Use Of Laser-induced Breakdown Spectroscopy Research Articles

Surface hardness imaging of a low-alloy steel using laser-induced breakdown spectroscopy

Conventional tactile hardness testing methods such as Brinell, Rockwell or Vickers rely on direct mechanical contact, which results in significant surface damage and prohibits their application to complex specimen geometries. Furthermore, the sample must have a certain thickness for tactile testing methods to be applied. In this work, we investigated the use of laser-induced breakdown spectroscopy as a fast non-quantitative alternative for visualizing surface hardness gradients. To eliminate the influence of different chemical compositions, we manually heat-treated low-alloy steel pieces cut from the same raw material and batch. By partially quenching a sample piece, we were able to obtain a hardness gradient along the longitudinal axis. We found a positive correlation between the ratio of ionic to atomic line intensities of iron (I Fe II 263.1 nm / I Fe I 358.1 nm) and the mechanical hardness of the sample surface. By scanning the surface and measuring the line intensity ratios, we were able to obtain a spatially resolved map directly correlating with the surface hardness distribution. Additionally, it was observed that the irradiation of laser pulses resulted in significant surface alterations, thereby invalidating subsequent measurements and scans at identical positions.

Immediate traceability of ash based on laser-induced breakdown spectroscopy and machine learning

This article reports on an advanced level physical laboratory experiment designed for college level undergraduate education and for scholars who need specialized training on using and interpreting Laser-Induced Breakdown Spectroscopy. The technical principle, experimental operation and sample preparation of Laser-Induced Breakdown Spectroscopy are introduced in detail. A presentation and discussion of the use of Laser-Induced Breakdown Spectroscopy in the traceability of four common samples is emphasized. Combining Laser-Induced Breakdown Spectroscopy with Machine Learning, two distinct datasets are constructed through the extraction of spectral features. Dimensionality reduction of spectral data is performed using Linear Discriminant Analysis, while the Random Forest model is employed for provenance classification. Finally, the interpretability of the Random Forest model is leveraged to explore the contributions of different spectral elements to provenance tracing. Results demonstrated the system’s effectiveness in not only accurately identifying ash types but also in elucidating the influential chemical components, offering significant implications for material analysis and environmental monitoring. On an educational standpoint, this paper will allow any reader, in particular, undergraduate and graduate students, to gain a better understanding of the theory and practice of Laser-Induced Breakdown Spectroscopy and machine learning.

Automated, real-time material detection during ultrashort pulsed laser machining using laser-induced breakdown spectroscopy, for process tuning, end-pointing, and segmentation.

The rapid, high-resolution material processing offered by ultrashort pulsed lasers enables a wide range of micro and nanomachining applications in a variety of disciplines. Complex laser processing jobs conducted on composite samples, require an awareness of the material type that is interacting with laser both for adjustment of the lasering process and for endpointing. This calls for real-time detection of the materials. Several methods such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and energy dispersive X-Ray spectroscopy (EDS) can be used for material characterization. However, these methods often need interruption of the machining process to transfer the sample to another instrument for inspection. Such interruption significantly increases the required time and effort for the machining task, acting as a prohibitive factor for many laser machining applications. Laser induced breakdown spectroscopy (LIBS) is a powerful technique that can be used for material characterization, by analyzing a signal that is generated upon the interaction of laser with matter, and thus, it can be considered as a strong candidate for developing an in-situ characterization method. In this work, we propose a method that uses LIBS in a feedback loop system for real time detection and decision making for adjustment of the lasering process on-the-fly. Further, use of LIBS for automated material segmentation, in the 3D image resulting from consecutive lasering and imaging steps, is showcased.

Identification of fluoroquinolone-resistant Mycobacterium tuberculosis through high-level data fusion of Raman and laser-induced breakdown spectroscopy.

