Laser-induced Breakdown Spectroscopy Data Research Articles

Data Fusion of Laser-Induced Breakdown Spectroscopy (LIBS) and RAMAN Spectroscopy: Improving the Accuracy of Ginseng Classification

Ginseng, a traditional Chinese herbal medicine, has experienced increasing application. Due to variations in the medicinal value of ginseng based upon its cultivation location, an accurate method is required to identify its origin. Here laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy were employed with machine learning algorithms for the authentication of ginseng. Accurate origin identification was conducted on dried white ginseng. Initially, LIBS and Raman spectra were acquired separately. Regional differences were not observable in the LIBS spectra. Conversely, in the Raman spectra, similarity was observed between the Baishan and Dunhua regions, contributing to reduced accuracy. Subsequently, the LIBS and Raman spectra were fused. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS, Raman, and fused data. The top 20 principal components for these data sets explained variances of 97.17%, 86.51%, and 94.22%, respectively. The reduced-dimension LIBS, Raman, and LIBS-Raman data were utilized as input variables for random forest, extreme gradient boosting, and categorical boosting (CatBoost) classification. The results indicate that the LIBS-Raman data yield the highest accuracy with CatBoost reaching 99%. Compared to the results using LIBS and Raman, there were improvements in accuracy of 11% and 6%. This method holds significance for the rapid and accurate identification of ginseng.

Classification of e-waste using machine learning-assisted laser-induced breakdown spectroscopy.

Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapid and contactless classification of metals in electronic waste (e-waste) management. In the present research work, five types of aluminium alloys, because of their extensive use in structural, electrical and thermotechnical functions in the electronics industry, were taken. Laser-induced breakdown spectroscopy (LIBS), a spectral identifier technique, was employed in conjunction with machine learning (ML) classification models of AI. Principal component analysis (PCA), an unsupervised ML classifier, was found incapable to differentiate LIBS data of alloys. Supervised ML classifier was then trained (for 10-fold cross-validation) on randomly selected 80% and tested on 20% spectral data of each alloy to assess classification capacity of each. In most of the tested variants of K nearest neighbour (kNN) the resulting accuracy was lower than 30% but kNN ensembled with random subspace method showed improved accuracy up to 98%. This study revealed that an AI-based LIBS system can classify e-waste alloys rather effectively in a non-contactless mode and could potentially be connected with robotic systems, hence, minimizing manual labour.

Quality index for Martian in-situ laser-induced breakdown spectroscopy data

Laser-induced breakdown spectroscopy (LIBS) has been widely implemented in Mars explorations as a versatile technique for in-situ chemical analysis. Determination of elemental composition from Martian LIBS typically relies on uni/multi-variate models trained on specific laboratory datasets, which in turn requires similarities between this training dataset and Martian data. However, plasma excitation can affect this similarity. The inability to identify insufficiently excited data may ignore poor focusing for some observations or lead to unawareness of compositional uncertainty. This study establishes the LIBS Quality Index (LQI) for Martian LIBS data quality by evaluating the carbon ionic doublet emission around 658 nm for the Martian atmosphere, assessing its signal-to-noise ratio as well as its deviation from the line profile standard of the instrument-specific laboratory dataset. This method allows the LQI to be used as a confidence level for assessing LIBS excitation quality and measurement for similarity with instrument-specific laboratory datasets. The LQI evaluation routines were established for ChemCam, MarSCoDe, and SuperCam. Validation of LQI was carried out on the laboratory datasets and Mars data with known quality assessments of the three instruments. The index was verified to be sensitive to input irradiance, applicable with three current Mars LIBS instruments, robust against various material types (except carbonate), and moderate interference from H, Fe, and Ti. Applying the LQI, the study was able to identify the remote micro-imager (RMI) autofocus as an optimal mechanism, to discover the influence of environmental conditions on data quality, to discuss the impact of target material consolidation and morphology on data quality, and to investigate the contribution of data quality to quantification uncertainty. These application examples demonstrated the feasibility of LQI as a useful and sensitive tool to assess data quality and its potential to aid current and future Martian LIBS explorations.

