Determination of Mn and Si in iron ore by laser-induced plasma spectroscopy

Chromium in soil detection using adaptive weighted normalization and linear weighted network framework for LIBS matrix effect reduction

Rapid and accurate detection of agricultural soil chromium (Cr) is of great significance for soil pollution assessment. Laser-induced breakdown spectroscopy (LIBS) could serve as a rapid and chemical-free method for hazardous metal analysis compared with conventional chemical methods. However, the detection of LIBS is interfered by uncertainty and matrix effect. In this study, an average strategy combined with linear weighted network (LWNet) was proposed to reduce the uncertainty. Adaptive weighted normalization-LWNet (AWN-LWNet) framework was proposed to reduce the matrix effect in two soil types. The results indicated that LWNet outperformed traditional machine learning and achieved the average relative error (ARE) of 2.08 % and 3.03 % for yellow brown soil and lateritic red soil, respectively. Moreover, LWNet could effectively mine Cr feature peaks even under the low spectral resolution. AWN-LWNet was the optimal model compared with commonly used models to reduce matrix effect (ARE=4.12 %). Besides, AWN-LWNet greatly reduced the number (from 22016 to 72) of spectral variables for model input. By extracting Cr peaks from models, the difference of Cr peaks intensity could be intuitively observed, which served as spectral interpretation for matrix effect reduction. The two methods have the potential to realize the detection of hazardous metals in soil by LIBS.

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

Here, the potential of laser-induced breakdown spectroscopy (LIBS) in grading calcareous rocks for the lime industry was investigated. In particular, we developed a system equipped with non-intensified detectors operating in scanning mode, defined a suitable data acquisition protocol, and implemented quantitative data processing using both partial least squares regression (PLS-R) and a multilayer perceptron (MLP) neural network. Tests were carried out on 32 samples collected in various limestone quarries, which were preliminarily analyzed using traditional laboratory X-ray fluorescence (XRF); then, they were divided into two groups for calibration and validation. Particular attention was dedicated to the development of LIBS methodology providing a reliable basis for precise material grading. The congruence of the results achieved demonstrates the capability of the present approach to precisely quantify major and minor geochemical components of calcareous rocks, thus disclosing a concrete application perspective within the lime industry production chain.

Interpretable deep learning-assisted laser-induced breakdown spectroscopy for brand classification of iron ores

Brand classification of iron ores using laser-induced breakdown spectroscopy (LIBS) combined with artificial neural networks can quickly realize the compliance verification and guarantee the interests of both trading partners. However, its practical application is impeded by complex pretreatments and unexplained feature learning problems. According to the LIBS data characteristics of iron ores, a convolutional neural network (CNN) is designed to predict 16 types of brand iron ores from Australia, Brazil, and South Africa. The accuracies of the calibration set and the prediction set with five-fold cross-validation (5-CV) were 99.86% and 99.88%, and the value of loss function was 0.0356. Meanwhile, the established CNN method was also compared with common machine learning methods using raw spectra as input variables, and it outperformed other methods. For the first time, this work interprets the CNN's effectiveness layer by layer in self-adaptively extracting LIBS features through t-distributed stochastic neighbor embedding (t-SNE) and the quantitative data of major chemical components in iron ores. Our approach shows that deep learning assisted LIBS is able to significantly reduce manual factors in preprocessing and feature selection and has broad application prospects in the brand classification of iron ores.

Laser-Induced Breakdown Spectroscopy—An Emerging Analytical Tool for Mineral Exploration