Accurate and rapid diagnosis of drug susceptibility of Mycobacterium tuberculosis is crucial for the successful treatment of tuberculosis, a persistent global public health threat. To shorten diagnosis times and enhance accuracy, this study introduces a fusion model combining laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. This model offers a rapid and accurate method for diagnosing drug-resistance. LIBS and Raman spectroscopy provide complementary information, enabling accurate identification of drug resistance in tuberculosis. Although individual use of LIBS or Raman spectroscopy achieved approximately 90% accuracy in identifying drug resistance, the fusion model significantly improved identification accuracy to 98.3%. Given the fast measurement capabilities of both techniques, this fusion approach is expected to markedly decrease the time required for diagnosis.

Laser cleaning of paint layers on white marble surface based on cooperative use of laser-induced breakdown spectroscopy and image binarization

The key to precisely removing paint layers sprayed onto stone surfaces using laser cleaning without damaging the stone substrate is to select the optimized laser parameters based on the online monitoring methods. In this paper, a pulsed laser beam with a high-pulse repetition frequency of 100 kHz, a pulse width of 200 ns, and a wavelength of 1064 nm was used to clean 39 µm- and 31 µm- thick of gold and silver paint layers off of white marble surfaces. The optimized average laser power for removing the paint layers was determined to be 40 W using laser-induced breakdown spectroscopy (LIBS) method and an image binarization method. Under the optimized average laser power of 40 W, the optimized spot overlap ratios for cleaning the layers of gold and silver paint were determined to be 10% and 30% respectively, using the image binarization method. The corresponding optimized number of cleaning shots was 5 and 4, respectively. The laser cleaning efficiency for both the gold and silver paint layers exceeded 95%. It was thereby demonstrated that this research protocol based on the cooperative use of two online monitoring methods to determine the optimized laser parameters in sequence (average laser power → spot overlap ratio → number of cleaning shots) not only optimizes the laser cleaning efficiency for different paint layers but does so without causing any damage to the white marble substrate.

Accurate identification of inflammation in blood based on laser-induced breakdown spectroscopy using chemometric methods

Diagnosis of inflammation in the blood requires multiple medical examinations that rely heavily on doctors' experiences, which offer a large room for error. Thus, diagnosis becomes complex and time consuming. Herein, we proposed the use of laser-induced breakdown spectroscopy (LIBS) and chemometric methods to rapidly and accurately diagnose inflammation in blood. Serum samples were collected from 10 healthy people and 10 patients suffering from inflammation in blood. The training and test sets were divided according to each individual in a ratio of 7:3. After excluding considerably different divisions of training and test sets (DTTs) using the Wilcoxon test, 10 DTTs were randomly selected. With these 10 DTTs, backpropagation neural network (BPNN) models were built for substrate optimization, and glass slides exhibited the best performance with an average accuracy of 80.87%. Using glass slides and a multiplicative scatter correction–mean impact value–backpropagation neural network (MSC–MIV–BPNN) model, the accuracy of the best DTT was improved to 93.00%. For the identification effect validation of the improved model, the same features were selected from other DTTs to build new MSC–MIV–BPNN models. The accuracies of all the DTTs were increased by 4.67%–16.33% compared to the original BPNN models. Furthermore, the validation time of a single DTT was ∼0.7 s. These results demonstrated that LIBS combined with the MSC–MIV–BPNN model could accurately and rapidly identify samples with inflammation in blood from healthy blood samples for emergency applications.

Real-time classification of aluminum metal scrap with laser-induced breakdown spectroscopy using deep and other machine learning approaches