Comparison of laser-induced breakdown spectroscopy (LIBS) and ICP analysis results for measuring Pb and Zn in soil

Context Laser-induced breakdown spectroscopy (LIBS) is a rapid, multielement analytical technique. It is particularly suitable for the qualitative and quantitative analyses of heavy metals in solid samples. Aims To validate the technique, the LIBS data were compared with the data obtained via conventional inductively coupled plasma (ICP) spectroscopy for the same soil samples. Methods In this study, standard and unknown soil samples from contaminated areas were prepared and fixed to an adhesive tape for LIBS analysis. The soils were also digested with acids for ICP analysis. The emission intensity of one selected line for each of the two analytes, i.e. lead (Pb) and zinc (Zn), was normalised to the background signal and plotted as a function of the concentration values previously determined via ICP analysis. Key results The data demonstrated good linearity for the calibration lines drawn, and the correlation between the ICP and LIBS data was confirmed by the satisfactory agreement between the corresponding values. Conclusions The concentration coefficient of determination (R2) between LIBS and ICP-aqua regia digestion analysis or ICP-total digestion analysis were >0.86 and >0.89 for Pb and Zn, respectively. The total analysis time for the LIBS method was 310 min, which was 54.40% shorter than that for the ICP method (680 min). Implications Consequently, LIBS can be used to measure Pb and Zn in soils without any chemical preparation.

Classification of diverse plastic samples by LIBS and Raman data fusion

The plastic production and usage in the world is steadily increasing. This leads to increased amounts of plastic waste. Most of the waste could be potentially recycled, but only 14 % of plastic waste is recycled. In order to increase the share of recycling in plastic waste management, the recycling process should be completely automated. The problematic part of sorting is being solved by either manual (labor-intensive) or spectroscopy-based (still in development) methods. In this work, we propose the data fusion of Laser-Induced Breakdown Spectroscopy (LIBS) and Raman spectroscopy as a fast, robust, and reliable way to sort/classify any potential polymer material. The sample set of this work consists of several types of polymers in clear, colored, and even mixture versions. So far, no LIBS/Raman classification works involved all these categories in one experiment. Additionally, the low and medium level of data fusion is discussed, and the performance is compared. By using LIBS and Raman data fusion method and both linear and nonlinear chemometric techniques, increased accuracy reaching more than 98 % in the classification of investigated plastic samples was achieved, which was a significant improvement when compared with singular methods classification accuracy.

Effects of feature engineering on the robustness of laser-induced breakdown spectroscopy for industrial steel classification

Steel is a widely used material. Since steel has different properties and uses depending on the steel type, it is essential to accurately classify the steel types. LIBS is a promising technique for real-time classification due to its advantages such as rapid analysis and feasibility of measurement in air. However, enhancing the robustness of classification models is still challenging in industrial applications. This study investigates the effects of feature engineering on the robustness of laser-induced breakdown spectroscopy (LIBS) for industrial steel classification. To make LIBS applicable to the steel industry, a remote LIBS system was developed. The LIBS system was utilized to classify the six representative types of steel. To evaluate the performance of the LIBS system, the measurements were reproduced, with the same sample, at seven different laser energies. The robustness of the LIBS system was investigated by conducting experiments using various feature-engineering and learning-based algorithms on the LIBS data, with test datasets having laser energies different from those of the training datasets. The results indicate that the LIBS signal intensity ratio as the input data leads to a more robust classification model than using principal components or random forest-based datasets. In addition, the intensity ratios with similar upper-state energies were found to be more suitable input data for steel classification. These findings demonstrate the potential of the LIBS system for the highly accurate and robust classification of industrial-grade steel.