The mineral exploration industry requires new methods and tools to address the challenges of declining mineral reserves and increasing discovery costs. Laser-induced breakdown spectroscopy (LIBS) represents an emerging geochemical tool for mineral exploration that can provide rapid, in situ, compositional analysis and high-resolution imaging in both laboratory and field and settings. We demonstrate through a review of previously published research and our new results how LIBS can be applied to qualitative element detection for geochemical fingerprinting, sample classification, and discrimination, as well as quantitative geochemical analysis, rock characterization by grain size analysis, and in situ geochemical imaging. LIBS can detect elements with low atomic number (i.e., light elements), some of which are important pathfinder elements for mineral exploration and/or are classified as critical commodities for emerging green technologies. LIBS data can be acquired in situ, facilitating the interpretation of geochemical data in a mineralogical context, which is important for unraveling the complex geological history of most ore systems. LIBS technology is available as a handheld analyzer, thus providing a field capability to acquire low-cost geochemical analyses in real time. As a consequence, LIBS has wide potential to be utilized in mineral exploration, prospect evaluation, and deposit exploitation quality control. LIBS is ideally suited for field exploration programs that would benefit from rapid chemical analysis under ambient environmental conditions.

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression

Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.

Elemental analysis of soils using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS) with multivariate discrimination: tape mounting as an alternative to pellets for small forensic transfer specimens

Elemental analysis of soil is a useful application of both laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS) in geological, agricultural, environmental, archeological, planetary, and forensic sciences. In forensic science, the question to be answered is often whether soil specimens found on objects (e.g., shoes, tires, or tools) originated from the crime scene or other location of interest. Elemental analysis of the soil from the object and the locations of interest results in a characteristic elemental profile of each specimen, consisting of the amount of each element present. Because multiple elements are measured, multivariate statistics can be used to compare the elemental profiles in order to determine whether the specimen from the object is similar to one of the locations of interest. Previous work involved milling and pressing 0.5 g of soil into pellets before analysis using LA-ICP-MS and LIBS. However, forensic examiners prefer techniques that require smaller samples, are less time consuming, and are less destructive, allowing for future analysis by other techniques. An alternative sample introduction method was developed to meet these needs while still providing quantitative results suitable for multivariate comparisons. The tape-mounting method involved deposition of a thin layer of soil onto double-sided adhesive tape. A comparison of tape-mounting and pellet method performance is reported for both LA-ICP-MS and LIBS. Calibration standards and reference materials, prepared using the tape method, were analyzed by LA-ICP-MS and LIBS. As with the pellet method, linear calibration curves were achieved with the tape method, as well as good precision and low bias. Soil specimens from Miami-Dade County were prepared by both the pellet and tape methods and analyzed by LA-ICP-MS and LIBS. Principal components analysis and linear discriminant analysis were applied to the multivariate data. Results from both the tape method and the pellet method were nearly identical, with clear groupings and correct classification rates of >94%.

Dual-pulse laser-induced breakdown spectroscopy with combinations of femtosecond and nanosecond laser pulses

Nanosecond and femtosecond laser pulses were combined in an orthogonal preablation spark dual-pulse laser-induced breakdown spectroscopy (LIBS) configuration. Even without full optimization of interpulse alignment, ablation focus, large signal, signal-to-noise ratio, and signal-to-background ratio enhancements were observed for both copper and aluminum targets. Despite the preliminary nature of this study, these results have significant implications in the attempt to explain the sources of dual-pulse LIBS enhancements.

Dual-pulse laser-induced breakdown spectroscopy in bulk aqueous solution with an orthogonal beam geometry

Use of dual-pulse laser-induced breakdown spectroscopy with an orthogonal spark orientation is presented as a technique for trace metal analysis in bulk aqueous solutions. Two separate Q-switched Nd:YAG lasers operating at their fundamental wavelengths are used to form a subsurface, laser-induced plasma in a bulk aqueous solution that is spectroscopically analyzed for the in situ detection of Ca, Cr, and Zn. Optimizing the key experimental parameters of proper spark alignment, gate delay (td), gate width (tb), and interpulse timing (deltaT) allowed experimentally determined detection limits of the order of micrograms per milliliter and submicrograms per milliliter. We present supporting evidence of a sampling mechanism that involves the formation of a cavitation bubble with the first pulse (E1) followed by analysis of that bubble with a second pulse (E2). The plasma created by E2 contains the analytically relevant information from the aqueous sample and often represents >250-fold enhancement over a single laser pulse with energy equal to E1 alone.