In the recycling industry, the use of deep spectral convolutional networks for the purpose of material classification and composition estimation is still limited, despite the great opportunities of these techniques. In this study, the use of Laser-Induced Breakdown Spectroscopy (LIBS), Machine Learning (ML), and Deep Learning (DL) for the three-way sorting of Aluminum (Al) is proposed. Two sample sets of Al scrap are used: one containing 733 pieces for pre-training and validation with a ground truth of X-Ray Fluorescence (XRF), and the second containing 210 pieces for testing for unknown compositions. The proposed method comprises a denoising system combined with a method that extracts 145 features from the raw LIBS spectra. Further, three ML algorithms are assessed to identify the best-performing one to classify unknown pieces of aluminum post-consumer scrap into three commercially interesting output classes. The classified pieces are weighed, melted, and analyzed using spark analysis. Finally, to optimize the best-performing ML system, three state-of-the-art denoising and three feature extraction networks are pre-trained for learning the baseline correction and the proposed feature extraction. Transfer Learning from the six pre-trained networks is applied to create and evaluate 24 end-to-end DL models to classify Al in real-time from >200 spectra simultaneously. The end-to-end DL scheme shows the advantages of learning and denoising the spectra, allowing the transfer of traditional spectral analysis knowledge and the proposed feature extraction into DL, where the network learns from the entire spectrum. The best results for ML and DL were obtained with Random Forest processing one spectrum in 150 ms and BPNN+GHOSTNET(Fine-tuning) processing 200 spectra in 9 ms, which achieved 0.80 Precision, 0.81 Recall, 0.80 F1-score, and 0.80 Precision, 0.79 Recall, 0.79 F1-score, respectively.

Effect of LIBS-Induced Alteration on Subsequent Raman Analysis of Iron Sulfides.

Mineral alteration is a possible side effect of spectroscopic techniques involving laser ablation, such as laser-induced breakdown spectroscopy (LIBS), and is related to the interaction of the generated plasma and ablated material with samples, dust, or ambient atmosphere. Therefore, it is essential to understand these interactions for analytical techniques involving laser ablation, especially for space research. In this combined LIBS–Raman analytical study, pyrite (FeS2) and pyrrhotite (Fe1–xS) samples have been consecutively measured with LIBS and Raman spectroscopy, under three different atmospheric conditions: ∼10–4 mbar (atmosphereless body), ∼7 mbar, and Martian atmospheric composition (Martian surface conditions), and 1 bar and Martian atmospheric composition. Furthermore, a dust layer was simulated using ZnO powder in a separate test and applied to pyrite under Martian atmospheric conditions. In all cases, Raman spectra were obscured after the use of LIBS in the area of and around the formed crater. Additional Raman transitions were detected, associated with sulfur (pyrite, 7.0 mbar and 1.0 bar), polysulfides (all conditions), and magnetite (both minerals, 1.0 bar). Magnetite and polysulfides formed a thin film of up to 350–420 and 70–400 nm in the outer part of the LIBS crater, respectively. The ZnO-dust test led to the removal of the dust layer, with a similar alteration to the nondust pyrite test at 7.0 mbar. The tests indicate that recombination with the CO2-rich atmosphere is significant at least for pressures from 1.0 bar and that plasma–dust interaction is insignificant. The formation of sulfur and polysulfides indicates fractionation and possible loss of volatile elements caused by the heat of the LIBS laser. This should be taken into account when interpreting combined LIBS–Raman analyses of minerals containing volatile elements on planetary surfaces.

Comparison of Mg/Ca concentration series from Patella depressa limpet shells using CF-LIBS and LA-ICP-MS

The elemental composition of marine mollusk shells can offer valuable information about environmental conditions experienced by a mollusk during its lifespan. Previous studies have shown significant correlations between Mg/Ca concentration ratios measured on biogenic carbonate of mollusk shells and sea surface temperature (SST). Here we propose the use of Laser-Induced Breakdown Spectroscopy (LIBS) and the validation of the Calibration-Free LIBS (CF-LIBS) approach for the rapid measurement and estimation of Mg/Ca molar concentration profiles within Patella depressa Pennant, 1777 limpet shells. To achieve these objectives, results derived from CF-LIBS methodology are compared with those obtained from an established analytical technique for this purpose, such as Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). Concentration series obtained with both methodologies show defined temporal patterns and reflect the season-of-capture in each specimen. The results evidence a significant correlation (R2 = 0.63–0.81) between CF-LIBS and LA-ICP-MS Mg/Ca molar concentration profiles within four live-collected P. depressa shells. Averaged error for the molar concentration estimated with CF-LIBS was lower than 10% in every specimen. The comparison between the results obtained from two techniques used in this study has allowed us to demonstrate for the first time that Mg/Ca molar concentration measured in biogenic carbonates were accurately inferred using CF-LIBS technique. The CF-LIBS approach validation represents great potential for the rapid and large-scale paleoenvironmental and archaeological analysis of this mollusk species, which is frequently found in archaeological sites.

