Quantitative Laser-induced Breakdown Spectroscopy Research Articles

A strategy to reduce spectral intensity uncertainty and predicted content uncertainty of low and medium alloy steel elements

This work presents a correction method for reducing the uncertainty in the spectral intensity and quantitative prediction model by incorporating plasma acoustic pressure parameters (First peak value and First attenuation slope) and validated it in order to reduce the uncertainty in the quantitative analysis of laser-induced breakdown spectroscopy (LIBS). Mo, Al, Mn, and V elements in low and medium alloy steels were analyzed using a simultaneous plasma spectroscopy and acoustic pressure signal acquisition system. The relationship between the spectra and the acoustic pressure parameters was then examined in order to analyze the spectral intensities and the uncertainty of the quantitative prediction model after incorporating the acoustic pressure parameters. The findings indicate that, in comparison to the original spectra, the average relative standard deviation (ARSD%) of the spectral intensities of the elements Mo, Al, Mn, and V incorporated in the first peak of the sound pressure were reduced by approximately 50.32%, 44.33%, 52.74%, and 41.57%, respectively, and that of the elements incorporated in the first attenuation slopes were reduced by approximately 35.82%, 29.99%, 38.38%, and 24.66%, respectively. We discovered that the quantitative laser-induced breakdown spectroscopy model based on the plasma acoustic pressure parameter correction may also successfully lower the uncertainty of the projected elemental content values through prediction model cross-validation studies.

In situ chemical analysis of duplex stainless steel weld by laser induced breakdown spectroscopy

The high corrosion resistance and good mechanical properties of duplex stainless steel (DSS) are due to its special chemical composition, which is a balanced phase ratio of ferrite (α) and austenite (γ). Many industrial applications require the integration of DSS components. For this, Gas tungsten arc welding (GTAW) is an excellent choice, as it allows an automated operation with high reproducibility. However, when the weld pool solidifies, critical ratios of α- and γ- phases can occur, which lead to solidification cracking, increased susceptibility to corrosion, and a decrease in ductility and critical strength. Previous studies have shown that these defects can be caused by the accumulation of manganese and chromium in the heat affected zone (HAZ), requiring ongoing monitoring of this accumulation. A suitable method for such monitoring is laser-induced breakdown spectroscopy (LIBS), which can be used in two operating modes: calibration using standard reference samples and calibration-free. Unlike conventional quantitative LIBS measurements, which require reference samples to generate a calibration curve, calibration-free LIBS (CF-LIBS) allows chemical compositions to be determined solely from the emission spectrum of the plasma. Numerous publications show that CF-LIBS is a fast and efficient analytical method for the quantitative analysis of metal samples. In this work, CF-LIBS is applied to spectra obtained during GTAW DSS welding and the result is compared with those obtained by PLS analysis. A good correlation was found between both types of analysis, demonstrating the suitability of the CF-LIBS method for this application. The CF-LIBS method has a significant advantage over conventional LIBS due to the rapid in situ measurement of concentrations of major alloying elements without calibration procedure. This, combined with fast feedback and appropriate adjustment of welding parameters, helps prevent welding defects.

Quantification of alloying elements in steel targets: The LIBS 2022 regression contest

We present the results of the regression contest organized for the LIBS 2022 conference. While the motivation and design of the contest are briefly presented, the work focuses on the methodologies of the three best-performing teams. The employed spectral preprocessing strategies, choice of regression models and its optimization are detailed for each team separately. The aim of the contest reflects the long-term challenges faced by quantitative laser-induced breakdown spectroscopy (LIBS) analysis. Thus, the contest was designed with the purpose of providing a transparent platform for comparing and evaluating the large range of data processing tools available in the LIBS literature. Namely, the contest consisted of the quantification of two major (Cr, Ni) and two minor (Mn, Mo) elements in 15 steel targets. For constructing an appropriate regression model, spectra of 42 targets were provided. The spectra were collected using a commercially available laboratory-based LIBS system and made publicly available. The contest lasted 53 days during which the teams did not receive feedback. In total, 21 teams participated out of which the three best-performing methodologies are presented here. A single linear partial least squares model and two artificial neural network regression models are presented. The corresponding feature selection strategies included emission line selection, spectral range selection, and automatized wavelength selection. Various spectral normalization strategies and data augmentation strategies are also presented.

