Simultaneous Application of Raman and Laser-Induced Breakdown Spectroscopy in the Gas Phase with a Single Laser and Detector

Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) are powerful tools for molecular and elemental analysis, respectively. Their combined application, however, is challenging due to the differences in the signal generation and detection characteristics. This note proposes three experimental schemes for the simultaneous application of Raman and LIBS for gas-phase diagnostics. Ring-cavity optical pulse stretchers facilitate shaping suitable pulse pairs from a Q-switched laser that enables the quasi-simultaneous detection of the Raman and LIBS signals on a single detector.

Laser-Induced Plasmas of Plutonium Dioxide in a Double-Walled Cell

Plutonium research has been stifled by the significant number of administrative controls and safety procedures, space and instrumentation limitations in radiological gloveboxes, and the potential for personnel and equipment contamination. To address the limited number of spectroscopic studies in Pu-bearing compounds in the current scientific literature, this work presents the use of double-walled cells (DWCs) in "clean" buildings/laboratories as an alternative to research in radiological gloveboxes. This study reports the first laser-induced breakdown spectroscopy (LIBS) experiments of a PuO2 pellet contained within a DWC, where the formation of elemental (atomic and ionic) species as well as the evolution from elemental to molecular products (PuxOy) was measured. Raman spectroscopy was also used to characterize the surface of the ablated pellet and the particulates deposited on the window of the inner cell. The full width half-maximum of the T2g band enabled us to obtain an estimate of the temperature at the pellet surface after the ablation pulse and the particulates based on the crystal lattice disorder. Particulates deposited on the window of the DWC during laser ablation were characterized using scanning electron microscopy, where molten irregular particulates and spheroids were observed. This exciting research conducted in a DWC describes our initial attempts to incorporate LIBS in the arsenal of spectroscopic tools for nuclear forensics applications.

Real-Time Monitoring of Thermal Phenomena during Femtosecond Ablation of Bone Tissue for Process Control

Femtosecond (fs) laser technology is currently being considered in innovative fields such as osteotomy and treatment of hard tissue thanks to the achievable high resolution and ability to prevent tissue damage. In a previous study, suitable process parameters were obtained to achieve competitive ablation rates on pork femur processing. Nevertheless, a better control of thermal accumulation in the tissue during laser ablation could further improve the postoperative regeneration of the treated bone compared with conventional procedures and push forward the exploitation of such technology. This study presents methods for real time analyses of bone tissue temperature and composition during fs laser ablation and highlights the importance of implementing an efficient cooling method of bone tissue in order to achieve optimized results. Results show that it is possible to achieve a larger process window for bone tissue ablation where bone tissue temperature remains within the protein denaturation temperature in water-based processing environment. This is a key outcome towards a clinical exploitation of the presented technology, where higher process throughputs are necessary. The effects of process parameters and environments on bone tissue were confirmed by LIBS technique, which proved to be an efficient method by which to record real-time variation of bone tissue composition during laser irradiation.

Machine Learning-Assisted Determination of C6H14 Mole Fraction From Molecular Emissions of Laser-Induced Hexane-Air Plasmas

Laser-induced plasmas of materials containing hydrocarbons present strong carbon molecular emission features. Using these emissions to build models relating changes in spectral features to a physical parameter of the system, such as hydrocarbon content, can be difficult because of the dynamic complexity of the spectral features and temperature disequilibrium between molecular species. This study presents machine learning models trained to quantify the mole fraction of hexane in hexane-air plasmas from CN Violet and C2 Swan spectral features. Ensemble regression methods provide the most accurate predictions with root mean squared error on the order 10-2. Artificial neural network regressions produce predictions with superlative sensitivity, exhibiting detection limits as low as 0.008. These foundational models can be further refined with more advanced data to quantify the presence of carbon species in complex plasma environments, such as high-speed reacting flows.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Laser Ablation Plasmas and Spectroscopy for Nuclear Applications

The development of measurement methodologies to detect and monitor nuclear-relevant materials remains a consistent and significant interest across the nuclear energy, nonproliferation, safeguards, and forensics communities. Optical spectroscopy of laser-produced plasmas is becoming an increasingly popular diagnostic technique to measure radiological and nuclear materials in the field without sample preparation, where current capabilities encompass the standoff, isotopically resolved and phase-identifiable (e.g., UO and UO2 ) detection of elements across the periodic table. These methods rely on the process of laser ablation (LA), where a high-powered pulsed laser is used to excite a sample (solid, liquid, or gas) into a luminous microplasma that rapidly undergoes de-excitation through the emission of electromagnetic radiation, which serves as a spectroscopic fingerprint for that sample. This review focuses on LA plasmas and spectroscopy for nuclear applications, covering topics from the wide-area environmental sampling and atmospheric sensing of radionuclides to recent implementations of multivariate machine learning methods that work to enable the real-time analysis of spectrochemical measurements with an emphasis on fundamental research and development activities over the past two decades. Background on the physical breakdown mechanisms and interactions of matter with nanosecond and ultrafast laser pulses that lead to the generation of laser-produced microplasmas is provided, followed by a description of the transient spatiotemporal plasma conditions that control the behavior of spectroscopic signatures recorded by analytical methods in atomic and molecular spectroscopy. High-temperature chemical and thermodynamic processes governing reactive LA plasmas are also examined alongside investigations into the condensation pathways of the plasma, which are believed to serve as chemical surrogates for fallout particles formed in nuclear fireballs. Laser-supported absorption waves and laser-induced shockwaves that accompany LA plasmas are also discussed, which could provide insights into atmospheric ionization phenomena from strong shocks following nuclear detonations. Furthermore, the standoff detection of trace radioactive aerosols and fission gases is reviewed in the context of monitoring atmospheric radiation plumes and off-gas streams of molten salt reactors. Finally, concluding remarks will present future outlooks on the role of LA plasma spectroscopy in the nuclear community.

Microstructure Formations Resulting from Nanosecond and Picosecond Laser Irradiation of a Ti-Based Alloy under Controlled Atmospheric Conditions and Optimization of the Irradiation Process

This paper presents a study and comparison of surface effects induced by picosecond and nanosecond laser modification of a Ti6Al4V alloy surface under different ambient conditions: air and argon- and nitrogen-rich atmospheres. Detailed surface characterization was performed for all experimental conditions. Damage threshold fluences for picosecond and nanosecond laser irradiation in all three ambient conditions were determined. The observed surface features were a resolidified pool of molten material, craters, hydrodynamic effects and parallel periodic surface structures. Laser-induced periodic surface structures are formed by multi-mode-beam nanosecond laser action and picosecond laser action. Crown-like structures at crater rims are specific features for picosecond Nd:YAG laser action in argon-rich ambient conditions. Elemental analysis of the surfaces indicated nitride compound formation only in the nitrogen-rich ambient conditions. The constituents of the formed plasma were also investigated. Exploring the impact of process control parameters on output responses has been undertaken within the context of laser modification under different environmental conditions. Parametric optimization of the nanosecond laser modification was carried out by implementing an advanced method based on Taguchi's parametric design and multivariate statistical techniques, and optimal settings are proposed for each atmosphere.