Application of Laser-Induced Breakdown Spectroscopy and Chemometrics for the Quality Evaluation of Foods with Medicinal Properties: A Review.

Laser-induced Breakdown Spectroscopy (LIBS) is becoming an increasingly popular analytical technique for characterizing and identifying various products; its multi-element analysis, fast response, remote sensing, and sample preparation is minimal or nonexistent, and low running costs can significantly accelerate the analysis of foods with medicinal properties (FMPs). A comprehensive overview of recent advances in LIBS is presented, along with its future trends, viewpoints, and challenges. Besides reviewing its applications in both FMPs, it is intended to provide a concise description of the use of LIBS and chemometrics for the detection of FMPs, rather than a detailed description of the fundamentals of the technique, which others have already discussed. Finally, LIBS, like conventional approaches, has some limitations. However, it is a promising technique that may be employed as a routine analysis technique for FMPs when utilized effectively.

Comparative study on quantitative carbon content mapping in archaeological ferrous metals with laser-induced plasma spectroscopy (LIBS) and nuclear reaction analysis (NRA) for 3D representation by LIBS

The carbon content distribution of ferrous archaeological artefacts in a quantitative way allows us to understand the nature of the materials. Our previous works have ever proved that the use of laser-induced breakdown spectroscopy (LIBS) could be an efficient method to solve problems of in situ analysis and offer a high spatial resolution. To assess the ability of this quantification in an absolute way the carbon quantification by LIBS is compared in this paper with the nuclear reaction analysis (NRA), one of the ion beam analysis (IBA) techniques generated by a deuterium ion beam. The IBA analysis has specific advantages, especially in terms of precision and non-destructiveness. A validation study is also presented on quantitative carbon content mapping in archaeological ferrous metals with LIBS and deuterium ion beam analysis. The application of LIBS quantitative mapping on different structures of archaeological pieces shows that the carbon content distribution can be well determined, demonstrating the 2D mapping capabilities of the laser techniques. With the help of LIBS stratigraphic analysis, a 3D deeper insight on microscale features presents a heterogeneity, induced better understanding of material organization at the microscale.

Noninvasive diagnosis of type 2 diabetes mellitus by hair analysis using laser-induced breakdown spectroscopy (LIBS)

Currently, the reference method for diagnosing the pathology of diabetes is the measurement of glycated hemoglobin HbA1c. As a major public health problem, as categorized by the World Health Organization, the need to develop new techniques that are more accessible, especially for developing countries, is necessary. The objective of this work is to propose a new technique based on laser-induced breakdown spectroscopy (LIBS) using the essential minerals calcium, sodium, magnesium, and zinc in hair as biomarkers for the screening of type 2 diabetes. The study involved 130 women: 24 were classified as low-level diabetics with a glycated hemoglobin HbA1c <7% and 56 were classified as high-level diabetics with an HbA1c >7% after clinical tests were performed at the Habib Thameur Hospital in Tunis. The other 50 women were healthy and used as controls. The correlation of the mineral concentrations in the hair with the values of glycated hemoglobin allow an economical and rapid detection of the disease. The results showed a strong negative correlation between the levels of calcium, magnesium, and zinc with the HbA1c values and a strong positive correlation between the intensity of the sodium lines and HbA1c. These results allowed the discrimination of the three groups in the cohort, offering a new approach to quickly classify the patients involved in a screening campaign. In addition, the strong correlation between laser-induced breakdown spectroscopy (LIBS) and the front face fluorescence spectroscopy (FFFS), previously applied on the same samples, may help identify the source of the fluorescence spectra. All of these results support the use of LIBS upon hair to evaluate minerals as effective biomarkers for the detection of type II diabetes to be a promising approach.