Assessment of toxicity of residues of normal/green cracker and their impact on soil

In the present work, laser-induced breakdown spectroscopy (LIBS) is used to examine the hazardous constituents present in the residues of six types of normal and six types of green firecracker samples. The residue of the normal firecracker’s samples contains the spectral lines of toxic chemicals such as Al, Ba, Sr, Mg, and Ti in a similar way as the fresh powder of normal crackers. The residues of the green firecracker’s samples contain toxic elements such as Al and Ba, and the intensities of these toxic elements are so high that these samples also contain the electronic bands of AlO and SrO. The UV-vis spectra of residues of normal and green firecracker samples contain the molecules of KNO3, CaO, Al2O3, and SrO in a similar way as the fresh powder of these firecrackers does. This reflects that the toxicity of the powder of firecracker samples remains similar after the burning of these firecrackers’ samples. Therefore, these toxic residues are mixed in the soil, where they burn and contaminate it. For the assessment of the contamination of the soil, the concentration of micronutrients such as Fe, Cu, Mn, Zn, and P is calculated using atomic absorption spectroscopy (AAS) techniques and found to increase in all the contaminated soil compared to blank soil. This reflects that the soil is contaminated. For the classification of the residues and soil contaminated with residues, the principal component analysis (PCA) and hierarchical clustering analysis (HCA) are applied to the LIBS data set.

Identification of wood specimens utilizing fs-LIBS and machine learning techniques

We report on the ability to identify wood specimens by utilizing 30 fs Laser Induced Breakdown Spectroscopy (LIBS) in conjunction with machine learning techniques. Ten different wood specimens have been studied. The spectral features were assigned to atomic/ionic and diatomic molecular transitions. The origin of the latter has been explored by investigating the dynamics of the created plume in ambient and argon atmosphere. Principal Component Analysis (PCA) was employed for dimensionality reduction based on the primary LIBS analysis. The principal components formation is grounded on the CN, Ca II, Ca I, and Na, LIBS data. Furthermore, applying the weighted k nearest neighbor (kNN) algorithm led to an accurate identification of the investigated specimens, since the evaluation metrics of specificity value were found to be in the range of 0.96–1.00, while that of accuracy was within 0.93–1.00.

Effect of laser polarization on atomic and ionic emissions in Laser-Induced Breakdown Spectroscopy (LIBS)

Laser-Induced Breakdown Spectroscopy (LIBS) is attracting a great deal of interest in qualitative and quantitative analysis of materials. Close observation reveals that developments in the science that underpins LIBS as an analytical technique are limited to either signal enhancement strategies or newer data analysis techniques that facilitate better interpretation of raw LIBS data. Developments related to the excitation part of LIBS have been restricted to the effects of laser beam characteristics or the effect of ambient experimental conditions. The influence, if any, of the polarization state of the excitation laser has largely remained unexplored. We address this lacuna by probing the influence of different polarization states of the excitation laser on LIBS spectra of metals (copper and silver) and non-metals (polyethylene) by analyzing the polarization-dependent behavior of both atomic and ionic emission lines. Our observations open the possibility of tackling the problem of relatively faint emissions from ionic species in the plasma by appropriately manipulating the polarization state of the incident laser beam; this may lead to easy-to-implement improvement in the performance of LIBS instruments.Graphical abstract