Rapid Authentication and Detection of Olive Oil Adulteration Using Laser-Induced Breakdown Spectroscopy

The adulteration of olive oil is a crucial matter for food safety authorities, global organizations, and consumers. To guarantee olive oil authenticity, the European Union (EU) has promoted the labeling of olive oils with the indices of Protected Designation of Origin (PDO) and Protected Geographical Identification (PGI), while food security agencies are also interested in newly emerging technologies capable of operating reliably, fast, and in real-time, either in situ or remotely, for quality control. Among the proposed methods, photonic technologies appear to be suitable and promising for dealing with this issue. In this regard, a laser-based technique, namely, Laser-Induced Breakdown Spectroscopy (LIBS), assisted via machine learning tools, is proposed for the real-time detection of olive oil adulteration with lower-quality oils (i.e., pomace, soybean, sunflower, and corn oils). The results of the present work demonstrate the high efficiency and potential of the LIBS technique for the rapid detection of olive oil adulteration and the detection of adulterants.

Multimodal feedback systems for smart laser osteotomy: Depth control and tissue differentiation

OBJECTIVES

The study aimed to improve the safety and accuracy of laser osteotomy (bone surgery) by integrating optical feedback systems with an Er:YAG laser. Optical feedback consists of a real-time visual feedback system that monitors and controls the depth of laser-induced cuts and a tissue sensor differentiating tissue types based on their chemical composition. The developed multimodal feedback systems demonstrated the potential to enhance the safety and accuracy of laser surgery.

MATERIALS AND METHODS

The proposed method utilizes a laser-induced breakdown spectroscopy (LIBS) system and long-range Bessel-like beam optical coherence tomography (OCT) for tissue-specific laser surgery. The LIBS system detects tissue types by analyzing the plasma generated on the tissue by a nanosecond Nd:YAG laser, while OCT provides real-time monitoring and control of the laser-induced cut depth. The OCT system operates at a wavelength of 1288 ± 30 nm and has an A-scan rate of 104.17 kHz, enabling accurate depth control. Optical shutters are used to facilitate the integration of these multimodal feedback systems.

RESULTS

The proposed system was tested on five specimens of pig femur bone to evaluate its functionality. Tissue differentiation and visual depth feedback were used to achieve high precision both on the surface and in-depth. The results showed successful real-time tissue differentiation and visualization without any visible thermal damage or carbonization. The accuracy of the tissue differentiation was evaluated, with a mean absolute error of 330.4 μm and a standard deviation of ±248.9 μm, indicating that bone ablation was typically stopped before reaching the bone marrow. The depth control of the laser cut had a mean accuracy of 65.9 μm with a standard deviation of ±45 μm, demonstrating the system's ability to achieve the pre-planned cutting depth.

CONCLUSION

The integrated approach of combining an ablative laser, visual feedback (OCT), and tissue sensor (LIBS) has significant potential for enhancing minimally invasive surgery and warrants further investigation and development.

A Simple Laser-Induced Breakdown Spectroscopy Method for Quantification and Classification of Edible Sea Salts Assisted by Surface-Hydrophilicity-Enhanced Silicon Wafer Substrates

Salt, one of the most commonly consumed food additives worldwide, is produced in many countries. The chemical composition of edible salts is essential information for quality assessment and origin distinction. In this work, a simple laser-induced breakdown spectroscopy instrument was assembled with a diode-pumped solid-state laser and a miniature spectrometer. Its performances in analyzing Mg and Ca in six popular edible sea salts consumed in South Korea and classification of the products were investigated. Each salt was dissolved in water and a tiny amount of the solution was dropped and dried on the hydrophilicity-enhanced silicon wafer substrate, providing homogeneous distribution of salt crystals. Strong Mg II and Ca II emissions were chosen for both quantification and classification. Calibration curves could be constructed with limits-of-detection of 87 mg/kg for Mg and 45 mg/kg for Ca. Also, the Mg II and Ca II emission peak intensities were used in a k-nearest neighbors model providing 98.6% classification accuracy. In both quantification and classification, intensity normalization using a Na I emission line as a reference signal was effective. A concept of interclass distance was introduced, and the increase in the classification accuracy due to the intensity normalization was rationalized based on it. Our methodology will be useful for analyzing major mineral nutrients in various food materials in liquid phase or soluble in water, including salts.

Characterization of Functional Coatings on Cork Stoppers with Laser-Induced Breakdown Spectroscopy Imaging

Evaluating the efficiency of surface treatments is a problem of paramount importance for the cork stopper industry. Generically, these treatments create coatings that aim to enhance the impermeability and lubrification of cork stoppers. Yet, current methods of surface analysis are typically time-consuming, destructive, have poor representativity or rely on indirect approaches. In this work, the use of a laser-induced breakdown spectroscopy (LIBS) imaging solution is explored for evaluating the presence of coating along the cylindrical surface and in depth. To test it, several cork stoppers with different shaped areas of untreated surface were analyzed by LIBS, making a rectangular grid of spots with multiple shots per spot, to try to identify the correspondent shape. Results show that this technique can detect the untreated area along with other features, such as leakage and holes, allowing for a high success rate of identification and for its performance at different depths, paving the way for future industry-grade quality control solutions with more complex surface analysis.

Application of Laser-Induced Breakdown Spectroscopy for Depth Profiling of Multilayer and Graded Materials

Laser-induced breakdown spectroscopy (LIBS) has emerged as a powerful analytical method for the elemental mapping and depth profiling of many materials. This review offers insight into the contemporary applications of LIBS for the depth profiling of materials whose elemental composition changes either abruptly (multilayered materials) or continuously (functionally graded or corroded materials). The spectrum of materials is discussed, spanning from laboratory-synthesized model materials to real-world products including materials for fusion reactors, photovoltaic cells, ceramic and galvanic coatings, lithium batteries, historical and archaeological artifacts, and polymeric materials. The nuances of ablation conditions and the resulting crater morphologies, which are instrumental in depth-related studies, are discussed in detail. The challenges of calibration and quantitative profiling using LIBS are also addressed. Finally, the possible directions of the evolution of LIBS applications are commented on.

Detection of Off-Gassed Products From Molten Salts Using Laser-Induced Breakdown Spectroscopy

The detection of off-gassed sodium from molten sodium nitrate (NaNO3) at temperatures between 330 °C and 505 °C and off-gassed calcium from molten lithium chloride-potassium chloride eutectic (LKE) mixtures at 510 °C with laser-induced breakdown spectroscopy (LIBS) was demonstrated. NaNO3 and LKE samples were melted in a custom-built crucible that promoted the generation of off-gassed products from the molten sample. The off-gassed products were analyzed with a LIBS system designed to probe the high-temperature environment. Na D emission lines, Na(I)588.99 nm and Na(I) 589.59 nm, were detected from the NaNO3 samples after reaching a temperature threshold, which indicated the occurrence of phase change. In LKE mixtures, the detection of Ca impurities at a concentration of 78 mg/kg was possible using the emission lines Ca(II) 393.66 nm and Ca(II) 395.85 nm. This work demonstrates the real-time monitoring capabilities of LIBS in high-temperature environments that simulate the conditions of molten salt reactors.

