Qualitative and Quantitative Analysis of Soils Using Laser-Induced Breakdown Spectroscopy and Chemometrics Tools

Laser induced breakdown spectroscopy (LIBS) is an atomic emission based spectroscopy that uses a laser pulse as the source of excitation. The laser is focused to form hot plasma, which atomizes and excites the sample. In the LIBS spectrum each “feature” is the amplitude or intensity detected at different wavelengths in the range of 200–1000 nm. Pattern recognition techniques were applied on samples with similar elemental composition resulting in almost similar LIBS spectra which are visually very difficult to differentiate. It was observed that the classification results obtained from different classifiers were sensitive to data preprocessing. The outlier detection and removal techniques PCA, Dendrogram using Agglomerative Algorithm, Editing by Nearest Neighbour (NN) and Distance Matrix approaches were used in preprocessing step. After removing outlier(s) the resulting training patterns were used to model the k-Nearest Neighbour (k-NN), Principal Component Analysis (PCA), Dendrogram, Multiclass Support Vector Machine (SVM) and Decision Tree classifiers. In k-NN after removing outlier(s) the average classification accuracy was increased by 2% for high energy materials (HEM), but no improvement in non high energy materials (Non HEM) or in top level classification (decide either HEM or Non HEM). But, for other classifiers the classification accuracy gets reduced. Finally instead of removing outlier(s) dimensionality reduction by thresholding was applied and the classification accuracy increased by 4% in k-NN for HEM and 38% in multiclass SVM for HEM and 4% for Non-HEM.

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) were applied to the classification of LIBS spectra from gold ores prepared as pressed pellets from pulverized bulk samples. For each sample, 5000 single-shot LIBS spectra were obtained. Although the gold concentrations in the samples were as high as 7.7 µg/g, Au emission lines were not observed in most single-shot LIBS spectra, rendering the application of the usual ensemble-averaging approach for spectral processing to be infeasible. Instead, a PCA approach was utilized to analyze the collection of single-shot LIBS spectra. Two spectral ranges of 21 nm and 0.15 nm wide were considered, and LIBS variables (i.e., wavelengths) reduced to no more than three principal components. Single-shot spectra containing Au emission lines (positive spectra) were discriminated by PCA from those without the spectral feature (negative spectra) in a spectral range of less than 1 nm wide around the Au(I) 267.59 nm emission line. Assuming a discrete gold distribution at very low concentration, LIBS sampling of gold particles seemed unlikely; therefore, positive spectra were considered as data outliers. Detection of data outliers was possible using two PCA statistical parameters, i.e., sample residual and Mahalanobis distance. Results from such a classification were compared with a standard database created with positive spectra identified with a filtering algorithm that rejected spectra with an Au intensity below the smallest detectable analytical LIBS signal (i.e., below the LIBS limit of detection). The PCA approach successfully identified 100% of the data outliers when compared with the standard database. False identifications in the multivariate approach were attributed to variations in shot-to-shot intensity and the presence of interfering emission lines.

In the process of oil exploration and development, it becomes difficult to identify the lithology of cuttings due to the small size of cutting particles produced by extensively used Polycrystalline Diamond Compact (PDC) drill bit. Laser Induced Breakdown Spectroscopy (LIBS) is an appealing technique compared with many other conventional analysis methods due to its simple set-up, on-line, real time, stand off and multi-element analytical capabilities. In this paper, a newly developed approach for cuttings identification using laser-induced breakdown spectroscopy was introduced. Principal component analysis (PCA) and partial least squares (PLS) were applied to analyze LIBS spectra of three drill cuttings to perform cuttings identification. With the purpose of reducing workload and improving predictive accuracy, 23 characteristic spectra ranges were extracted as fingerprints from each integrated LIBS spectrum. Leave-one-out cross-validation method (LOO-CV) was used to evaluate the predictive capability of this approach. All the LIBS spectra were identified correctly under a lax criterion. The obtained preliminary results demonstrated the potential feasibility of cutting identification using LIBS in combination with chemometric methods.

Data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) coupled with random forest (RF) for the classification and discrimination of compound salvia miltiorrhiza

Response surface methodology for optimizing LIBS testing parameters: A case to conduct the elemental contents analysis in soil

We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types (fused silica, flint, borosilicate and soda–lime) were involved in the tests. Four multivariate chemometric data evaluation methods (linear discrimination analysis, quadratic discrimination analysis, classification tree and random forest) for LIBS data were evaluated with and without data compression (principal component analysis). Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda–lime glasses. The developed methods can be used in forensic analysis.