Deep learning regression for quantitative LIBS analysis

One of the most promising innovation strategies for sorting and recycling post-consumer aluminium scrap is using quantitative Laser-Induced Breakdown Spectroscopy (LIBS) analysis. However, existing methods to estimate alloying element concentrations based on LIBS spectra, such as linear univariate regression and Machine Learning models, are still too limited in their performance to achieve the accuracy demanded by the industry. Therefore, this study presents novel Deep Learning approaches and compares their performance to those of traditional univariate regression and Machine Learning methods in terms of RMSE, MAE, and R2 value. For this evaluation, two sample sets of aluminium pieces are used: one containing 27 certified aluminium reference samples and the second containing 733 post-consumer scrap pieces for which the ground truth concentrations are determined by X-Ray Fluorescence (XRF).Adopting multiple loss functions, one for each element, has proven its significant value for the regression performance. It improves the results for all performance metrics in the Scrap Sample set, and the same is true for the Reference Sample set, except for the coefficient of determination of Fe, Mn and Mg. In addition, the proposed methodology considers the learning prioritisation problem to prevent that learning the concentration of the base element is prioritised over the alloying elements. Although the effect of excluding the base alloy aluminium from the learning is small and not always positive for the performance, demonstrating this effect is also considered valuable. Since the average RMSE on the prediction is just 0.02 wt% for Al and Si, and not more than 0.01 wt% for Fe, Cu, Mn, Mg, and Zn, the best-performing Deep Learning model shows promise for the future of LIBS in metal sorting applications.

Quantitative laser-induced breakdown spectroscopy mapping of centerline segregation via optical emissions of Mn and Cr in steel wire rod products

A steel wire rod is one of the cast steel products used for springs to absorb repeated shocks, particularly in harsh conditions. Quality assessment of steel wire rods is critically important to prevent unwanted breakage during their lifetime. However, centerline segregation is the most common cause of weakened steel wire rods. Currently, centerline segregation is monitored using an optical microscope and an electron probe microanalyzer (EPMA). Herein, we demonstrate quantitative concentration mapping of Mn and Cr, the representative alloying elements, in steel wire rods using laser-induced breakdown spectroscopy (LIBS) as an alternative centerline segregation monitoring methodology. Sensitivity, detection limit, precision, and spatial resolution were found to be enough to observe centerline segregation. Concentration maps obtained by LIBS were very similar to those from EPMA. Microscopic matrix changes due to segregation were also identified. The resulting decrease in plasma temperatures was rationalized by formation of metal carbides that hardened local segregated areas. Considering the rapid multi-elemental mapping capability of LIBS in open air, LIBS is suggested as a practical alternative to optical microscope limited to carbon mapping and EPMA that needs high-vacuum equipment for screening faulty steel wire rod products. The centerline segregation of Mn and Cr in steel wire rods are rapidly monitored by laser-induced breakdown spectroscopy mapping. • A LIBS instrument for rapid elemental mapping for steel wire rods was developed • Centerline segregation of Mn and Cr was well observed by the LIBS instrument • Microscopic matrix changes due to the centerline segregation were identified and rationalized • LIBS outperforms optical microscopy in terms of simplicity in sample preparation and multiple element mapping for monitoring centerline segregation • LIBS is faster and more cost-effective than EPMA for monitoring centerline segregation

Application of elastic net in quantitative analysis of major elements using Martian laser-induced breakdown spectroscopy datasets

Multiple sets of laser-induced breakdown spectroscopy (LIBS) instruments, including ChemCam (Curiosity), SuperCam (Perseverance), and the Mars Surface Composition Detector (MarSCoDe, Zhurong), are working currently on the surface of Mars, a booming scene of LIBS technology application to planetary exploration. One of the primary challenges faced by those LIBS instruments is accurate and quantitative chemical composition determination of unexplored geological targets. Elastic net is a linear regression model that combines ridge regression and lasso model, which inherits the sparseness of lasso and the stability of ridge. In this work, we investigated the spectral features selected by elastic net model, the model performance and application to over 23,000 LIBS points. Selected features exhibit significant emission lines that are attributed to a certain single element and a few lines attributed to other related elements. These features enhance the interpretability of the model and weaken the matrix effect in LIBS quantitative analysis. In comparison, the results of elastic net are comparable and may be slightly better in some cases than other common linear regression models. For igneous calibration targets (norite, picrite, and shergottite), the predictions of elastic net are closer to the values measured in Earth laboratory than the final multivariate oxide composition values predicted by ChemCam team. Finally, we predicted the oxide abundances of 8 major elements from Mars LIBS spectra of over 23,000 points using elastic net. The predicted values of the elastic net models are highly correlated with those from the ChemCam team model. These results indicate that elastic net is viable for quantitative analysis Mars LIBS spectra. We propose that elastic net is an important candidate model for the quantitative analysis of the Zhurong MarSCoDe LIBS spectra.

