Laser-induced Breakdown Spectroscopy Research Articles

Excitation of optically trapped single particles using femtosecond pulses.

Excitation from optically trapped particles is examined through laser-induced breakdown spectroscopy following interactions with mJ-level fs pulses. Optical emissions from sub-ng ablation of precisely positioned cupric oxide microparticles are used as a method to spatially resolve laser-particle interactions resulting in excitation. External focusing lenses are often used to change the dynamics of nonlinear self-focusing of fs pulses to form laser filaments or, alternatively, to form very intense air plasmas. Given the significant implications external focusing has on laser propagation and plasma conditions, single-particle emissions are studied with focusing lenses ranging from 50 to 300 mm. It is shown that, while single particles are less excited at longer focal lengths due to limited energy transfer through laser-particle interactions, the cooler plasma results in a lower thermal background to reveal resolved single-shot emission peaks. By developing an understanding in the fundamental interaction that occurs between single particles and fs pulses and filaments, practical improvements can be made for atmospheric remote sensing of low-concentration aerosols.

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.

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review.

Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.

Enhancing Diagnostic Capabilities for Occupational Lung Diseases Using LIBS Imaging on Biopsy Tissue.

Laser-induced breakdown spectroscopy (LIBS) imaging continues to gain strength as an influential bioanalytical technique, showing intriguing potential in the field of clinical analysis. This is because hyperspectral LIBS imaging allows for rapid, comprehensive elemental analysis, covering elements from major to trace levels consistently year after year. In this study, we estimated the potential of a multivariate spectral data treatment approach based on a so-called convex envelope method to detect exotic elements (whether they are minor or in trace amounts) in biopsy tissues of patients with occupational exposure-related diseases. More precisely, we have developed an approach called Interesting Features Finder (IFF), which initially allowed us to identify unexpected elements without any preconceptions, considering only the set of spectra contained in a LIBS hyperspectral data cube. This task is, in fact, almost impossible with conventional chemometric tools, as it entails identifying a few exotic spectra among several hundred thousand others. Once this detection was performed, a second approach based on correlation was used to locate their distribution in the biopsies. Through this unique data analysis pipeline to processing massive LIBS spectroscopic data, it was possible to detect and locate exotic elements such as tin and rhodium in a patient's tissue section, ultimately leading to a possible reclassification of their lung condition as an occupational disease. This review will thus demonstrate the potential of this new diagnostic tool based on LIBS imaging in addressing the shortcomings of approaches developed thus far. The proposed data processing approach naturally transcends this specific framework and can be leveraged across various domains of analytical chemistry, where the detection of rare events is concealed within extensive data sets.

Effect of Femtosecond Ultraviolet and Infrared Laser Wavelength on Plasma Characteristics of Metals, Ceramics and Glass Samples Using Femtosecond Laser-Induced Breakdown Spectroscopy.

In this work, we present studies on the effect of laser wavelengths on the laser-induced plasma characterization using a femtosecond (fs) ytterbium-doped potassium-gadolinium tungstate (Yb:KGW) laser. Plasma plumes of copper, steel, ceramics, and glass samples were induced using a multiple shot of 200 fs laser pulses with 1030 nm and 343 nm wavelengths at fixed laser fluence () and analyzed using the laser-induced breakdown spectroscopy (LIBS) technique. Time-resolved fs-LIBS measurements were performed on the same set of samples and under the same experimental conditions. For the calculation of plasma parameters, the set of spectral lines of Cu(I) (for copper sample), Fe(I) (for steel sample), and Ca(I), K(I) (for glass and ceramics samples) were observed. The plasma electron temperature and density were evaluated from the Boltzmann plots and Stark-broadening profiles of the plasma spectral lines, assuming the local thermodynamic equilibrium condition. Time-resolved plasma temperature and electron density for fs-LIBS using ultraviolet (UV) and infrared (IR) laser wavelengths were analyzed and no significant dependence on fs laser wavelength was observed for any of the samples. However, for all samples the signal-to-noise ratio (SNR) significantly increased using UV laser radiation: copper (), steel (), glass (), and ceramics (). Therefore, by using a fs UV laser wavelength for laser-induced breakdown spectroscopy experiments, for certain materials the SNR and at the same time the limit of detection can be significantly enhanced.

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.

