From big to strong: growth of the Asian laser-induced breakdown spectroscopy community
Accuracy improvement of calibration-free laser-induced breakdown spectroscopy
Calibration-free laser-induced breakdown spectroscopy can overcome the matrix effect and the huge application prospects of in situ and on-line measurement, so it has been studied and applied to many analytical samples by numerous researchers since it was first proposed in 1999. However, its accuracy is always lower than other analytical techniques and traditional quantitative analysis methods of laser-induced breakdown spectroscopy. The goal of this paper is to review the improvement of accuracy in the experimental setup and spectral analysis, especially after 2010, but not limited to it. The main contents include the accurate measurement of spectral intensity, the spatial and temporal window of local thermodynamic equilibrium and the accurate calculation of temperature and electron density. Due to the requirement of one or more standard samples, the combination of standard samples and CF-LIBS is discussed as a separate section. Finally, a simple conclusion is offered to relevant researchers who want to use CF-LIBS for quantitative analysis.
Detection of heavy metals in water samples by laser-induced breakdown spectroscopy combined with annular groove graphite flakes Hot!
The use of laser-induced breakdown spectroscopy (LIBS) for the analysis of heavy metals in water samples is investigated. Some factors such as splashing, surface ripples, extinction of emitted intensity, and a shorter plasma lifetime will influence the results if the water sample is directly measured. In order to avoid these disadvantages and the ‘coffee-ring effect’, hydrophilic graphite flakes with annular grooves were used for the first time to enrich and concentrate heavy metals in water samples before being analyzed by LIBS. The proposed method and procedure have been evaluated to concentrate and analyze cadmium, chromium, copper, nickel, lead, and zinc in a water sample. The correlation coefficients were all above 0.99. The detection limits of 0.029, 0.087, 0.012, 0.083, 0.125, and 0.049 mgl -1 for Cd, Cr, Cu, Ni, Pb, and Zn, respectively, were obtained in samples prepared in a laboratory. With this structure, the heavy metals homogeneously distribute in the annular groove and the relative standard deviations are all below 6%. This method is very convenient and suitable for online in situ analysis of heavy metals.
Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy
Laser-induced breakdown spectroscopy (LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve (CC) and support vector regression (SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error (ARE), relative standard deviation (RSD) and root mean squared error of prediction (RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%, RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.
A method of laser focusing control in micro-laser-induced breakdown spectroscopy
This paper presents a method for the automatic adjustment of the laser defocusing amount in micro-laser-induced breakdown spectroscopy. A microscopic optical imaging system consisting of a CCD camera and a 20× objective lens was adopted to realize the method. The real-time auto-focusing of the system was achieved by detecting the effective pixels of the light spot generated by the laser pointer. The focusing accuracy of the method could achieve 3 μm. The element concentrations of Mn and Ni in low-alloy steels were analyzed at a crater diameter of about 35 μm using the presented method. After using the presented method, the determination coefficients of Mn and Ni both exceeded 0.997, with the root-mean-square errors being 0.0133 and 0.0395, respectively. Scanning analysis was performed on the inclined plane and the curved surface by means of focusing control and non-focusing control. Ten characteristic spectral lines of Fe were selected as the analysis lines. With the focusing control, the average relative standard deviations obtained on the inclined plane and curved surface were both less than 5%, and much less than the values without focusing control, 14.6% and 40.39%.
Elemental analysis of copper alloy by high repetition rate LA-SIBS using compact fiber spectrometer
High repetition rate laser-ablation spark-induced breakdown spectroscopy (HRR LA-SIBS) was first used to analyze trace elements in copper alloy samples. The 1064 nm output of an acousto- optically Q-switched Nd:YAG laser operated at a pulse repetition rate of 1 kHz was utilized to ablate copper alloy and to form original plasma, spark-discharge was applied to further breakdown the ablated samples and enhance the emission of the laser-induced plasma. A compact multichannel fiber spectrometer was used to analyze the plasma emission under non- gated operation mode. Under the assistance of high repetition rate spark discharge, the plasma emission was able to be improved significantly. The determined limits of the detection of lead and aluminum were 15.5 ppm and 1.9 ppm by HRR LA-SIBS, respectively, which were 11 and 6 folds better than that determined by HRR LIBS under the same laser-ablation condition. This work demonstrates the feasibility of using fiber spectrometer to analyze plasma emission under non-gated operation mode and the possibility of building a portable HRR LA-SIBS system for rapid elemental analysis of copper alloys and other solid samples.
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 R2 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.