Quantitative laser-induced breakdown spectroscopy based on corona equilibrium for estimating atomic composition in powdered milk

Quantitative laser-induced breakdown spectroscopy based on corona equilibrium for estimating atomic composition in powdered milk

Quantitative measurement of mixture fraction in counterflow diffusion flames by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been widely employed in various flames to determine local element compositions. Compared with other laser-based measurement techniques, LIBS is a robust and fast-to-implement method that allows for real-time monitoring and remote analyses in various industrial applications. However, quantitative LIBS measurements of elemental ratios in the gas phase are challenging as the atomic emissions also depend on laser intensities, gas densities, and gas compositions. In this work, we propose a new spectral correction method to eliminate these influences. In detail, the plasma temperature is obtained by fitting spectral lines of one single element based on the local thermodynamic equilibrium (LTE) assumption of the laser-induced plasma. The plasma temperature is then utilized to correct the intensity ratios of spectral lines from different elements. The proposed method is first applied to pure air flow to correct the ratios of the O line at 716 nm and the N line at 819 nm. The corrected O/N ratios are independent of laser intensities and gate delays in the investigated range. Shot-to-shot fluctuations are also decreased after correction. This new method is then used to perform measurements in a counterflow diffusion flame. The elemental ratios of C/N, O/N, and H/N at different positions are obtained to derive the mass fractions of C, H, O, N as well as the mixture fraction. Agreement between experimental and simulation results indicates the feasibility of this method for quantitative measurement of mixture fractions in flames.

Quantitative determination of antimony in archaeological bronze artefacts by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a reliable, fast and micro-destructive diagnostic method for qualitative and quantitative analysis of a variety of materials. In this paper, we report the results of quantitative determination of antimony in four archaeological bronze artefacts analyzed by means of LIBS. For the purpose of this study, an approach of multi-elemental quantitative LIBS analysis is proposed, based on the use of an element with known concentration as an internal standard and measurement of the spectral lines intensities of the internal standard and the element with unknown concentration. An important plasma parameter which is included in the quantitative analysis is the excitation temperature as determined by the Boltzmann plot method. The quantity of antimony in the bronze artefacts obtained by means of the proposed LIBS approach is between 1.49% and 3.13%.

Quantitative laser-induced breakdown spectroscopy for discriminating neoplastic tissues from non-neoplastic ones.

In this paper, we present a method to distinguish neoplastic tissues from non-neoplastic ones using calibration-free laser-induced breakdown spectroscopy (CF-LIBS). For this propose, plasma emission was collected from neoplastic and non-neoplastic tissues taken from the ovarian cancer mice models. Results were obtained by utilizing the characteristic plasma emission lines of different elements that have been confirmed in the investigated samples. From the temporal evolution of plasma emission, the optimum temporal-observation-windows are identified for LIBS investigation. The concentrations of the detected elements in tissues were measured by a calibration-free approach based on data process of plasma parameters at the local thermodynamic equilibrium. The neoplastic specimens provided more energetic plasma than non-neoplastic ones that resulting in higher peaks intensities, electron density and electron temperature especially in the early windows (between 0.1 µs to 0.8 µs). Results demonstrated higher concentrations of major and trace elements such as Mg, Fe, Ca, Na, and K in the neoplastic tissues. Finally, the results using CF-LIBS method were found to be in good agreement with that of Inductive coupled plasma-optical emission spectroscopy (ICP-OES).

Elemental Mapping of Entire Thick Film Electrodes By Laser-Induced Breakdown Spectroscopy