Fast diagnostic of osteoporosis based on hair analysis using LIBS technique

Currently, the reference method for the diagnose of the osteoporosis pathology is the Dual energy X-ray Absorptiometry (DXA) technique which assesses Bone Mineral Density (BMD). As a major public health problem, classified as such by the World Health Organization, it is necessary to develop new techniques that are more accessible, especially for developing countries. The objective of this work is to propose a new technique based on laser-induced breakdown spectroscopy (LIBS) using calcium and sodium in hair as a biomarker for the detection of osteoporosis pathology. The study involved 90 women aged 50 to 70 years divided in three groups, thirty women with osteoporosis, thirty women with osteopenia and thirty healthy women. The application of LIBS technique on their hair to correlate calcium and sodium lines with T-scores allows a quick and cheap screening of the disease. The results showed strong correlations between calcium lines and disease. We noted a negative correlation between the intensity of calcium and sodium lines. In addition to these results, we show that we were able to cluster the three groups using the emission lines of these two elements. All these results support the idea that the use of LIBS applied to hair to evaluate calcium and sodium elements as effective biomarkers for the detection of osteoporosis is a promising approach.

A Review on Laser-Induced Breakdown Spectroscopy in Different Cancers Diagnosis and Classification

Due to the new demonstrations of Laser-induced breakdown spectroscopy (LIBS) applicability in a surprisingly wide variety of applications, the use of LIBS as a medical diagnostic tool is steadily gaining momentum. Especially in different cancer diseases, LIBS has the potential to become a fast and valuable analytical tool. We addressed LIBS equipment and quantitative analytical procedures, and signal enhancement techniques for improving element detection. For detailed aspects of applications, we reviewed the recent progress of LIBS in different cancer diseases diagnoses by using different tissues and medical fluid as samples. To fulfill the high demands in the medical industry and overcome the severe tissue sample problem, it is proposed that the chemometric and signal amplification techniques for quantitative analysis should be employed, and robust and effective LIBS devices should be developed. This overview of the different cancers by LIBS is meant to summarize the research performed to date and suggest some suitable advanced chemometrics techniques and effective LIBS devices, if successfully implemented, would be significantly beneficial to the medical field in the future.

Tire Classification by Elemental Signatures Using Laser-Induced Breakdown Spectroscopy.

Tire evidence is a form of trace evidence that is often overlooked in today's forensics, while frequently found at crime or accident scenes, usually in the form of skid marks. The pattern of the tire skid mark has been used before to link a tire or car to a scene, but the widespread use of anti-lock braking systems makes this an almost impossible and abandoned route of analysis. With this in mind, using the chemical profile of a tire has potential to link a car or tire back to a scene in which its trace material is found. This study shows the successful use of the elemental profile of tire rubber to classify 32 different samples using laser-induced breakdown spectroscopy, analyzed by principal component analysis combined with linear discriminant analysis. A classification accuracy close to 99% shows the ever-growing use of laser-induced breakdown spectroscopy as a technique of choice for forensic analysis of tire rubber, opening the path for its use as a forensic evidence.

Sulfur Detection in Soil by Laser Induced Breakdown Spectroscopy Assisted by Multivariate Analysis.

Laser-induced breakdown spectroscopy (LIBS) is used for the detection and determination of sulfur content in some organic soil samples. The most suitable sulfur spectral lines for such tasks were found to occur in the vacuum ultraviolet (VUV) spectral region and they were used for the construction of calibration curves. For the analysis, both univariate and multivariate statistical models were employed. The results obtained by the different analysis techniques are evaluated and compared. The present study demonstrates both the applicability and efficiency of LIBS for fast sulfur detection in soil matrices when aided by multivariate analysis methods improving the accuracy and extending the potential use of LIBS in such applications.