Remote open-path laser-induced breakdown spectroscopy for the analysis of manganese in steel samples at high temperature
A remote open-path laser-induced breakdown spectroscopy (LIBS) system was designed and studied in the present work for the purpose of combining the LIBS technique with the steel production line. In this system, the relatively simple configuration and optics were employed to measure the steel samples at a remote distance and a hot sample temperature. The system has obtained a robustness for the deviation of the sample position because of the open-path and all- optical structure. The measurement was carried out at different sample temperatures by placing the samples in a muffle furnace with a window in the front door. The results show that the intensity of the spectral lines increased as the sample temperature increased. The influence of the sample temperature on the quantitative analysis of manganese in the steel samples was investigated by measuring ten standard steel samples at different temperatures. Three samples were selected as the test sample for the simulation measurement. The results show that, at the sample temperature of 500°C, the average relative error of prediction is 3.1% and the average relative standard deviation is 7.7%, respectively.
Hybrid classification of coal and biomass by laser-induced breakdown spectroscopy combined with K-means and SVM
Laser-induced breakdown spectroscopy (LIBS) is a new technology suitable for classification of various materials. This paper proposes a hybrid classification scheme for coal, municipal sludge and biomass by using LIBS combined with K-means and support vector machine (SVM) algorithm. In the study, 10 samples were classified in 3 groups without supervision by K-means clustering, then a further supervised classification of 6 kinds of biomass samples by SVM was carried out. The results show that the comprehensive accuracy of the hybrid classification model is over 98%. In comparison with the single SVM classification model, the hybrid classification model can save 58.92% of operation time while guaranteeing the accuracy. The results demonstrate that the hybrid classification model is able to make an efficient, fast and accurate classification of coal, municipal sludge and biomass, furthermore, it is precise for the detection of various kinds of biomass fuel.
Effect of lens focusing distance on laser-induced silicon plasmas at different sample temperatures
We investigated the dependence of laser-induced breakdown spectral intensity on the focusing position of a lens at different sample temperatures (room temperature to 300°C) in atmosphere. A Q-switched Nd:YAG nanosecond pulsed laser with 1064 nm wavelength and 10 ns pulse width was used to ablate silicon to produce plasma. It was confirmed that the increase in the sample’s initial temperature could improve spectral line intensity. In addition, when the distance from the target surface to the focal point increased, the intensity firstly rose, and then dropped. The trend of change with distance was more obvious at higher sample temperatures. By observing the distribution of the normalized ratio of Si atomic spectral line intensity and Si ionic spectral line intensity as functions of distance and temperature, the maximum value of normalized ratio appeared at the longer distance as the initial temperature was higher, while the maximum ratio appeared at the shorter distance as the sample temperature was lower.
Consistency of intensity ratio between spectral lines with similar self-absorption characteristics during ungated laser induced breakdown spectroscopy measurements
This work reports that the intensity ratio of spectral lines having similar self-absorption characteristics during laser induced breakdown spectroscopy (LIBS) analysis can become nearly constant over a wide range of irradiation conditions if the intensities are integrated over a sufficiently long time. It is shown that the plasma temperature and intensity ratio of these spectral lines have temporal similarity. The spectral lines with similar self-absorption properties may be selected to improve the accuracy and consistency of LIBS analysis results under an environment with fluctuating measurement conditions.
Signal processing for real-time identification of similar metals by laser-induced breakdown spectroscopy Hot!
Laser-induced breakdown spectroscopy (LIBS) is regarded as a promising technique for real- time sorting of scrap metals due to its capability of fast multi-elemental and in-air analysis. This work reports a method for signal processing which ensures high accuracy and high speed during similar metal sorting by LIBS. Similar metals such as aluminum alloys or stainless steel are characterized by nearly the same constituent elements with slight variations in elemental concentration depending on metal type. In the proposed method, the original data matrix is substantially reduced for fast processing by selecting new input variables (spectral lines) using the information for the constituent elements of similar metals. Specifically, principal component analysis (PCA) of full-spectra LIBS data was performed and then, based on the loading plots, the input variables of greater significance were selected in the order of higher weights for each constituent element. The results for the classification test with aluminum alloy, copper alloy, stainless steel and cast steel showed that the classification accuracy of the proposed method was nearly the same as that of full-spectra PCA, but the computation time was reduced by a factor of 20 or more. The results demonstrated that incorporating the information for constituent elements can significantly accelerate classification speed without loss of accuracy.
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.
Enhancement of optical emission generated from femtosecond double-pulse laser-induced glass plasma at different sample temperatures in air
In double-pulse laser-induced breakdown spectroscopy (DP-LIBS), the collinear femtosecond double-pulse laser configuration is experimentally investigated with different initial sample temperatures using a Ti:sapphire laser. The glass sample is ablated to produce the plasma spectroscopy. During the experiment, the detected spectral lines include two Na (I) lines (589.0 nm and 589.6 nm) and one Ca (I) line at the wavelength of 585.7 nm. The emission lines are measured at room temperature (22 °C) and three higher initial sample temperatures (Ts =100 °C, 200°C, and 250 °C). The inter-pulse delay time ranges from -250 ps to 250 ps. The inter-pulse delay time and the sample temperature strongly influence the spectral intensity, and the spectral intensity can be significantly enhanced by increasing the sample temperature and selecting the optimized inter-pulse time. For the same inter-pulse time of 0ps (single-pulse LIBS), the enhancement ratio is approximately 2.5 at Ts=200 °C compared with that obtained at Ts=22 °C. For the same inter-pulse time of 150 ps, the enhancement ratio can be up to 4 at Ts=200 °C compared with that obtained at Ts=22 °C. The combined enhancement effects of the different initial sample temperatures and the double-pulse configuration in femtosecond LIBS are much stronger than that of the different initial sample temperatures or the double-pulse configuration only.
Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks
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.
Semi-supervised LIBS quantitative analysis method based on co-training regression model with selection of effective unlabeled samples
The accuracy of laser-induced breakdown spectroscopy (LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semi- supervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.
Mechanisms and efficient elimination approaches of self-absorption in LIBS Hot!
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.
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 rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm
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.
Effect of parameter setting and spectral normalization approach on study of matrix effect by laser induced breakdown spectroscopy of Ag–Zn binary composites
The complex nature of laser-material interaction causes non-stoichiometric ablation of alloy samples. This is attributed to matrix effect, which reduces analyzing capability. To address this issue, the analytical performance of three different normalization methods, namely normalization with background, internal normalization and three point smoothing techniques at different parameter settings is studied for quantification of Ag and Zn by Laser induced breakdown spectroscopy (LIBS). The LIBS spectra of five known concentration of silver zinc binary composites have been investigated at various laser irradiances (LIs). Calibration curves for both Ag(I) line (4d105s2S1/2 →4d10 5p2P1/2 at 338.28 nm) and Zn(I) line (4s5s3S1 →4s4p3P2 at 481.053 nm) have been determined at LI of 5.86×1010 W cm -2 . Slopes of these calibration curves provide the valuation of matrix effect in the Ag–Zn composites. With careful sample preparation and normalization after smoothing at optimum parameter setting (OPS), the minimization of sample matrix effect has been successfully achieved. A good linearity has been obtained in Ag and Zn calibration curve at OPS when normalized the whole area of spectrum after smoothing and the obtained coefficients of determination values were R 2 =0.995 and 0.998 closer to 1. The results of matrix effect have been further verified by analysis of plasma parameters. Both plasma parameters showed no change with varying concentration at OPS. However, at high concentration of Ag, the observed significant changes in both plasma parameters at common parameter setting PS-1 and PS-2 were the gesture of matrix effect. In our case, the better analytical results were obtained at smoothing function with optimized parameter setting that indicates it is more efficient than normalization with background and internal normalization method.
Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM
According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy (LIBS) has shown a great potential for rapid analysis in steel industry. Nevertheless, the accuracy and precision may be limited by complex matrix effect and self- absorption 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 (R2), Root-mean- square Errors of Calibration (RMSEC), Root-mean-square Errors of Prediction (RMSEP) of GA- KELM 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.
Investigation of the factors affecting the limit of detection of laser-induced breakdown spectroscopy for surface inspection
Laser-induced breakdown spectroscopy (LIBS) was examined to detect a trace substance adhered onto Al alloys for the surface inspection of materials to be adhesively bonded. As an example of Si contamination, silicone oil was employed and sprayed onto substrates with a controlled surface concentration. LIBS measurements employing nanosecond UV pulses (λ=266 nm) and an off-axis emission collection system with different detecting heights were performed. Because surface contaminants are involved in the plasma formed by laser ablation of the substrates, the relative contribution of the surface contaminants and the substrates to the plasma emission could be changed depending on the conditions for plasma formation. The limit of detection (LOD) was evaluated under several detecting conditions for investigating the factors that affected the LOD. A significant factor was the standard deviation values of signal intensities obtained for the clean substrates. This value varied depending on the measurement conditions. For the Al alloy (A6061), the smallest LOD obtained was 0.529 μg·cm 2. Furthermore, an improved LOD (0.299 μg·cm 2) was obtained for the Al alloy with a lower Si content.
Study of pressure effects on ocean in-situ detection using laser-induced breakdown spectroscopy
Laser-induced breakdown spectroscopy (LIBS) has attracted extensive attention as a new technique for in-situ marine application. In this work, the influence of deep-sea high pressure environment on LIBS signals was investigated by using a compact LIBS-sea system developed by Ocean University of China for the in-situ chemical analysis of seawater. The results from the field measurements show that the liquid pressure has a significant effect on the LIBS signals. Higher peak intensity and larger line broadening were obtained as the pressure increases. By comparing the variations of the temperature and salinity with the LIBS signals, a weak correlation between them can be observed. Under high pressure conditions, the optimal laser energy was higher than that in air environment. When the laser energy exceeded 17 mJ, the effect of laser energy on the signal intensity weakened. The signal intensity decreases gradually at larger delays. The obtained results verified the feasibility of the LIBS technique for the deep-sea in-situ detection, and we hope this technology can contribute to surveying more deep-sea environments such as the hydrothermal vent regions.