Next generation lithium-ion batteries require new research and development in the field of high energy / high power materials and concepts regarding cell design and electrode architectures. Particularly for automotive applications, the identification and control of degradation processes are main issues for maintaining long cell life-time and high safety standards. One promising approach for the visualization of elemental composition in lithium-based battery systems is the appliance of laser-induced breakdown spectroscopy (LIBS), which enables a rapid screening of materials, e.g., entire electrodes. So far, this diagnostic approach is being used for semi-quantitative investigation of battery components. However, at KIT, a full-quantitative analysis was developed for characterizing the lithium distribution and concentration as function of state-of-charge. From technical point of view, recording of three-dimensional (3D) elemental profiles, verification of material stoichiometry or even a quality controlled analysis during continuous roll-to-roll production can be easily performed under standard ambient conditions. In this work, we will focus on establishing LIBS for quantitative chemical mapping of entire electrodes after electrochemical cycling. Electrodes with variation in local porosity and tortuosity have been prepared by mechanical loading, while fs-laser radiation was applied for the development of 3D electrode architectures. First of all, on anode side, an increased tortuosity was realized by separator defects, consciously placed in coin cells during cell assembly. Simply saying, the separator sheet was blocked on selected regions in order to investigate post-mortem the possible emergence of local lithium plating. Secondly, compression molding was locally performed for studying lithium distribution in cathodes showing a sudden change in porosity. Two types of geometrical arrangements were applied for the formation of low and high porosity regions. Briefly, non-uniformities in porosity and tortuosity affect the electrochemical performance, especially at charging and discharging currents equal or above 1C. In an early stage of electrochemical cycling, a significant drop in specific capacity could be observed for both types of electrodes. Yielded from quantitative LIBS data, an enhanced local lithium concentration could be detected at the electrode surface, located in the neighborhood of separator defects (for anodes) and at the interface between low and high porosity regions (for cathodes). In contrast, fs-laser generated micro-pillars provide new pathways for Li+-ions, leading to a tremendous boost in diffusion kinetics. Thus, an enhanced specific capacity was observed for cells with laser-structured electrodes during high power conditions. Chemically proven by LIBS, a slight enrichment of intercalated lithium could be detected along the contour of each laser-generated micro-pillar, which indicates that laser-structuring can fulfill the requirements for next generation batteries, providing both, a contemporaneous high energy- and power density on battery-level. In summary, a full screening of entire electrodes achieved from post-mortem LIBS studies will be presented, based on influencing factors such as porosity, tortuosity, and laser-assisted surface modification.

Accuracy improvement of quantitative LIBS analysis of coal properties using a hybrid model based on a wavelet threshold de-noising and feature selection method.

A hybrid model based on a wavelet threshold de-noising (WTD) and recursive feature elimination with cross-validation (RFECV) method was proposed to improve the measurements in quantitative analysis of coal properties using laser-induced breakdown spectroscopy (LIBS). First, a modified threshold of WTD was proposed based on wavelet coefficient theory. Interference of noise in the LIBS spectrum was reduced by using this modified method. Then, the RFECV method was applied to extract effective features from the de-noised LIBS spectrum. Finally, support vector regression (SVR) models of coal properties were established by the selected features. A validation set was used to verify the effectiveness and robustness of the hybrid model. The improvement of the hybrid model on the quantitative analysis of each index of coal properties (heat value, ash, volatile content) was studied and discussed. By using the proposed model, the determination coefficient (R2), root mean square error of prediction, average relative error, and relative standard deviation were all significantly improved over the original spectra model. The results demonstrated that the proposed model could effectively improve the accuracy and precision of LIBS quantitative analysis for coal properties.

A new approach to non-linear multivariate calibration in laser-induced breakdown spectroscopy analysis of silicate rocks

In this paper a new approach to quantitative Laser-Induced Breakdown Spectroscopy (LIBS) analysis of silicate rocks is presented. The method is adapted from the Franzini and Leoni algorithm, a method widely used in X-Ray Fluorescence analysis for correcting the matrix effects in the determination of the composition of geological materials. To illustrate the features of the new method proposed, nine elements were quantified in 19 geological standards by building linear univariate calibration curves, linear multivariate calibration surfaces (PLS) and using Artificial Neural Networks. The results were then compared with the predictions derived from the application of the algorithm here proposed. It was found that the Franzini and Leoni approach gives results much more precise than linear uni- and multivariate approaches, and comparable with the ones derived from the application of Artificial Neural Networks. A definite advantage of the proposed approach is the possibility of building multivariate non-linear calibration surfaces using linear optimization algorithms, a feature which makes the application of the Franzini and Leoni method in LIBS analysis much simpler (and controllable) with respect to the algorithms based on Artificial Neural Networks.

Comparative study on self-absorption of laser-induced tungsten plasma in air and in argon.