Material Classification and Aging Time Prediction of Structural Metals Using Laser-Induced Breakdown Spectroscopy Combined with Probabilistic Neural Network

In this paper, laser-induced breakdown spectroscopy (LIBS) combined with a probabilistic neural network (PNN) was applied to classify engineering structural metal samples (valve stem, welding material, and base metal). Additionally, utilizing data from the plasma emission spectrum generated by laser ablation of samples with different aging times, an aging time prediction model based on a firefly optimized probabilistic neural network (FA-PNN) was established, which can effectively evaluate the service performance of structural materials. The problem of insufficient features obtained by principal component analysis (PCA) for predicting the aging time of materials is addressed by the proposal of a time-frequency feature extraction method based on short-time Fourier transform (STFT). The classification accuracy (ACC) of time-frequency features and principal component features was compared under PNN. The results indicate that, in comparison to the PCA feature extraction approach, the time-frequency feature extraction method based on STFT demonstrates higher accuracy in predicting the time of aging materials. Then, the relationship between classification accuracy (ACC) and settings of PNN was discussed. The ACC of the PNN model for both the material classification test set and the aging time test set achieved 100% with Firefly (FA) optimization algorithms. This result was also compared with the ACC of ANN, KNN, PLS-DA, and SIMCA for the aging time test set (95%, 87.5%, 85%, and 62.5%, respectively). The experimental results demonstrated that the classification model using LIBS combined with FA-PNN could realize better classification accuracy.

Characterization and Optimization of a Spectral Window for Direct Gaseous Uranium Hexafluoride Enrichment Assay Using Laser-Induced Breakdown Spectroscopy

Through a systematic scanning of 235U and 238U emission lines between 280 nm and 745 nm, the optimal emission line for direct gaseous uranium hexafluoride (UF6) enrichment assay using laser-induced breakdown spectroscopy (LIBS) was found. Screening for spectral features that are potentially useful for U isotopic analysis was gauged from the magnitude of the 235U-238U isotopic shift and the signal-to-background ratio of the emission line through a parameter termed ΔSBR 235U-238U. The ΔSBR spectrum shows peaks at wavelength positions where there are strong lines with significant 235U-238U shifts. The screening identified 13 spectral-window candidates, which were down selected based on their overall accuracy in predicting the 235U enrichment of three UF6 samples of natural (0.720 atom% 235U) and low-enriched (4.675 atom% and 9.157 atom% 235U) grades. The U(I) 646.498 nm emission line, with a determined 235U-238U isotopic shift of -17.7 pm, was found to be the optimal spectral window for direct UF6 enrichment assay. The root mean square error for enrichment assays on the three natural and low-enriched UF6 samples, with each sample measured in six replicates, was 0.31% in absolute 235U content. Each measurement comprised LIBS signals accumulated from 3000 laser shots. The analytical bias and precision were better than 0.5% and 0.3%, respectively, in absolute [235U/(235U + 238U)] ratios. Specific for the two low-enriched UF6 samples, the relative standard deviations from six replicated measurements were around 2%.

Multi-Spectroscopic Characterization of MgO/Nylon (6/6) Polymer: Evaluating the Potential of LIBS and Statistical Methods

The potential of using laser-induced breakdown spectroscopy (LIBS) in combination with various other spectroscopic and statistical methods was assessed for characterizing pure and MgO-doped nylon (6/6) organic polymer samples. The pure samples, obtained through a polycondensation chemical technique, were artificially doped with MgO prior to analysis for comparative purposes. These artificially doped samples served as crucial reference materials for comparative analysis and reference purposes. The LIBS studies were performed under local thermodynamic equilibrium (LTE) and optically thin plasma conditions. To assess the structural crystallinity of the nylon (6/6) polymer samples, X-ray diffraction (XRD) analysis, and Fourier transform infrared (FTIR) spectroscopy were employed to detect functional groups such as N-H, C-H, and C-N in the adsorbent polyamide nylon sample. Additionally, diffuse reflectance spectroscopy (DRS) analysis was conducted to investigate the effects of doping and temperature on the band gap and material reflectance across different sample temperatures. Chemical compositional analysis was performed using X-ray photoelectron spectroscopy (XPS) with the carbon C1s peak at 248.8 eV serving as a reference for spectrum calibration, along with energy-dispersive X-ray (EDX) analysis, which demonstrated good agreement between the techniques. To validate the different methodologies, the results obtained from CF-LIBS and EDX were compared with those from the standard inductively coupled plasma mass spectrometry (ICP-MS) technique. Finally, for classification analysis, principal component analysis (PCA) was applied to the LIBS spectral data at different sample temperatures (25 °C, 125 °C, 225 °C, and 325 °C). The analyses demonstrated that the combination of LIBS with PCA, along with other methods, presents a robust technique for polymer characterization.

Construction, Spectral Modeling, Parameter Inversion-Based Calibration, and Application of an Echelle Spectrometer

We have developed a compact, asymmetric three-channel echelle spectrometer with remarkable high-spectral resolution capabilities. In order to achieve the desired spectral resolution, we initially establish a theoretical spectral model based on the two-dimensional coordinates of spot positions corresponding to each wavelength. Next, we present an innovative and refined method for precisely calibrating echelle spectrometers through parameter inversion. Our analysis delves into the complexities of the nonlinear two-dimensional echelle spectrogram. We employ a variety of optimization techniques, such as grid exploration, simulated annealing, genetic algorithms, and genetic simulated annealing (GSA) algorithms, to accurately invert spectrogram parameters. Our proposed GSA algorithm synergistically integrates the strengths of global and local searches, thereby enhancing calibration accuracy. Compared to the conventional grid exploration method, GSA reduces the error function by 22.8%, convergence time by 2.16 times, and calibration accuracy by 7.05 times. Experimental validation involves calibrating a low-pressure mercury lamp, resulting in an average spectral accuracy error of 0.0257 nm after performing crucial parameter inversion. Furthermore, the echelle spectrometer undergoes a laser-induced breakdown spectroscopy experiment, demonstrating exceptional spectral resolution and sub-10 ns time-resolved capability. Overall, our research offers a comprehensive and efficient solution for constructing, modeling, calibrating, and applying echelle spectrometers, significantly enhancing calibration accuracy and efficiency. This work contributes to the advancement of spectrometry and opens up new possibilities for high-resolution spectral analysis across various research and industry domains.