Because characterization of ink pens on documents represents an important forensic discipline, the need of legal systems for faster, in-situ and more accurate technique that identify and differentiate questioned documents involved in criminal or legal matters is of great interest.Laser-induced breakdown spectroscopy (LIBS) was experimentally proved to be a promising technique emerging in the field of forensic applications, specifically, questioned documents despite being from the same source or different source.However, the call for newly improved data analysis is unlimited and the need to know if the used ink has the same principal components or not.In the present work, the application of PCA has been demonstrated as statistics-based spectral analysis of the output resultant LIBS spectra for the forensic questioned documents analysis.The LIBS spectra were acquired from IR laser induced plasmas of black ink on regular document paper of ten brands of black gel inks commercially available in the Egyptian market.The idea behind principal component analysis is based on the loadings for the first three principal components.The obtained results have been shown to characterize ink pens used in the analysis.However, the first principal component encompasses the dominant characterization for the used pens.The results of this study indicate that Laser Induced Breakdown Spectroscopy (LIBS) of ink pens aided with PCA technique can promote the LIBS capability to faster, error-free and automatic elemental identification and differentiation for in-situ forensic purposes, specifically, questioned documents.

In the present work, appropriate machine learning techniques coupled with LIBS have been proposed for the effective classification of multielement rock samples. To obtain the best classification efficiency most suitable emission lines were selected. Plasma on the surface of seventeen rock samples was generated using a 532 nm Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser, and optical emission spectra were collected via an Avantes spectrometer. Well-isolated signature emission lines corresponding to detected elements (Ca, Mg, Na, K, Fe, Ba, Sr, Si, Al, and Li) were chosen as input for the machine learning algorithms. Three machine learning techniques, including analysis of variance (ANOVA), principal component analysis (PCA), and PCA coupled with standard normal variate (SVM), were utilized on normalized intensities of selected spectral lines of detected elements. ANOVA testing on the selected lines was employed to assess the normality and suitability of data for further machine learning techniques. The combination of laser-induced breakdown spectroscopy (LIBS) with PCA enabled a comprehensive classification of rock samples. The linearity and efficiency of PCA were enhanced by utilizing the support vector machine (SVM), resulting in the accurate classification of rock samples. This study demonstrates that to assess the effective classification of multielement rock samples the appropriate emission lines and machine learning techniques are crucial. Using this methodology results become more reliable as compared to conventional machine learning techniques.

The present paper deals with the detection and quantification of toxic heavy metals like Cd, Co, Pb, Zn, Cr, etc. in environmental samples by using the technique of laser-induced breakdown spectroscopy (LIBS) and calibration-free LIBS (CF-LIBS). A MATLABTM program has been developed based on the CF-LIBS algorithm given by earlier workers and concentrations of pollutants present in industrial area soil have been determined. LIBS spectra of a number of certified reference soil samples with varying concentrations of toxic elements (Cd, Zn) have been recorded to obtain calibration curves. The concentrations of Cd and Zn in soil samples from the Jajmau area, Kanpur (India) have been determined by using these calibration curves and also by the CF-LIBS approach. Our results clearly demonstrate that the combination of LIBS and CF-LIBS is very useful for the study of pollutants in the environment. Some of the results have also been found to be in good agreement with those of ICP-OES.

We report a simple sub parts per million (sub-ppm) detection method for heavy metals in water by laser-induced breakdown spectroscopy (LIBS). Filter papers were used as the substrates for both transforming aqueous solutions to solid samples and for pre-concentrating dissolved heavy metal ions. The amount of sample uptake was 1.2 g by soaking a filter paper. This provides limit-of-detection (LOD) values of 2.7 and 0.36 ppm for Pb and Cr, respectively. The LODs could be improved remarkably by pre-concentrating the heavy metal ions. When a 40 g sample solution was evaporated on a filter paper, the LODs of 75 and 18 parts per billion (ppb) were obtained for Pb and Cr, respectively. Moreover, by either increasing the evaporated amount of sample solution or applying an argon gas flow, further improvement of the LODs was found to be very promising. The LIBS spectra of tap water were recorded using the pre-concentration method and are discussed in comparison with the results from inductively coupled plasma atomic emission spectroscopy (ICP-AES). We could observe strong emission lines of Ca, Mg, K, Cu, and Sr in the tap water, of which concentrations were determined to be 6.3, 1.3, 1.1, 0.64, and 0.046 ppm, respectively, by ICP-AES. Our method shows promise as a fast, reliable, water-quality monitoring tool for heavy-metal concentrations as well as for hardness.