The onset of self-absorption of laser-induced plasma poses a problem for converting emission line intensities to concentrations, which is one of the main bottlenecks in quantitative laser-induced breakdown spectroscopy (LIBS) measurements. In this paper, the effects of atmosphere and laser fluence on self-absorption reduction of the plasma induced on tungsten-copper alloy target were investigated with nanosecond infrared (1064 nm) laser pulse over a range of 2.9 to 18.2 J/cm2. The time-resolved features of self-absorption, and temperature and electron density of the plasma were characterized in atmospheric air and argon, respectively. The experimental results show the effect of self-absorption can be significantly reduced by increasing the laser pulse energy. The argon atmosphere is more helpful for self-absorption reduction. The time-resolved diagnostics of emission spectra in the early stage of the plasma formation are very effective to prevent self-absorption to improve the LIBS analytical performance.

A first simulation of soil-laser interaction investigation for soil characteristic analysis

Laser Induced Breakdown Spectroscopy (LIBS) is an important technique utilized in several areas including that of agriculture and space exploration. However, whilst LIBS provides a new way of analyzing chemical composition of targeted soils or rocks, the quality and repeatability of the results are affected by the terrain and soil conditions as a result of physical matrix effects which occur due to varying properties like specific heat and thermal conductivity. These physical and chemical matrix effects cause difficulties with quantitative LIBS analysis. Together with this, the diverse areas in which LIBS is utilized means that it can require varying conditions of ablation techniques. Therefore, it is prudent to investigate theoretically the effect of different soil characteristics on the ablation process. The work presented here is the first simulation based research on soil quality analysis using LIBS. Aiming to gain insights into the soil breakdown process, laser coupling, sample temperature and its sensing performance through simulation of the laser ablation of soil using finite element modelling software. The proposed model within COMSOL Multiphysics was designed and developed to study the influence of multiple nanosecond (ns) laser pulses on the surface of samples of soil with varying properties. The simulation results reveal the simulated soil sensing behaviour for the first time. The computational results were compared to those obtained from LIBS experiments conducted for the Argibot project at the University of Strathclyde.

Measurement and kinetics of elemental and atomic potassium release from a burning biomass pellet

Combining polarizing-filtered planar laser-induced fluorescence (PLIF) with simultaneous laser absorption, quantitative laser-induced breakdown spectroscopy (LIBS) and two-color pyrometry, the potassium release during the combustion of biomass fuels (corn straw and poplar) has been investigated. The temporal release profiles of volatile atomic potassium and potassium compounds from a corn straw show a single peak. The woody biomass, poplar, produces a dual-maxima distribution for potassium and potassium compounds. For both biomass samples, the highest concentrations of released atomic potassium and potassium compounds occur in the devolatilization stage. The mass ratios between volatile atomic potassium and potassium compounds in the corn straw and poplar cases are 0.77% and 0.79%, respectively. These values agree well with chemical equilibrium predictions that 0.68% of total potassium will be in atomic form. A two-step kinetic model of potassium release has been developed, which gives better predictions during the devolatilization stage than the existing single-step model. Finally, a map of potassium transformation processes during combustion is developed. Starting with inorganic and organic potassium, there are eight proposed transformation pathways including five proposed release pathways that occur during the combustion. The pathways describe the transformation of potassium between the fuel volatile matter, char, and ash. Potassium release during the devolatilization stage is due to pyrolysis and evaporation; during the char burnout stage, potassium release is due to char oxidation and decomposition; and during the ash cooking stage, potassium release is caused by reactions between the ash and H2O in the co-flow.

Laser-induced plasma characterization using self-absorption quantification method