Feasibility of a Low-Power, Low-Resolution Laser-Induced Breakdown Spectroscopy Instrument for Analysis of Nickel Alloys: Quantification of the Major Alloying Elements and Classification

A simple cost-effective laser-induced breakdown spectroscopy (LIBS) instrument was used for quantification of major elements in several nickel alloys and also sorting them. A compact low-power diode-pumped solid-state laser and a miniature low-resolution spectrometer were assembled for the LIBS instrument. Material properties of the nickel alloys depend mainly on the composition of the major elements, Ni, Cr, and Fe, ranging from a few to ∼60 wt%. The emission peaks at 547.7 nm, 520.4 nm, and 438.1 nm for Ni, Cr, and Fe, respectively, were chosen for this analysis. The analytical performance was found to be enough for the quantification of Ni, Cr, and Fe in the nickel alloys. Limits of detection and accuracy were estimated to be a few weight percent (wt%) and measurement precisions were less than 10% in terms of relative standard deviation. The calibration performance of this intensity-based method was compared with that of the "ratio method" which is used in conventional optical emission spectroscopy analyses. The comparison indicates that the intensity-based method is more appropriate with the low-performance LIBS instrument that detects emission peaks of only a few major elements. Also, multivariate modeling of the six different nickel alloy samples based on the emission peak intensities of Ni, Cr, and Fe was performed using k-nearest neighbors (KNN) and linear discriminant analysis (LDA). The KNN and ordinary LDA models showed 95.0% and 98.3% classification correctness for the separate test data set, respectively. To improve classification performance further, the two-step LDA model was trained. In this approach, the two closest sample classes responsible for the decrease in the classification correctness were separately modeled in the second step to exploit their difference effectively. The two-step LDA model showed 100% correctness in classifying the test objects. Our results indicate that such a low-performance LIBS instrument can be effectively utilized for quantitative analysis of the major elements in the nickel alloys and their rapid identification or sorting in combination with an appropriate multivariate modeling algorithm.

In-Situ Elemental Composition Analysis of Large Inhalable Aerosol Using Laser Induced Breakdown Spectroscopy

The ability to obtain information on the composition of airborne particles is a necessary part of identifying and controlling risks from exposure to potentially toxic materials, especially in the workplace. However, very few aerosol sampling instruments can characterize elemental composition in real time or measure large inhalable particles with aerodynamic diameter exceeding 20 µm. Here, we present the development and validation of a method for real time elemental composition analysis of large inhalable particles using laser-induced breakdown spectroscopy (LIBS). The prototype sensor uses a passive inlet and an optical triggering system to ablate falling particles with an LIBS plasma. Particle composition is quantified based on collected emission spectra using a real-time material classification algorithm. The approach was validated with a set of 1480 experimental spectra from four different aerosol test materials. We have studied effects of varying detection thresholds and find operating conditions with good agreement to truth values (F₁ score ≥ 0.9). Details of the analysis method, including subtracting the spectral contribution from the air plasma and reasons for the infrequent misclassifications, are discussed. The LIBS elemental analysis can be combined with our previously demonstrated direct-reading particle sizer (DRPS) to provide a system capable of both counting, sizing, and elemental analysis of large inhalable particles.

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis

A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.

A Versatile Method for Quantitative Analysis of Total Iron Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy

Focus in quality assessment of iron ore is the content of total iron (TFe). Laser-induced breakdown spectroscopy (LIBS) technology possesses the merits of rapid, in situ, real-time multielement analysis for iron ore, but its application to quantitative TFe content is subject to interference of the iron matrix effect and the lack of suitable data mining tools. Here, a new method of LIBS-based variable importance back propagation artificial neural network (VI-BP-ANN) for quantitative TFe content in iron ore was first proposed. After the LIBS spectra of 80 representative iron samples were obtained, random forest (RF) was optimized by out-of-bag (OOB) error and then used to measure and rank variable importance. The variable importance thresholds and the number of neurons were optimized with five-fold cross-validation (CV) with correlation coefficient (R²) and root mean square error (RMSE). With using only 1.40% of full spectral variables to construct BP-ANN model, the resulted R², the root mean squared error of prediction (RMSEP) and the modeling time of the final VI-BP-ANN model was 0.9450, 0.3174 wt%, and 24 s, respectively. Compared with full spectrum-based model, for example, BP-ANN, RF, support vector machine (SVM), and PLS and VI-RF model, the VI-BP-ANN model reduced overfitting and obtained the highest R² and the lowest RMSE both for calibration and prediction. Meanwhile, the characteristics of variables selected by VI were analyzed. In addition to the elemental emission lines of Ca, Al, Na, K, Mn, Si, Mg, Ti, Zr, and Li, partial spectral baselines of 540-610 nm and 820-970 nm were also selected as characteristic variables, which indicated that VI can take into full consideration the elemental interactions and the spectral baselines. Our approach shows that LIBS combined with VI-BP-ANN is able to quantify TFe content rapidly and accurately in iron ore.

Comparative Long-Wave Infrared Laser-Induced Breakdown Spectroscopy Employing 1-D and 2-D Focal Plane Array Detectors

Long-wave infrared (LWIR) emissions of laser-induced plasma on solid potassium chloride and acetaminophen tablet surfaces were studied using both a one-dimensional (1-D) linear array detection system and, for the first time, a two-dimensional (2-D) focal plane array (FPA) detection system. Both atomic and molecular infrared emitters in the vicinity of the plasma were identified by analyzing the detected spectral signatures in the infrared region. Time- and space-resolved long-wave infrared emissions were also studied to assess the temporal and spatial behaviors of atomic and molecular emitters in the plasma. These pioneer temporal and spatial investigations of infrared emissions from laser-induced plasma would be valuable to the modeling of plasma evolutions and the advances of the novel LWIR laser-induced breakdown spectroscopy (LIBS). When integrated both temporally (≥200 µs) and spatially using a 2-D FPA detector, the observed intensities and signal-to-noise-ratio (SNR) of single-shot LWIR LIBS signature emissions from intact molecules were considerably enhanced (e.g., with enhancement factors up to 16 and 3.76, respectively, for a 6.62 µm band of acetaminophen molecules) and, in general, comparable to those from the atomic emitters. Pairing LWIR LIBS with conventional ultraviolet-visible-near infrared (UV/Vis/NIR) LIBS, a simultaneous UV/Vis/NIR + LWIR LIBS detection system promises unprecedented capability of in situ, real-time, and stand-off investigation of both atomic and molecular target compositions to detect and characterize a range of chemistries.

Convolution Neural Network with Laser-Induced Breakdown Spectroscopy as a Monitoring Tool for Laser Cleaning Process

In this study, eight different painted stainless steel 304L specimens were laser-cleaned using different process parameters, such as laser power, scan speed, and the number of repetitions. Laser-induced breakdown spectroscopy (LIBS) was adopted as the monitoring tool for laser cleaning. Identification of LIBS spectra with similar chemical compositions is challenging. A convolutional neural network (CNN)-based deep learning method was developed for accurate and rapid analysis of LIBS spectra. By applying the LIBS-coupled CNN method, the classification CNN model accuracy of laser-cleaned specimens was 94.55%. Moreover, the LIBS spectrum analysis time was 0.09 s. The results verified the possibility of using the LIBS-coupled CNN method as an in-line tool for the laser cleaning process.

VOILA on the LUVMI-X Rover: Laser-Induced Breakdown Spectroscopy for the Detection of Volatiles at the Lunar South Pole

The project Lunar Volatiles Mobile Instrumentation-Extended (LUVMI-X) developed an initial system design as well as payload and mobility breadboards for a small, lightweight rover dedicated for in situ exploration of the lunar south pole. One of the proposed payloads is the Volatiles Identification by Laser Analysis instrument (VOILA), which uses laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of the lunar surface with an emphasis on sampling regolith and the detection of hydrogen for the inference of the presence of water. It is designed to analyze targets in front of the rover at variable focus between 300 mm and 500 mm. The spectrometer covers the wavelength range from 350 nm to 790 nm, which includes the hydrogen line at 656.3 nm as well as spectral lines of most major rock-forming elements. We report here the scientific input that fed into the concept and design of the VOILA instrument configuration for the LUVMI-X rover. Moreover, we present the measurements performed with the breadboard laboratory setup for VOILA at DLR Berlin that focused on verifying the performance of the designed LIBS instrument in particular for the detection and quantification of hydrogen and other major rock forming elements in the context of in situ lunar surface analysis.