Depth profile analysis and high-resolution surface mapping of lithium isotopes in solids using laser-induced breakdown spectroscopy (LIBS)

Lithium isotopic analysis is of interest in various fields from biology to energy. In addition to analytical methods commonly used for that purpose, a new complementary method for determining the spatial resolution of isotopic ratio in solid samples was previously developed: laser induced self-reversal isotopic spectrometry (LIBRIS), derived from laser-induced breakdown spectroscopy (LIBS). In this paper, we present a proof of concept of fast spatially-resolved quantitative isotopic analysis of lithium by LIBS both in depth (z resolution) and in configuration of high-resolution surface mapping (x and y resolution). First, a depth profile measurement where a 9% relative uncertainty on the 6Li isotopic ratio and 0.74 μm depth resolution is obtained. Then, we introduce the first high-resolution lithium isotopic mapping obtained by LIBS with a 3.3 μm lateral resolution and a typical 40% relative uncertainty on the 6Li isotopic ratio is obtained. Those results could be applied in the future to a large number of solid samples where fast information on the lithium isotopic distribution are needed, for instance in solid-state batteries development.

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.

Detection and diagnosis of bacterial pathogens in blood using laser-induced breakdown spectroscopy

The ability to rapidly and accurately detect and identify pathogenic bacteria in clinically-obtained blood specimens with laser-induced breakdown spectroscopy (LIBS) is evaluated. Samples of blood obtained from five patients in a local hospital were confirmed to be negative for the presence of bacteria by the pathology department and were then tested with LIBS. Specimens of blood were tested as obtained from the hospital with no other alteration as control samples and were also intentionally spiked with known aliquots of Escherichia coli, Staphylococcus aureus, Enterobacter cloacae, and Pseudomonas aeruginosa to simulate blood infections. LIBS spectra were acquired from blood deposited on nitrocellulose filters. The intensities of 15 emission lines measured in the spectra and 92 ratios of those line intensities were used as 107 independent variables in a partial least squares discriminant analysis (PLS-DA) to discriminate between sterile control samples and those spiked with bacteria. In addition, the entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected.The PLS-DA test possessed a 96.3% sensitivity and a 98.6% specificity for the detection of pathogenic bacteria in blood when 776 spectra from 26 filters were tested by removing one entire filter at a time from the model and testing each spectrum individually. When all the spectra obtained from a filter were averaged to enhance the signal to noise of the spectrum, 19 of 19 filters of infected blood tested positive and 7 of 7 filters with sterile blood tested negative, yielding 100% sensitivity and 100% specificity. The PCA-ANN test performed on the entire LIBS spectrum possessed a 100% sensitivity and 100% specificity when using 80% of the data to build a model and withholding 20% for cross-validation testing. The same PCA-ANN performed on each of the 19 filters individually, using the other 18 filters to build the model, possessed an average sensitivity of 85.5%, an average specificity of 95.0%, and a classification accuracy of 92.5%. These results indicate the potential usefulness of LIBS for detecting and diagnosing blood infections in a clinical setting.

Improved convolutional neural network-assisted laser-induced breakdown spectroscopy for identification of soil contamination types

Identification of soil contamination types is of great scientific significance for soil remediation and environmental pollution control. However, traditional identification methods for soil determination are time-consuming, laborious, and complicated. Here, we proposed an accurate method for identifying soil contamination types based on laser-induced breakdown spectroscopy (LIBS) and an improved convolutional neural network (CNN) model. The spectral feature extraction-based multiple attention residual network (SFEMARNet) model was constructed to extract detailed features by spectral feature extraction (SFE) modules, and highlight useful features by multiple attention residual (MAR) modules in LIBS spectral data. In addition, deep learning models and machine learning models were used to identify the data. The results showed that the SFEMARNet model achieved an accuracy of 98.75% on the test set. The recall, precision, and F1-score of the models reached 98.78%, 98.75%, and 98.76%, respectively, which were significantly better than the three deep learning models and of four machine learning models. It seems that the SFEMARNet model combined with LIBS technology may be a potential method for the accurate identification of soil contamination types.

Laser-Induced Breakdown Spectroscopy in Biological Samples: A Review of Experiments with Soft Tissues

This article reviews the advances made during the past two decades in the application of Laser-Induced Breakdown Spectroscopy (LIBS) to biological samples, specifically soft tissues (both animal and human). The first sections include a historical overview and a summary of the biomedical relevance of analyzing metals in these tissues. Next, statistical methods employed in some works are presented, along with a detailed description of the innovations developed in experimental systems. The remainder of the review reports the approaches used in the experiments, focusing on a description of the advances that have enabled the successful application of LIBS to soft tissues. The results are evaluated, and the major challenges remaining for this type of sample are discussed. The aim of this review is to provide useful information that encourages future research on LIBS for biological samples.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566% and 0.1024%. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Crater-Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy.

Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater-spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater-spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater-spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.

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 decay 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.

Quantification of H2O in Amorphous and Crystalline Ferric Sulfates Relevant to Mars

AbstractCrystalline ferric sulfates (e.g., ferricopiapite and Fe(OH)SO4) have been proposed at multiple locations on Mars by the orbiter. While at Meridiani Planum and Gale Crater by rover missions, amorphous ferric sulfates were also suggested to exist in soils and sedimentary rocks. Ferric sulfates with different hydration degrees may play a key role in the water cycle on Mars. In order to understand the role of the hydrated ferric sulfates in the water cycle and their exact hydration states on Mars, five crystalline ferric sulfates and seven amorphous ferric sulfates with different hydration states were synthesized in the laboratory. The water content (number of H2O molecules per ferric sulfate molecule) was quantified by Raman spectroscopy and Laser‐induced breakdown spectroscopy (LIBS), respectively. It was found that the amorphous ferric sulfates water content has a good relationship with the SO4 tetrahedron main Raman feature position around 1,000 cm−1, shown as the intensity and area of water feature around 3,500 cm−1 over SO4 tetrahedron main Raman feature around 1,000 cm−1, respectively. Twelve ferric sulfates' Hα emission line area at 656.7 nm was normalized by the O emission line area at 778 nm in LIBS spectra. The crystalline and amorphous ferric sulfates all showed a good relationship between the values of normalized results and the water content. These results will aid us in precisely constraining the exact phases of hydrated ferric sulfates, provide a better reference for ChemCam, SuperCam, and Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals data interpretation and their use to quantify the water content in detected targets.

Sensitivity and stability improvement on slippery surface-aggregated substrate for trace heavy metals detection using NELIBS

The sensitive and stable detection of trace heavy metals in liquid is crucial given its profound impact on various aspects of human life. Currently, nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) with dried droplet method (DDM) is widely applied for heavy metals detection. Nevertheless, the coffee ring effect (CRE) in DDM affects the stability, accuracy, and sensitivity of NELIBS. Here, we developed a slippery surface-aggregated substrate (SS substrate) to suppress the CRE and enrich analytes, and form a plasmonic platform for NELIBS detection. The SS substrate was prepared by infiltrating perfluorinated lubricant into the pores of PTFE membrane. The droplet, with targeted elements and gold nanoparticles, was dried on the SS substate to form the plasmonic platform for NELIBS analysis. Then, trace heavy metal elements copper (Cu) and manganese (Mn) were analyzed by NELIBS. The results of Cu (RSD = 5.60%, LoD = 3.72 μg/L) and Mn (RSD = 7.42%, LoD = 6.37 μg/L), illustrated the CRE suppression and analytes enrichment by the SS substrate. The results verified the realization of stable, accurate and sensitive NELIBS detection. And the LoDs succeeded to reach the standard limit of China (GB/T 14848-2017). Furthermore, the results for groundwater detection (relative error: 5.92% (Cu) and 4.74% (Mn)), comparing NELIBS and inductively coupled plasma mass spectrometry (ICP-MS), validated the feasibility of the SS substrate in practical applications. In summary, the SS substrate exhibits immense potential for practical application such as water quality detection and supervision.

Simple and sensitive determination of Cr (III), Cu (II) and Pb (II) in tea infusions using AgNPs-modified resin combined with laser-induced breakdown spectroscopy

The detection of heavy metals in tea infusions is important because of the potential health risks associated with their consumption. Existing highly sensitive detection methods pose challenges because they are complicated and time-consuming. In this study, we developed an innovative and simple method using Ag nanoparticles-modified resin (AgNPs-MR) for pre-enrichment prior to laser-induced breakdown spectroscopy for the simultaneous analysis of Cr (III), Cu (II), and Pb (II) in tea infusions. Signal enhancement using AgNPs-MR resulted in amplification with limits of detection of 0.22 μg L−1 for Cr (III), 0.33 μg L−1 for Cu (II), and 1.25 μg L−1 for Pb (II). Quantitative analyses of these ions in infusions of black tea from various brands yielded recoveries ranging from 83.3% to 114.5%. This method is effective as a direct and highly sensitive technique for precisely quantifying trace concentrations of heavy metals in tea infusions.