Deuterium supersaturated surface layer in tungsten: ion energy dependence

Properties of deuterium (D) supersaturated surface layers (DSSLs) formed in tungsten (W), such as thickness, internal microstructures, and D retention, are experimentally investigated as a function of the incident ion energy, E i. W samples were exposed to D plasmas in the PISCES-A linear plasma device in a range of E i ∼ 45–175 eV, while other plasma exposure parameters were fixed: sample temperature, T s, ∼423 K, ion flux, Γi, ∼1.2 × 1021 m−2s−1, and fluence, Φi, ∼3.0 × 1024 m−2. High-resolution, cross-sectional, transmission electron microscopy observations confirm that (1) a DSSL forms even at the lowest E i ∼ 45 eV, (2) the DSSL thickness, Δt DSSL, is found to decrease with decreasing E i from ∼11–12 nm at E i ∼ 175 eV to ∼5–6 nm at ∼45 eV, and to agree with approximately the maximum implantation depth calculated using SDTrimSP, and (3) high-density D nanobubbles with a diameter of ∼1 nm or less exist inside the DSSL, which is deemed to validate a theory-predicted vacancy stabilization process due to trapping of a solute D atom(s). Utilizing a D areal density of ∼4.2 × 1019 m−2 in the first 14 nm from the surface at E i ∼ 75 eV from nuclear reaction analysis and the measured E i dependence of Δt DSSL, our previous laser-induced breakdown spectroscopy data is updated: both dynamic and static D retention increase with decreasing E i, and the D/W atomic fraction during plasma exposure reaches ∼0.3 at E i ∼ 45 eV. A possible DSSL formation mechanism is proposed.

Statistical comparison of predictive models for quantitative analysis and classification in the framework of LIBS spectroscopy: A tutorial

Laser-Induced Breakdown Spectroscopy (LIBS) is a widely accepted technique used for both classification and quantification purposes considering complex and heterogeous samples. Based on a set of training spectra acquired from diverse and representative samples within a specific application domain, it becomes possible to apply various data processing techniques and modeling methods to construct the predictive model in question. Naturally the complexity of both the laser-matter and the laser-plasma interactions and the heterogeneity of natural samples often requires the development of various predictive models, which are then compared based on figures of merit such as the RMSEP (Root Mean Square Error of Prediction) value for quantification or the classification rate for qualitative analysis. Our ultimate goal is, of course, to select the model that appears to be the most accurate, which ultimately boils down to searching for the lowest RMSEP value or the highest classification rate. This is precisely where the whole problem lies because even if we observe a different level of error for two models, for example, this difference is not necessarily statistically significant. In such a case, we are therefore not allowed to say that the lower error indicates the best predictive model to consider. The purpose of this article is to provide a tutorial on introducing a statistical model comparison procedure, whether they are quantitative or qualitative. Two LIBS data sets have been used to illustrate the principles of the proposed method.

Combined in situ XRF–LIBS analyses as a novel method to determine the provenance of central Mediterranean obsidians

This work presents a new calibration method for determining the provenance of obsidian artefacts based on the combined use of XRF and laser-induced breakdown spectroscopy (LIBS). At first, obsidian samples collected from the main Mediterranean sources were characterized using portable XRF and LIBS systems. After data treatment, elemental information was used to carry out principal component analysis (PCA) for each technique. Rb, Sr, Zr, Y and Fe elements, detected by using XRF, were found to be the key parameters enabling obsidians discrimination. Likewise, LIBS data helped differentiating the analysed patterns by the intensity of their main elemental components (Ca, Al, Mg and K). After selecting the key parameters detected by each technique, a new data matrix combining XRF and LIBS data was finally built. According to PCA results, the discrimination of Mediterranean sources based on combined XRF–LIBS data ensured a higher reliability over mono-analytical models, by increasing the Euclidean distance between sources projections over three-dimensional principal components plots. Knowing that the representativeness of elemental data could be compromised by the presence of superficial degradation products or deposition patinas, a shot-to-shot comparison of in-depth LIBS analyses is finally proposed as a method to disclose whether the spot under analysis was superficially contaminated or altered. Thus, the proposed strategy based on the combined use of portable XRF and LIBS spectrometers could be particularly useful for the in situ analysis of obsidian artefacts that underwent superficial alteration or could be covered by patina products.Graphical abstract

Developing Tailored Data Combination Strategies to Optimize the SuperCam Classification of Carbonate Phases on Mars