Homogeneity Measurements of Li-Ion Battery Cathodes Using Laser-Induced Breakdown Spectroscopy

We study the capability of nanosecond laser-induced breakdown spectroscopy (ns-LIBS) for depth-resolved concentration measurements of Li-Ion battery cathodes. With our system, which is optimized for quality control applications in the production line, we pursue the goal to unveil manufacturing faults and irregularities during the production process of cathodes as early as possible. Femtosecond laser-induced breakdown spectroscopy (fs-LIBS) is widely considered to be better suited for depth-resolved element analysis. Nevertheless, the small size and intensity of the plasma plume, non-thermal energy distribution in the plasma and high investment costs of fs-LIBS make ns-LIBS more attractive for inline application in the industrial surrounding. The system, presented here for the first time, is able to record quasi-depth-resolved relative concentration profiles for carbon, nickel, manganese, cobalt, lithium and aluminum which are the typical elements used in the binder/conductive additive, the active cathode material and the current collector. LIBS often causes high variations in signal intensity from pulse to pulse, so concentration determination is, in general, conducted on the average of many pulses. We show that the spot-to-spot variations we measure are governed by the microstructure of the cathode foil and are not an expression of the limited precision of the LIBS setup.

Intrinsic Performance of Monte Carlo Calibration-Free Algorithm for Laser-Induced Breakdown Spectroscopy

The performance of the Monte Carlo (MC) algorithm for calibration-free LIBS was studied on the example of a simulated spectrum that mimics a metallurgical slag sample. The underlying model is that of a uniform, isothermal, and stationary plasma in local thermodynamical equilibrium. Based on the model, the algorithm generates from hundreds of thousands to several millions of simultaneous configurations of plasma parameters and the corresponding number of spectra. The parameters are temperature, plasma size, and concentrations of species. They are iterated until a cost function, which indicates a difference between synthetic and simulated slag spectra, reaches its minimum. After finding the minimum, the concentrations of species are read from the model and compared to the certified values. The algorithm is parallelized on a graphical processing unit (GPU) to reduce computational time. The minimization of the cost function takes several minutes on the GPU NVIDIA Tesla K40 card and depends on the number of elements to be iterated. The intrinsic accuracy of the MC calibration-free method is found to be around 1% for the eight elements tested. For a real experimental spectrum, however, the efficiency may turn out to be worse due to the idealistic nature of the model, as well as incorrectly chosen experimental conditions. Factors influencing the performance of the method are discussed.

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 (Fe_1-x S) 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.

Rapid Identification of Beached Marine Plastics Pellets Using Laser-Induced Breakdown Spectroscopy: A Promising Tool for the Quantification of Coastal Pollution

The rapid identification of beached marine micro-plastics is essential for the determination of the source of pollution and for planning the most effective strategies for remediation. In this paper, we present the results obtained by applying the laser-induced breakdown spectroscopy (LIBS) technique on a large sample of different kinds of plastics that can be found in a marine environment. The use of chemometric analytical tools allowed a rapid classification of the pellets with an accuracy greater than 80%. The LIBS spectrum and statistical tests proved their worth to quickly identify polymers, and in particular, to distinguish C-O from C-C backbone pellets, and PE from PP ones. In addition, the PCA analysis revealed a correlation between appearance (surface pellets roughness) and color (yellowing), as reported by other recent studies. The preliminary results on the analysis of metals accumulated on the surface of the pellets are also reported. The implication of these results is discussed in view of the possibility of frequent monitoring of the marine plastic pollution on the seacoast.

Two-Step Partial Least Squares-Discriminant Analysis Modeling for Accurate Classification of Edible Sea Salt Products Using Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been widely applied to material classification in various fields, and partial least squares-discriminant analysis (PLS-DA) is one of the frequently used classical multivariate statistics to construct classification models based on the LIBS spectra. However, classification accuracy of the PLS-DA model is sensitive to the number of classes and their similarities. Considering this characteristic of PLS-DA, we suggest a two-step PLS-DA modeling approach to improve the classification accuracy. This strategy was demonstrated for a six-class problem in which six commercial edible sea salts produced in Japan, South Korea, and France are classified using their LIBS spectra. At the first step, test spectra were sorted into four classes and one extended class, composed of the two other most confusing classes, and then the test spectra in the extended class were further classified into each of the two constituent classes which were modeled separately from the other four classes. This two-step classification has been found to remarkably improve the PLS-DA classification accuracy by maximizing the difference between the confusing classes in the second-step modeling.

Comparison of Convolutional and Conventional Artificial Neural Networks for Laser-Induced Breakdown Spectroscopy Quantitative Analysis

The introduction of "deep learning" algorithms for feature identification in digital imaging has paved the way for artificial intelligence applications that up to a decade ago were considered technologically impossible to achieve, from the development of driverless vehicles to the fully automated diagnostics of cancer and other diseases from histological images. The success of deep learning applications has, in turn, attracted the attention of several researchers for the possible use of these methods in chemometrics, applied to the analysis of complex phenomena as, for example, the optical emission of laser-induced plasmas. In this paper, we will discuss the advantages and disadvantages of convolutional neural networks, one of the most diffused deep learning techniques, in laser-induced breakdown spectroscopy (LIBS) applications (classification and quantitative analysis), to understand the real potential of "deep LIBS" in practical everyday use. In particular, the comparison with the results obtained using "shallow" artificial neural networks will be presented and discussed, taking as a case study the analysis of six bronze samples of known composition.

Dependence on the Lens-to-Target Distance and with the Laser Energy at Constant Irradiance of the Laser-Induced Breakdown Spectroscopy Signal

The emission signal-to-noise ratio (S/N) of a laser-produced plasma on an aluminum target at different focusing distances and at fixed irradiances was investigated. The plasma was produced by a 1064 nm nanosecond-pulsed laser and the energy and irradiances were varied in the 6-110 mJ and 0.4-700 GW cm^-2 ranges, respectively. Regardless of the applied laser energy, adjusting the lens-to-target distance, best emission values were obtained for an irradiance of nearly 8 GW cm^-2. At lower irradiances, the signal decreases due to less matter removal, while at higher values, the plasma shielding effect prevents the laser from reaching the sample. This mechanism is surpassed when the lens-to-sample distance is close to the nominal focusing value at about 100 GW cm^-2. The enhancement of the signal with the focusing distance is due to a combination of an increment of the plasma temperature, electron density, and atomized mass. When the irradiance is kept fixed changing simultaneously the laser energy and the ablated area, an increment of the emission was observed. This is basically due to an increment of the ablated mass while both electron density and temperature do not show significant changes, even though the laser energy increased by more than one order of magnitude.