Analysis of lead-based archaeological pottery glazes by laser induced breakdown spectroscopy

The aim of this study was to demonstrate the potential of Laser-Induced Breakdown Spectroscopy (LIBS) in the analysis of pottery shards, which are commonly the most abundant artefacts recovered at archaeological sites. The analytical capability of LIBS in identification of elements characteristic to inorganic pigments used for decoration was assessed. Based on the specific elements detected in the LIBS spectra of glazes, correlations between the elemental composition of the glaze and the colours of the glazed surface were established. The results were in accordance with the previously reported Energy Dispersive X-ray Fluorescence (EDXRF) and Raman analyses of some of the samples, and with the presently performed XRF analysis. Two important parameters for plasma characterization, temperature and electron number density, were estimated from the spectral data. A potential use of LIBS for depth profiling was also investigated by analysing the silicon to aluminium intensity ratio as a function of the number of laser shots applied. Profilometry analysis of the damages created on the glazed surfaces by laser shots was used to estimate the average ablation depth and ablated mass.

Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning

Alzheimer's disease (AD) is a progressive incurable neurodegenerative disease and a major health problem in aging population. We show that the combined use of Laser-Induced Breakdown Spectroscopy (LIBS) and machine learning applied for the analysis of micro-drops of plasma samples of AD and healthy controls (HC) yields robust classification. Following the acquisition of LIBS spectra of 67 plasma samples from a cohort of 31 AD patients and 36 healthy controls (HC), we successfully diagnose late-onset AD (> 65 years old), with a total classification accuracy of 80%, a specificity of 75% and a sensitivity of 85%.

Discrimination of hazardous bacteria with combination laser-induced breakdown spectroscopy and statistical methods.

Real-time biohazard detectors must be developed to facilitate the rapid implementation of appropriate protective measures against foodborne pathogens. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the real-time detection of hazardous bacteria (HB) in the field. However, distinguishing among various HBs that exhibit similar C, N, O, H, or trace metal atomic emissions complicates HB detection by LIBS. This paper proposes the use of LIBS and chemometric tools to discriminate Staphylococcus aureus, Bacillus cereus, and Escherichia coli on slide substrates. Principal component analysis (PCA) and the genetic algorithm (GA) were used to select features and reduce the size of spectral data. Several models based on the artificial neural network (ANN) and the support vector machine (SVM) were built using the feature lines as input data. The proposed PCA-GA-ANN and PCA-GA-SVM discrimination approaches exhibited correct classification rates of 97.5% and 100%, respectively.

Echelle LIBS-Raman system: A versatile tool for mineralogical and archaeological applications

There are a number of analytical techniques used to study material related problems. Most of them imply experimental determination of parameters and functions by means of which elemental, optical and other properties of materials can be described. Laser Induced Breakdown Spectroscopy (LIBS) and Raman spectroscopy are highly complementary spectroscopic techniques used for lab, in-situ, and remote analyses of materials. The LIBS-Raman system provides further information compared to other conventional techniques since it can detect the presence of low atomic number-elements, isotopic composition, hydration and structure of trace materials which may be present as surface layers etc., which are very difficult, if not impossible, by portable X-ray based systems. A setup for LIBS and Raman spectroscopy measurements in a single unit has been developed and reported recently by us using an echelle spectrograph system. The system utilizes a single nanosecond pulsed Nd:YAG laser (532 nm) and an ICCD coupled echelle spectrograph for both measurements. The unit has been successfully used for multipurpose applications such as identification of minerals, pigments etc and also for checking quality assurance. The combined atomic and molecular information from the same location on a sample, at several locations, can provide more comprehensive information regarding its properties than using either of these quantities taken singly. In many cases, it has been observed that the high resolution of the echelle spectrograph provides better quality Raman signals by virtue of the small degeneracy/crystal field splitting of many fundamental Raman bands. This can provide valuable information on inclusions in bulk samples, changes due to symmetry alteration or complex formation with surroundings, hydrogen bonding etc. The advantage of echelle system to identify natural and artificial pigments by identifying the minor and trace components by the spectra of the elements therein, is also helpful in many situations. In the present paper, the orthogonal use of LIBS and Raman spectroscopy is assessed and highlighted. The results clearly demonstrate the potential of echelle-based LIBS-Raman system in applications where more detailed information on complex samples like minerals, archaeological artifacts etc, is required with minimum sample damage or consumption.