A rapid in-situ hardness detection method for steel rails based on LIBS and machine learning

The railway, as a public transportation system, has played a significant role in global economic development. However, the pursuit of high speed and heavy load in trains has brought significant bending and shear stresses on the rails, making the performance of steel rails a notable focal point. The properties of steel rails are crucial factors determining the operational safety of high-speed trains, while the surface hardness is regarded as a key mechanical characteristic. This study is to develop a new rapid in-situ method to measure the hardness of steel rails, by employing Laser-Induced Breakdown Spectroscopy (LIBS) and specified analysis technology. Three distinct methods, including spectral line intensity ratios, plasma excitation temperature and machine learning, have been compared and analysed. Particularly, a multivariate model is established and optimized using the machine learning methods for U71Mn steel rail hardness analysis. In the machine learning algorithms, variance normalization is utilized, resulted in a significant improvement for the information retention during the data dimensionality reduction process. Subsequently, twelve algorithm combinations were explored, revealing that the Particle Swarm Optimization employed in Support Vector Regression (PSO-SVR) yielded the lowest mean squared error (MSE). Further refinement of the PSO-SVR was achieved through the incorporation of adaptive stochastic weights, resulting in an elevated coefficient of determination (R2) to 0.9876. Finally, the performance of the model on the new five samples was validated with an R2 of 0.9864. Otherwise, its potential applicability may be extended to broader domains, providing robust support for enhancing the precision and reliability of LIBS technology in surface hardness quantitative analysis.

The First Application of Laser-Induced Breakdown Spectroscopy: A Fast-Analytical Technique in Targeted Search for Elements in Geological Samples from Deep Boreholes in Latvia

Abstract Laser-induced breakdown spectroscopy (LIBS) provides a rapid, cost-effective, and extra-sensitive analysis of geological samples to make preliminary conclusions about the presence of valuable elements up to the trace levels in the ore. We present the first results of a highly sensitive qualitative analysis of the core samples of geological ore from two boreholes in Latvia (Staicele 1, from a depth range of 794–802 m, and Garsene (Subate) 2A, from a depth range of 1102–1103 m) using LIBS. Our measurements using this technique confirmed the high iron content and indicated traces of rare and high in-demand metals (such as Ti, V, Co, Sm, etc.) in the sample from Staicele, renewing interest in studying boreholes across Latvia. The presented pilot studies demonstrated effectiveness and unique possibility in performing a very sensitive and time-saving qualitative analysis of the composition of samples of ores from the old but still valuable borehole cores by using the LIBS method. We compare these measurements with other methods of sample analysis.

Comparing laser-induced breakdown spectroscopy and visible near-infrared spectroscopy for predicting soil properties: A pan-European study

Soil organic carbon (SOC), texture, clay/organic carbon (OC) ratio, and extractable phosphorus are among the key soil descriptors representing the physical, chemical, and biological properties. However, analyzing these soil properties using conventional methods of analysis is time-consuming and often involves the use of hazardous chemicals. Therefore, scaling these methods to analyze more samples covering large geographical distributions within a reasonable turn-around time has become a challenge. In this study, two rapid techniques, namely laser-induced breakdown spectroscopy (LIBS) and visible and near-infrared spectroscopy (vis-NIRS), were compared for the prediction of SOC, texture, extractable phosphorus, and clay/OC ratio. A total of 896 samples collected from 23 European countries from the Land Use/Cover Area frame Survey (LUCAS) database were used to represent a wide distribution in terms of soil texture and geographical coverage. The data was manually split into calibration and validation samples while ensuring a representative geographical distribution in the calibration and validation set for the selected samples. Partial least square regression (PLSR) models were developed for both LIBS and vis-NIRS and compared based on prediction accuracy. LIBS outperformed vis-NIRS in predicting SOC, sand content, and clay/OC ratio, with root mean square error of prediction (RMSEP) values of 1.79 %, 12 %, and 7.69, respectively. Conversely, vis-NIRS yielded slightly higher RMSEP values for SOC, sand content, and clay/OC ratio predictions at 1.88 %, 15 %, and 8.13, respectively. Both LIBS and vis-NIRS performed similarly in predicting clay content, with an RMSEP of 6 %. However, there was a low predictive ability for silt and phosphorus concentration, as shown by the weak correlations (R2 < 0.5) between the predicted and reference soil properties. There was no significant difference between LIBS and vis-NIRS predictions for clay, silt, and SOC content (p > 0.05). However, there was a significant difference between the predicted sand content (p = 0.011) and clay/OC ratio (p < 0.001). The overall better performance of LIBS highlights its potential for application in a wide range of soils, especially for soils covering large geographical distributions and varying soil properties. Further, both techniques present opportunities for rapid determination of soil properties important for evaluating soil health on continental scales.