the measured value. By comparing the measurement condition in the industrial condition with that in the laboratory, the results show that the temperature of the molten ferroalloy samples to be measured is constant under laboratory conditions while it decreases gradually under industrial conditions. However, temperature has a considerable impact on the measurement of carbon content, and this is the reason why there is always an error between the actual value and the measured value. In this paper we compare the errors of carbon content determination at different temperatures to find the optimum reference temperature range which can fit the requirements better in industrial conditions and, hence, make the measurement more accurate. The results of the comparative analyses show that the measured value of the carbon content in molten state (1620 K) is consistent with the nominal value of the solid standard sample (error within 0.7%). In fact, it is the most accurate measurement in the solid state. Based on this, we can effectively improve the accuracy of measurements in laboratory and can provide a reference standard of temperature for the measurement in industrial conditions. 相似文献
15.
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. 相似文献
16.
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. 相似文献
17.
Guodong WANG 《等离子体科学和技术》2020,22(7):74002-074002
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. 相似文献
18.
Xueqiang CAO 《等离子体科学和技术》2020,22(11):115502-115502
Laser-induced breakdown spectroscopy (LIBS) has been applied to many fields for thequantitative analysis of diverse materials. Improving the prediction accuracy of LIBS regressionmodels is still of great significance for the Mars exploration in the near future. In this study, weexplored the quantitative analysis of LIBS for the one-dimensional ChemCam (an instrumentcontaining a LIBS spectrometer and a Remote Micro-Imager) spectral data whose spectra areproduced by the ChemCam team using LIBS under the Mars-like atmospheric conditions. Weconstructed a convolutional neural network (CNN) regression model with unified parameters forall oxides, which is efficient and concise. CNN that has the excellent capability of featureextraction can effectively overcome the chemical matrix effects that impede the predictionaccuracy of regression models. Firstly, we explored the effects of four activation functions on theperformance of the CNN model. The results show that the CNN model with the hyperbolictangent (tanh) function outperforms the CNN models with the other activation functions(the rectified linear unit function, the linear function and the Sigmoid function). Secondly, wecompared the performance among the CNN models using different optimization methods.The CNN model with the stochastic gradient descent optimization and the initial learningrate=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 theCNN model is superior to the SVR model and the PLSR model for all oxides. Based on theabove analysis, we conclude the CNN regression model can effectively improve the predictionaccuracy of LIBS. 相似文献
19.
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 by 3–6 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 and 0.08 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. 相似文献
20.
Spatial confinement can significantly enhance the spectral intensity of laser-induced plasma in air.It is attributed to the compression of plasma plume by the reflected shockwave.In addition,optical emission spectroscopy of laser-induced plasma can also be affected by the distance between lens and sample surface.In order to obtain the optimized spectral intensity,the distance must be considered.In this work,spatially confined laser-induced silicon plasma by using a Nd:YAG nanosecond laser at different distances between lens and sample surface was investigated.The laser energies were 12 mJ,16 mJ,20 mJ,and 24 mJ.All experiments were carried out in an atmospheric environment.The results indicated that the intensity of Si (I) 390.55 nm line firstly rose and then dropped with the increase of lens-to-sample distance.Moreover,the spectral peak intensity with spatial confinement was higher than that without spatial confinement.The enhancement ratio was approximately 2 when laser energy was 24 mJ. 相似文献