AbstractThe SuperCam instrument onboard the Mars 2020 Perseverance rover investigates Martian geological targets by a combination of multiple spectroscopic techniques. As Raman, Visible‐Infrared Spectroscopy, and Laser‐Induced Breakdown Spectroscopy (LIBS) spectra deliver complementary information about the interrogated sample, the multivariate analysis of combined spectroscopic data sets is here proposed as a tool to optimize the SuperCam capability to discriminate mineral phases on Mars. For this purpose, the laboratory study of carbonate phases within the Ca‐Mg‐Fe ternary system were selected as representative case of study. After the characterization of model samples, the discrimination capability of mono analytical Raman, VISIR, and LIBS data sets was evaluated by applying a chemometric approach based on the combination of principal component analysis (for sample clustering) and Linear Discriminant Analysis (for mineral classification). Afterward, the low‐level combination (LL) of Raman, VISIR, and LIBS data was achieved by concatenating their spectra into a single data matrix. The mineral classification achieved by LL data sets outperformed the mono analytical ones, thus proving the complementarity between molecular and elemental spectroscopic techniques. Mineral classification was further improved by using a mid‐level data combination strategy. After evaluating benefits and limitations afforded by the proposed combination strategies, future developments are finally outlined. As such, the final objective of this research line is to develop a classification model based on data combination to optimize the capability of SuperCam in discriminating relevant minerals on Mars, this being a key requirement for the selection of the optimal targets to be cached for the future Mars Sample Return Mission.

Investigation into the Affect of Chemometrics and Spectral Data Preprocessing Approaches upon Laser-Induced Breakdown Spectroscopy Quantification Accuracy Based on MarSCoDe Laboratory Model and MarSDEEP Equipment

As part of China’s Tianwen-1 Mars mission, the Mars Surface Composition Detector (MarSCoDe) instrument on the Zhurong rover adopts laser-induced breakdown spectroscopy (LIBS) to perform chemical component detection of the materials on the Martian surface. However, it has always been a challenging issue to achieve high accuracy in LIBS quantification. This study investigated the effect of chemometrics and spectral data preprocessing approaches on LIBS quantification accuracy based on different chemometrics algorithms and diverse preprocessing methods. A total of 2340 LIBS spectra were collected from 39 kinds of geochemical samples by a laboratory duplicate model of the MarSCoDe instrument. The samples and the MarSCoDe laboratory model were placed in a simulated Martian atmosphere environment based on equipment called the Mars-Simulated Detection Environment Experiment Platform (MarSDEEP). To quantify the concentration of MgO in the samples, we employed two common LIBS chemometrics; i.e., partial least squares (PLS) and a back-propagation neural network (BPNN). Meanwhile, in addition to necessary routine preprocessing such as dark subtraction, we used five specific preprocessing approaches, namely intensity normalization, baseline removal, Mg-peak wavelength correction, Mg-peak feature engineering, and concentration range reduction. The results indicated that the performance of the BPNN was better than that of the PLS and that the preprocessing of Mg-peak wavelength correction had the most prominent effect to improve the quantification accuracy. The results of this study are expected to provide inspiration for the processing and analysis of the in situ LIBS data acquired by MarSCoDe on Mars.

Cross-modal generative models for multi-modal plastic sorting

Automated sorting through chemometric analysis of plastic spectral data could be a key strategy towards improving plastic waste management. Deep learning is a promising chemometric tool, but further development through multi-modal deep learning has been limited by lack of data availability. A new Multi-modal Plastic Spectral Database (MMPSD) consisting of Fourier Transform Infrared (FTIR), Raman and Laser-induced Breakdown Spectroscopy (LIBS) data for each sample in the database is introduced in this work. MMPSD serves as the basis for novel cross-modality generative model technique termed Spectral Conversion Autoencoders (SCAE), which generates synthetic data from data of another modality. SCAE is advantageous over traditional generative models like Variational Autoencoders (VAE), as it can generate class specific synthetic data without the need to train multiple models for each data class. MMPSD also facilitated the exploration of multi-modal deep learning, which improved the classification accuracy as compared to an uni-modal approach from 0.933 to 0.970. SCAE can further be combined with multi-modal methods to achieve a higher accuracy of 0.963 while still using a single sensor to reduce costs, which can be applied for multi-modal augmentation from FTIR sensors used in industrial sorting.