Pressure Effects on Simultaneous Optical and Acoustics Data from Laser-Induced Plasmas in Air: Implications to the Differentiation of Geological Materials

The shockwave generated alongside the plasma is an intimately linked, yet often neglected additional input for the characterization of solid samples by laser-induced breakdown spectroscopy (LIBS). The present work introduces a dual LIBS-acoustics sensor that takes advantage of the analysis of the acoustic spectrum yielded by shockwaves produced on different geological samples to enhance the discrimination power of LIBS in materials featuring similar optical emission spectra. Six iron-based minerals were tested at a distance of 2 m using 1064 nm laser light and under pressure values ranging from 7 to 1015 mbar. These experimental parameters were selected to assess the effects of pressure, one of the main factors conditioning the propagation of sound as well as a commonly investigated influence in LIBS experiments. Moreover, precise values for carrying out the analyses were set based on one of the most exciting scenarios in which LIBS data may be enhanced by laser-induced acoustics: space exploration. This is exemplified by the tasks performed by the Mars 2020 SuperCam instrument located onboard the Perseverance rover. Authors evaluated the use of acoustic signals both in the time-domain and frequency-domain in sensitive cases for the distinguishing of minerals exhibiting LIBS spectra featuring almost the same emission lines using PCA schemes for each pressure setting. Thus, we report herein the impact of the surrounding pressure level upon this diagnostic tool. Overall, this paper seeks to show how the analytical potential of simultaneous phenomena taking place during a laser-produced plasma event is subjected to the defined operational conditions.

Application of Laser-Induced Breakdown Spectroscopy in the Quantitative Analysis of Elements-K, Na, Ca, and Mg in Liquid Solutions

Results of laser-induced breakdown spectroscopy measurements of K, Na, Ca, and Mg content in liquid media are discussed in the paper. Calibration results show correct parameters—linearity and R² coefficients of determination at the levels of 0.94–0.99. Obtained regression equations have been used to determine K, Na, Ca, and Mg concentrations in biological samples with known element content. Measurement results showed acceptable, within the expanded standard uncertainty, conformity with their content in the certified materials. Results have been supported by multivariate factorial analysis, which was especially effective for Ca and Mg samples. For these elements, factorial analysis allows the application of the whole spectra to obtain quantitative data on the tested samples, in contrast to a common method based on the selection of a particular spectral line for the calibration.

Accurate Identification and Quantification of Chinese Yam Powder Adulteration Using Laser-Induced Breakdown Spectroscopy

As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration. Pure powders (CY, rhizome of winged yam (RY) and cassava (CS)) and adulterated powders (CY adulterated with CS) were pressed into pellets to obtain LIBS spectra for identification and quantification experiments, respectively. After variable number optimization by principal component analysis and random forest (RF), the best model random forest-support vector machine (RF-SVM) decreased 48.57% of the input variables and improved the accuracy to 100% in identification. Following the better feature extraction method RF, the Gaussian process regression (GPR) method performed the best in the prediction of the adulteration rate, with a correlation coefficient of prediction (R_p²) of 0.9570 and a root-mean-square error of prediction (RMSEP) of 7.6243%. Besides, the variable importance of metal elements analyzed by RF revealed that Na and K were significant due to the high metabolic activity and maximum metal content of CY powder, respectively. These results demonstrated that chemometric methods combined with LIBS can identify and quantify CY powder adulteration accurately.

Sensing of Soil Organic Matter Using Laser-Induced Breakdown Spectroscopy Coupled with Optimized Self-Adaptive Calibration Strategy

Rapid quantification of soil organic matter (SOM) is a great challenge for the health assessment and fertility management of agricultural soil. Laser-induced breakdown spectroscopy (LIBS) with appropriate modeling algorithms is an alternative tool for this measurement. However, the current calibration strategy limits the prediction performance of the LIBS technique. In this study, 563 soil samples from Hetao Irrigation District in China were collected; the LIBS spectra of the soils were recorded in the wavenumber range of 288-950 nm with a resolution of 0.116 nm; a self-adaptive partial least squares regression model (SAM-PLSR) was employed to explore optimal model parameters for SOM prediction; and calibration parameters including sample selection for the calibration database, sample numbers and sample location sites were optimized. The results showed that the sample capacity around 60-80, rather than all of the samples in the soil library database, was selected for calibration from a spectral similarity re-ordered database regarding unknown samples; the model produced excellent predictions, with R² = 0.92, RPD = 3.53 and RMSEP = 1.03 g kg^-1. Both the soil variances of the target property and the spectra similarity of the soil background were the key factors for the calibration model, and the small sample set led to poor predictions due to the low variances of the target property, while negative effects were observed for the large sample set due to strong interferences from the soil background. Therefore, the specific unknown sample depended strategy, i.e., self-adaptive modelling, could be applied for fast SOM sensing using LIBS for soils in varied scales with improved robustness and accuracy.

Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network

Soybean seed purity is a critical factor in agricultural products, standardization of seed quality, and food processing. In this study, laser-induced breakdown spectroscopy (LIBS) as an effective technology was successfully used to identify ten varieties of soybean seeds. We improved the traditional sample preparation scheme for LIBS. Instead of grinding and squashing, we propose a time-efficient method by pressing soybean seeds into rubber sand filled with culture plates through a ruler to ensure a relatively uniform surface height. In our experimental scheme, three LIBS spectra were finally collected for each soybean seed. A majority vote based on three spectra was applied as the final decision judging the attribution of a single soybean seed. The results showed that the support vector machine (SVM) obtained the optimal identification accuracy of 90% in the prediction set. In addition, PCA-ResNet (propagation coefficient adaptive ResNet) and PCSA-ResNet (propagation coefficient synchronous adaptive ResNet) were designed based on typical ResNet structure by changing the way of self-adaption of propagation coefficients. Combined with a new form of input data called spectral matrix, PCSA-ResNet obtained the optimal performance with the discriminate accuracy of 91.75% in the prediction set. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the clustering process of the extracted features by PCSA-ResNet. For the interpretation of the good performance of PCSA-ResNet coupled with the spectral matrix, saliency maps were further applied to visually show the pixel positions of the spectral matrix that had a significant influence on the discrimination results, indicating that the content and proportion of elements in soybean seeds could reflect the variety differences.