Laser-induced breakdown spectroscopy (LIBS) is a well-known analytical technique based on the atomic emission spectroscopy.The elemental composition and relative abundance information can be obtained by analyzing the plasma radiation generated by focusing high-energy pulsed laser on the sample.It has a wide range of applications due to its many advantages,such as minimal-to-no sample preparation,broad applicability,and in-situ capability.But in LIBS,the self-absorption effect of the emitted line can reduce the spectral line intensity,and then affect the precision and accuracy of LIBS quantitative analysis.So there are many methods and researches to reduce or eliminate the adverse effects of selfabsorption on spectral lines.In this paper,a self-absorption quantification analysis method is proposed to characterize the laser-induced plasma quantitative parameters.This self-absorption quantification analysis method,which utilizes the intensity independent information in the self-absorbed spectral lines,is proposed to characterize the induced plasma and perform quantitative measurements.The plasma characteristics including electron temperature,elemental concentration ratio,and absolute species number density can be derived directly through quantifying the self-absorption degree of the analytical spectral lines.Compared with the traditional laser-induced breakdown spectroscopy,the new method is weakly related to the spectral intensity:neither the analysis results are affected by the self-absorption effects,nor the additional spectral efficiency calibration is required.The LIBS spectrum of an aluminum-lithium alloy (nominal weight compositions are Al 94.6%,Mg 1.8%,Li 0.8%,Cu 2.59%,and Mn 0.21%) is used to calculate the spatiallyaveraged electron temperature and the concentration ratio between Mg and Al,and the species number densities is obtained by using the proposed self-absorption quantification method.The results of experiment on aluminum-lithium alloy show that the mean electron temperatures obtained by the modified Saha-Boltzmann plots determined by Mg and Al are 0.96 eV and 0.97 eV,respectively.The weight ratio wMg/wAl in the plasma is calculated to be 0.0171,which is approximately coincident with the nominal value of 0.0169.The absolute singly ionized number density of matrix element Al is 1:65×1017 cm-3,which is comparable to the electron density calculated from the Hα line broadening (1:72×1017 cm-3).Evidently,the free electrons present in the plasma are mainly contributed by the singly ionized matrix element Al.These experimental results of aluminum-lithium alloy validate that the proposed method is qualified to realize accurate absolute quantitative measurements and fast diagnose the plasma characteristics,which verifies the practicability,advantages,and precision of this method.This self-absorption quantification method is of great significance for quantitative LIBS analysis,especially the CF-LIBS analysis.

Application of a compact diode pumped solid-state laser source for quantitative laser-induced breakdown spectroscopy analysis of steel

Laser-induced breakdown spectroscopy (LIBS) technology holds the potential for onsite real-time measurements of steel products. However, for a mobile and robust LIBS measurement system, an adequate small and ruggedized laser source is a key requirement. In this contribution, we present tests with our compact high-power laser source, which, initially, was developed for ignition applications. The CTR HiPoLas® laser is a robust diode pumped solid-state laser with a passive Q-switch with dimensions of less than 10 cm3. The laser generates 2.5-ns pulses with 30 mJ at a maximum continuous repetition rate of about 30 Hz. Feasibility of LIBS experiments with the laser source was experimentally verified with steel samples. The results show that the laser with its current optical output parameters is very well-suited for LIBS measurements. We believe that the miniaturized laser presented here will enable very compact and robust portable high-performance LIBS systems.

Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix

The dependence of laser-induced breakdown spectroscopy (LIBS) to the matrix of the sample remains an important consideration in performing quantitative analysis. In this study, a new matrix was introduced in the preparation of solid powder calibration curves. Heat-treated natural zeolite and KBr were mixed separately into high purity metal powders to generate calibration curves using a univariate approach. A LIBS technique was used in the detection and quantitative analysis of Cr, Cu and Pb in river sediment samples. The relative percent difference (RPD) was calculated to describe the variability measurements made using ICP/OES and LIBS as well as to evaluate the accuracy of the method. Calculated limits of detection in the matrices prepared were comparable with literature values and ranged from 0.41 to 6.1ppm. The resulting metal concentrations indicate that the natural zeolite matrix were closer to the reference values compared to the KBr matrix. By employing principal component analysis (PCA), heat treated zeolite was shown as a potential diluent or binder for generating calibration curves and could provide matrix-matched standards in identification of target metals from unknown sediment samples. The natural zeolite appeared to closely simulate the ablation behavior and property of the samples, and it is found to be a potential suitable matrix for the quantitative LIBS analysis of sediments.

Variables selection: A critical issue for quantitative laser-induced breakdown spectroscopy

In this paper, we demonstrate the importance of variable selection on the prediction ability of LIBS quantitative partial least squares (PLS) models. The spectral lines of potassium at 766.49nm and 769.90nm were considered in the framework of an agricultural soils analysis. Univariate models demonstrating very poor correlation between the peak areas of the potassium lines and the related concentration values, a series of PLS models allowed to significantly improve the prediction ability compared to the univariate approach. This improvement was due to advanced variable selection, achieved through the use of two output data provided after PLS calculation, namely the Variable Importance in Projection (VIP) and the Coefficients graph. In this demonstration, the gain was significant because the two spectral lines of potassium at 766.49nm and 769.90nm exhibited unusual profiles. Indeed, including in a PLS model only the variables related to the edges of these lines allowed a significant improvement of its predictive ability (Q2=0.84, RMSE=1.49g/kg) compared to another PLS model only including the variables related to the central parts of these lines (Q2=0.78, RMSE=1.59g/kg).