Feasibility of laser-induced breakdown spectroscopy to elucidate elemental changes in human tooth ankylosis

Tooth ankylosis is a pathological condition associated with the loss of physiological tooth mobility when the firm fusion between the alveolar bone and the tooth root occurs. Causes of dental ankylosis are uncertain, so the analysis of elemental distribution in ankylotic and surrounding tissues could provide additional information about its initiation and progression. Here, we used Laser-Induced Breakdown Spectroscopy (LIBS) to determine differences in the mineral composition among ankylotic tissue, bone, and dental tissue taking advantage of its high throughput and multi-elemental capability. Elemental imaging was performed with a spatial resolution of 30 μm to evaluate the distribution of carbon, calcium, magnesium, phosphorus, and strontium in human tooth. To further verify the difference in the mineral composition of ankylotic tissue, the semi-quantitative content of these elements was compared within the region of interest. We revealed a significant increase in calcium, magnesium, and phosphorus contents in the ankylotic tissues. However, the contents of magnesium and phosphorus were not significantly changed. This finding suggests a mineral disbalance only of just certain elements in the tooth-bone interface area during the spread of ankylosis associated with an intense calcification of connective tissue. This paper brings a feasibility study and shows the way of LIBS data interpretation. We propose that the LIBS analysis on a micro-scale can contribute to the understanding of ankylotic tissue composition and can distinguish even small differences of carbon, calcium, magnesium, phosphorus, and strontium contents on the tooth-bone boundary during the initiation of ankylosis. Therefore, it represents a new useful tool for their future, more extensive analyses.

Analysis of Ti-inclusions in high carbon chromium bearing steel by laser-induced breakdown spectroscopy

The presence of Ti-inclusions has a significant impact on the fatigue life of bearing steel. Laser-induced breakdown spectroscopy (LIBS) is a useful tool for analyzing Ti-inclusions, despite the fact that the effective excitation area of LIBS is actually larger than the pre-set spot diameter. The area affected by the LIBS spot ranges from 150 μm to 376.6 μm. A new parameter L has been introduced for better understanding the characterization of Ti-inclusions detected in SEM-EDS and LIBS measurements, When the L threshold falls below the value of 188.3 μm, the SEM-EDS and LIBS data are obtained from the same Ti-inclusion. The study demonstrates that there is a linear relationship between LIBS intensity and Ti-inclusions area, which was tested both theoretically and practically. The analysis of six samples of rolling GCr15 bearing steel with a Cr element content of 1.5% shows that one regression line is enough for analyzing the same type of inclusion processed under the same conditions. This method can significantly reduce the workload and analysis time during the actual analysis process.

Carbonate Detection With SuperCam in Igneous Rocks on the Floor of Jezero Crater, Mars

AbstractPerseverance explored two geological units on the floor of Jezero Crater over the first 420 Martian days of the Mars2020 mission. These units, the Máaz and Séítah formations, are interpreted to be igneous in origin, with traces of alteration. We report the detection of carbonate phases along the rover traverse based on laser‐induced breakdown spectroscopy (LIBS), infrared reflectance spectroscopy (IRS), and time‐resolved Raman (TRR) spectroscopy by the SuperCam instrument. Carbonates are identified through direct detection of vibrational modes of CO3 functional groups (IRS and TRR), major oxides content, and ratios of C and O signal intensities (LIBS). In Séítah, the carbonates are consistent with magnesite‐siderite solid solutions (Mg# of 0.42–0.70) with low calcium contents (<5 wt.% CaO). They are detected together with olivine in IRS and TRR spectra. LIBS and IRS also indicate a spatial association of the carbonates with clays. Carbonates in Máaz are detected in fewer points, as: (a) siderite (Mg# as low as 0.03); (b) carbonate‐containing coatings, enriched in Mg (Mg# ∼0.82) and spatially associated with different salts. Overall, using conservative criteria, carbonate detections are rare in LIBS (∼30/2,000 points), IRS (∼15/2,000 points), and TRR (1/150 points) data. This is best explained by (a) a low carbonate content overall, (b) small carbonate grains mixed with other phases, (c) intrinsic complexity of in situ measurements. This is consistent with orbital observations of Jezero crater, and similar to compositions of carbonates previously reported in Martian meteorites. This suggests a limited carbonation of Jezero rocks by locally equilibrated fluids.