Combined Spectroscopic Analysis of Terrestrial Analogs from a Simulated Astronaut Mission Using the Laser-Induced Breakdown Spectroscopy (LIBS) Raman Sensor: Implications for Mars

One of the primary objectives of planetary exploration is the search for signs of life (past, present, or future). Formulating an understanding of the geochemical processes on planetary bodies may allow us to define the precursors for biological processes, thus providing insight into the evolution of past life on Earth and other planets, and perhaps a projection into future biological processes. Several techniques have emerged for detecting biomarker signals on an atomic or molecular level, including laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy, laser-induced fluorescence (LIF) spectroscopy, and attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy, each of which addresses complementary aspects of the elemental composition, mineralogy, and organic characterization of a sample. However, given the technical challenges inherent to planetary exploration, having a sound understanding of the data provided from these technologies, and how the inferred insights may be used synergistically is critical for mission success. In this work, we present an in-depth characterization of a set of samples collected during a 28-day Mars analog mission conducted by the Austrian Space Forum in the Dhofar region of Oman. The samples were obtained under high-fidelity spaceflight conditions and by considering the geological context of the test site. The specimens were analyzed using the LIBS-Raman sensor, a prototype instrument for future exploration of Mars. We present the elemental quantification of the samples obtained from LIBS using a previously developed linear mixture model and validated using scanning electron microscopy energy dispersive spectroscopy. Moreover, we provide a full mineral characterization obtained using ultraviolet Raman spectroscopy and LIF, which was verified through ATR FT-IR. Lastly, we present possible discrimination of organics in the samples using LIF and time-resolved LIF. Each of these methods yields accurate results, with low errors in their predictive capabilities of LIBS (median relative error ranging from 4.5% to 16.2%), and degree of richness in subsequent inferences to geochemical and potential biochemical processes of the samples. The existence of such methods of inference and our ability to understand the limitations thereof is crucial for future planetary missions, not only to Mars and Moon but also for future exoplanetary exploration.

A Review of Diagnostics Methodologies for Metal Additive Manufacturing Processes and Products

Additive manufacturing technologies based on metal are evolving into an essential advanced manufacturing tool for constructing prototypes and parts that can lead to complex structures, dissimilar metal-based structures that cannot be constructed using conventional metallurgical techniques. Unlike traditional manufacturing processes, the metal AM processes are unreliable due to variable process parameters and a lack of conventionally acceptable evaluation methods. A thorough understanding of various diagnostic techniques is essential to improve the quality of additively manufactured products and provide reliable feedback on the manufacturing processes for improving the quality of the products. This review summarizes and discusses various ex-situ inspections and in-situ monitoring methods, including electron-based methods, thermal methods, acoustic methods, laser breakdown, and mechanical methods, for metal additive manufacturing.

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.

Characterization of Pottery from Teotihuacan Using Laser-Induced Breakdown Spectroscopy and Inductively Coupled Plasma Optical Emission Spectroscopy

Pottery sherds from Teotihuacan, Mexico, belonging to the Formative and Classic periods (150 BCE-700 CE) were investigated using laser-induced breakdown spectroscopy (LIBS) and inductively coupled plasma optical emission spectrometry (ICP-OES). LIBS results show that most of the investigated samples have primarily the same elemental composition. Nevertheless, there are also a few sherds that could be associated to foreign ceramic groups with characteristic concentrations of Na, K, Ca, Mn, Rb, and Sr. The relative elemental composition of red pigments applied on ceramic bodies was also analyzed through a LIBS depth profiling. Diverse hematite-based pigments were distinguished according to the detected iron content. Hematite was also combined with red soils with a high relative content of Mn, Sr, Ba, or Ti. The ICP-OES analysis of ceramic pastes is consistent with the emission intensities obtained using LIBS. Principal component analysis indicates that all samples identified as locals belong to a single chemical group. Moreover, locally made ceramics and the analyzed clays from the nearby area have the same elemental composition, which appears clearly differentiated from imported samples.

Determination of Spectroscopic Parameters of Ag(I) and Ag(II) Emission Lines Using Time-Independent Extended C-Sigma Method

The knowledge of the spectroscopic parameters of the elemental emission lines is important for diagnostics of laser-induced plasmas and the application of calibration-free/fundamental parameters analytical methods. In this paper, we used the recently proposed time-independent extended C-sigma method for determining, for the first time, the transition probabilities and Stark broadening coefficients of several neutral (TIECS) and ionic silver emission lines. The method allows for a compensation of self-absorption in the plasma, thus providing a measure of the spectroscopic parameters which is not affected by the optical thickness of the plasma.

Osbeck) by Laser-Induced Breakdown Spectroscopy (LIBS)

The correct recognition of sweet orange (Citrus sinensis L. Osbeck) variety accessions at the nursery stage of growth is a challenge for the productive sector as they do not show any difference in phenotype traits. Furthermore, there is no DNA marker able to distinguish orange accessions within a variety due to their narrow genetic trace. As different combinations of canopy and rootstock affect the uptake of elements from soil, each accession features a typical elemental concentration in the leaves. Thus, the main aim of this work was to analyze two sets of ten different accessions of very close genetic characters of three varieties of fresh citrus leaves at the nursery stage of growth by measuring the differences in elemental concentration by laser-induced breakdown spectroscopy (LIBS). The accessions were discriminated by both principal component analysis (PCA) and a classifier based on the combination of classification via regression (CVR) and partial least square regression (PLSR) models, which used the elemental concentrations measured by LIBS as input data. A correct classification of 95.1% and 80.96% was achieved, respectively, for set 1 and set 2. These results showed that LIBS is a valuable technique to discriminate among citrus accessions, which can be applied in the productive sector as an excellent cost-benefit tool in citrus breeding programs.

Ex vivo three-dimensional elemental imaging of mouse brain tissue block by laser-induced breakdown spectroscopy

Measurement and reconstruction of an elemental image of large brain tissue will be beneficial to the diagnosis of neurological brain diseases. Herein, laser-induced breakdown spectroscopy (LIBS) is introduced for three dimensional (3D) elemental analysis of paraffin-embedded mouse brain tissue blocks. It is used for the first time towards the mapping of mouse brain block samples. A micro-LIBS prototype is developed for brain elemental imaging and a layer-by-layer approach is used to reconstruct the 3D distribution of Ca, Mg, Na, Cu, and P in the brain tissue. Images are captured with 50 μm lateral resolution and 300 μm depth resolution. The images show that the reclamation area of the cortex surface is enriched with Ca and Mg. In contrast, the Cu distribution is circular and is found primarily in the entirety of the cerebral cortex for the paraffin-embedded brain samples. Elemental imaging results suggest that the highest P intensity is found in the cerebellum nearby the middle sagittal plane in the left-brain paraffin block. These preliminary results indicate that LIBS is a potentially powerful tool for elemental bioimaging of the whole brain and may further improve the understanding of complex brain mechanisms.

Gold Nanoparticle-Enhanced Laser-Induced Breakdown Spectroscopy and Three-Dimensional Contour Imaging of an Aluminum Alloy

In the present work, nanoparticle-enhanced laser-induced breakdown spectroscopy was used to analyze an aluminum alloy. Although LIBS has numerous advantages, it suffers from low sensitivity and low detection limits compared to other spectrochemical analytical methods. However, using gold nanoparticles helps to overcome such drawbacks and enhances the LIBS sensitivity in analyzing aluminum alloy in the current work. Aluminum was the major element in the analyzed samples (99.9%), while magnesium (Mg) was the minor element (0.1%). The spread of gold nanoparticles onto the Al alloy and using a laser with different pulse energies were exploited to enhance the Al alloy spectral lines. The results showed that Au NPs successfully improved the alloy spectral lines intensity by eight times, which could be useful for detecting many trace elements in higher matrix alloys. Under the assumption of local thermodynamic equilibrium, the Boltzmann plot was used to calculate the plasma temperature. Besides, the electron density was calculated using Mg and H lines at Mg(I) at 285.2 nm and H_α(I) at 656.2 nm, respectively. Three-dimensional contour mapping and color fill images contributed to understanding the behavior of the involved effects.

Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

Methodology for the Implementation of Internal Standard to Laser-Induced Breakdown Spectroscopy Analysis of Soft Tissues

The improving performance of the laser-induced breakdown spectroscopy (LIBS) triggered its utilization in the challenging topic of soft tissue analysis. Alterations of elemental content within soft tissues are commonly assessed and provide further insights in biological research. However, the laser ablation of soft tissues is a complex issue and demands a priori optimization, which is not straightforward in respect to a typical LIBS experiment. Here, we focus on implementing an internal standard into the LIBS elemental analysis of soft tissue samples. We achieve this by extending routine methodology for optimization of soft tissues analysis with a standard spiking method. This step enables a robust optimization procedure of LIBS experimental settings. Considering the implementation of LIBS analysis to the histological routine, we avoid further alterations of the tissue structure. Therefore, we propose a unique methodology of sample preparation, analysis, and subsequent data treatment, which enables the comparison of signal response from heterogenous matrix for different LIBS parameters. Additionally, a brief step-by-step process of optimization to achieve the highest signal-to-noise ratio (SNR) is described. The quality of laser-tissue interaction is investigated on the basis of the zinc signal response, while selected experimental parameters (e.g., defocus, gate delay, laser energy, and ambient atmosphere) are systematically modified.

Qualitative and Semiquantitative Determination of the Atomic and Molecular Tungsten Distributions in Hybrid Hydroxyurethanes-Poly(dimethylsiloxane) Films Containing Phosphotungstates ([PW12O40]3-)

In this study, hybrid poly(dimethylsiloxane)-derived hydroxyurethanes films (PDMSUr-PWA) containing phosphotungstic acid (H₃PW₁₂O₄₀/PWA) were characterized using field emission gun scanning electron microscopy (FEG-SEM), in attenuated total reflectance Fourier transform mid-infrared mode (ATR FT-MIR), and analyzed using synchrotron radiation micro-X-ray fluorescence (SR-μXRF), synchrotron radiation grazing incidence X-ray fluorescence (SR-GIXRF), laser-induced breakdown spectroscopy (LIBS), and instrumental neutron activation analysis (NAA) in order to correlate the distribution patterns of tungsten and properties of PDMSUr-PWA films. PDMS constitute elastomers with good mechanical, thermal, and chemical (hydrophobicity/non-hygroscopy) resistance. Currently, products based on urethanes (e.g., polyurethanes) are widely used in many applications as plastics, fiber-reinforced polymers, high-performance adhesives, corrosion-resistant coatings, photochromic films, among others. The possibility to combine inorganic and organic components can produce a hybrid material with unique properties. PWA has an important role as agent against the corrosion of steel surfaces in different media, besides exhibiting amazing catalytic and photochromic properties in these films. PWA kept its structure inside of these hybrid films through interactions between the organic matrix of PDMSUr and silanol from the inorganic part (organically modified silica), as was shown using ATR FT-MIR spectra. The FEG-SEM/SR-μXRF/wide-angle X-ray scattering (WAXS)/X-ray diffraction (XRD)/energy dispersive X-ray results proved the presence of PWA in the composition of domains of PDMSUr-PWA films. At PWA concentrations higher than 50 wt%/wt, tungsten segregation across the thickness is predominant, while that at PWA concentrations lower than 35 wt%/wt, tungsten segregation at surface is predominant. Inhomogeneities in the tungsten distribution patterns (at micrometric and millimetric level) may play an important role in the mechanical properties of these films (elastic modulus and hardness).

Depth Profiling Investigation of Seawater Using Combined Multi-Optical Spectrometry

Depth profiling investigation plays an important role in studying the dynamic processes of the ocean. In this paper, a newly developed hyphenated underwater system based on multi-optical spectrometry is introduced and used to measure seawater spectra at different depths with the aid of a remotely operated vehicle (ROV). The hyphenated system consists of two independent compact deep-sea spectral instruments, a deep ocean compact autonomous Raman spectrometer and a compact underwater laser-induced breakdown spectroscopy system for sea applications (LIBSea). The former was used to take both Raman scattering and fluorescence of seawater, and the LIBS signal could be recorded with the LIBSea. The first sea trial of the developed system was taken place in the Bismarck Sea, Papua New Guinea, in June 2015. Over 4000 multi-optical spectra had been captured up to the diving depth about 1800 m at maximum. The depth profiles of some ocean parameters were extracted from the captured joint Raman-fluorescence and LIBS spectra with a depth resolution of 1 m. The concentrations of SO42- and the water temperatures were measured using Raman spectra. The fluorescence intensities from both colored dissolved organic matter (CDOM) and chlorophyll were found to be varied in the euphotic zone. With LIBS spectra, the depth profiles of metallic elements were also obtained. The normalized intensity of atomic line Ca(I) extracted from LIBS spectra raised around the depth of 1600 m, similar to the depth profile of CDOM. This phenomenon might be caused by the nonbuoyant hydrothermal plumes. It is worth mentioning that this is the first time Raman and LIBS spectroscopy have been applied simultaneously to the deep-sea in situ investigations.

Fast Classification of Geographical Origins of Honey Based on Laser-Induced Breakdown Spectroscopy and Multivariate Analysis

Traceability of honey is highly required by consumers and food administration with the consideration of food safety and quality. In this study, a technique named laser-induced breakdown spectroscopy (LIBS) was used to fast trace geographical origins of acacia honey and multi-floral honey. LIBS emissions from elements of Mg, Ca, Na, and K had significant differences among different geographical origins. The clusters of honey from different geographical origins were visualized with principal component analysis. In addition, support vector machine (SVM) and linear discrimination analysis (LDA) were used to quantitively classify the origins. The results indicated that SVM performed better than LDA, and the discriminant results of multi-floral honey were better than acacia honey. The accuracy and mean average precision for multi-floral honey were 99.7% and 99.7%, respectively. This study provided a fast approach for geographical origin classification, and might be helpful for food traceability.

Fast Quantification of Honey Adulteration with Laser-Induced Breakdown Spectroscopy and Chemometric Methods

Honey adulteration is a major issue in food production, which may reduce the effective components in honey and have a detrimental effect on human health. Herein, laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to fast quantify the adulterant content. Two common types of adulteration, including mixing acacia honey with high fructose corn syrup (HFCS) and rape honey, were quantified with univariate analysis and partial least squares regression (PLSR). In addition, the variable importance was tested with univariable analysis and feature selection methods (genetic algorithm (GA), variable importance in projection (VIP), selectivity ratio (SR)). The results indicated that emissions from Mg II 279.58, 280.30 nm, Mg I 285.25 nm, Ca II 393.37, 396.89 nm, Ca I 422.70 nm, Na I 589.03, 589.64 nm, and K I 766.57, 769.97 nm had compact relationship with adulterant content. Best models for detecting the adulteration ratio of HFCS 55, HFCS 90, and rape honey were achieved by SR-PLSR, VIP-PLSR, and VIP-PLSR, with root-mean-square error (RMSE) of 8.9%, 8.2%, and 4.8%, respectively. This study provided a fast and simple approach for detecting honey adulteration.