Improving spatially-resolved lithium quantification in drill core samples of spodumene pegmatite by using laser-induced breakdown spectroscopy and pixel-matched reference areas

Laser ablation-inductively coupled plasma-time of flight mass spectrometry (LA-ICP-TOFMS) concentrations were used to develop accurate calibration models for laser-induced breakdown spectroscopy (LIBS) mappings of pegmatitic drill cores samples. Both methods were applied on the same area of drill core samples, providing two spatially-resolved datasets for this area. The datasets were aligned pixel by pixel to create a pixel-matched reference area that covered the heterogeneity of the complete drill core. This way, different matrix effects affecting LIBS intensities could be taken into account and accurate spatial quantification of Li2O, SiO2, Al2O3, Na2O, and K2O from LIBS measurements was enabled.In particular, LIBS intensities and LA-ICP-TOFMS concentrations of individual pixels of the reference area were used as the input for a linear Partial Least Square Regression (PLSR) and a non-linear Least Square Support Vector Machines (LS-SVM) calibration model. Varying numbers between 100 and 2000 pixels were used for model creation, and root mean square error (RMSE) and R2 of each model were compared. Better values were achieved for the LS-SVM calibration model. Based on these results, the PLSR model was discarded and only the LS-SVM model with 1000 train pixels was further validated.For two different validation areas, LA-ICP-TOFMS concentrations were compared to LIBS-based concentrations obtained from the LS-SVM calibration model. The spatially-resolved quantification results of the LIBS data agree very well with the independently analysed LA-ICP-TOFMS concentrations, which is e.g. reflected in R2 values between 0.83 and 0.96 (mean 0.89) for Li2O, SiO2, Al2O3, Na2O, and K2O. In general, it was shown that the combination of LA-ICP-TOFMS and LIBS yields a comprehensive dataset for robust multivariate calibration. This enables spatially-resolved LIBS-based quantification of pegmatite samples that are subject to significant physical and chemical matrix effects.

Accuracy enhancement of laser-induced breakdown spectroscopy by polarization spectrum fusion

Laser-induced breakdown spectroscopy (LIBS) has been proven to be an effective way for online analysis of coal properties (such as ash content, volatile content, and calorific value), but the quantitative analysis accuracy still needs to be improved. Herein, a polarization spectral data fusion scheme was proposed to obtain more abundant spectral information and improve the performance of quantitative analysis models. First, the polarization effect of laser-induced coal plasma emission was analyzed using the Stokes parameters of the average spectra. Due to the partially polarized phenomenon of emission, fused data of PRLIBS (0°, 0) (horizontal plasma polarization spectra) and PRLIBS (90°, 0) (vertical plasma polarization spectra) was utilized to establish an efficient prediction model to obtain high prediction accuracy. For fusion models, the root mean square error prediction (RMSEP) of ash content, volatile content and calorific values were 1.497%, 0.595%, and 0.597%, respectively, and the coefficients of determination (R2) were 0.980, 0.968, and 0.978, respectively. Comparison with regression models for individual PRLIBS (0°, 0), PRLIBS (90°, 0), and LIBS data, the data fusion scheme significantly improved the performance of the prediction model. The improvement in model performance is due to the fact that there is no perfect correlation between PRLIBS (0°, 0) and PRLIBS (90°, 0), whilst polarization spectral fusion makes it possible to acquire more number of characteristic spectral lines with high importance score. The results demonstrated the feasibility of PRLIBS spectral fusion to provide better analytical results for ash content, volatile content, and calorific values of coal.