High-throughput analysis of single particles by micro laser induced breakdown spectroscopy

A novel setup, combining two spectroscopic techniques, laser induced breakdown spectroscopy (LIBS) and Raman spectroscopy in a hybrid unit, is described. The work presented herein is part of a broader project that aims to demonstrate the applicability of the hybrid LIBS-Raman unit as an analytical tool for the investigation of samples and objects of cultural heritage. The system utilizes a nanosecond pulsed Nd:YAG laser (532 nm) for both LIBS and Raman analysis. In the Raman mode, a low intensity beam from the laser probes the sample surface and the scattering signal is collected into a grating spectrograph coupled to an intensified charge-coupled device (ICCD) detector, which records the Raman spectrum. In the LIBS mode a single high intensity pulse from the laser irradiates the sample surface and the time- and spectrally-resolved emission from the resulting laser ablation plume yields the LIBS spectrum. The use of a non-gated CCD detector was found to produce similar quality data (in terms of S/N ratio and fluorescence background) in the Raman mode, while in the LIBS mode spectral features were clearly broader but did not prevent identification of prominent atomic emission lines. Several model pigment samples were examined and the data obtained show the ability of the hybrid unit to record both Raman and LIBS spectra from the same point on the sample, a clear advantage over the use of different analytical setups.

The Kingdom of Saudi Arabia is one of the leading date producing countries. Unfortunately, this important fruit crop is under great threat from the red palm weevil (RPW) (Rhynchophorus ferrugineus), which is a highly invasive pest. Several techniques, including visual inspection, acoustic sensors, sniffer dogs, and pheromone traps have been tried to detect the early stages of a RPW infestation; however, each method has suffered certain logistical and implementation issues. We have applied laser induced breakdown spectroscopy (LIBS) for the early detection of RPW infestation. Through the analysis of the observed LIBS spectra of different infested and healthy samples, we have found presence of Ca, Mg, Na, C, K elements and OH, CN molecules. The spectra also reveal that with the population growth of the pest, the intensity of Mg and Ca atomic lines in LIBS spectra increases rapidly. Similar behavior is observed in the molecular lines of LIBS spectra. The obtained results indicate that the LIBS technique can be used for the early detection of RPW infestation without damaging the date palms.

Laser induced breakdown spectroscopy (LIBS) was applied for analyzing the impact of earthquake on the elemental distribution of the soil, collected from the earthquake-stricken locations near Eurasian and Indian Tectonic Plates in the Kashmir Valley. The quantitative elemental analysis was carried out using an advanced calibration-free LIBS technique, assuming the establishment of optically thin plasma under the local thermodynamic equilibrium conditions. The results exhibited that the soil has nearly random concentration distribution of metallic elements such as Al, Na, and K, nonmetallic element P, micronutrients such as Fe and Si and secondary macronutrients such as Ca, S, and Mg, in and around the radius of 9 km from the epicenter of the earthquake. It is exhibited that the concentration of carbon in particular is the highest near the epicenter and declines systematically with the outward radial distance. The quantitative analysis by LIBS was confirmed and counter verified through the inductively coupled plasma atomic emission spectroscopy (ICP–OES), and results achieved by both the techniques (LIBS and ICP–OES) are in good agreement.

Information about the elements in milk represents a major tool to identify different milk types and their quality. This is because milk constituents of minerals, proteins and fats differ by different mammals and even for different breeds. In the present work, fresh samples of widely consumed farm animals’ milk, namely from buffaloes, camels, sheep and goats were analyzed qualitatively using laser induced breakdown spectroscopy (LIBS) technique. It was focused on calcium, magnesium, iron, sodium, strontium and barium. Molecular bands of CN in the LIBS spectra were used to study the proteins levels in different milk samples. The results revealed that milk samples from different animals could be characterized as completely different combination of the elements constituents were present in magnesium and iron were higher in goat milk compared to the other animals; in addition, the percentage of barium relative to calcium was lower in all milk samples which confirm plant feeding for all studied animals. In general, this work demonstrates the feasibility of using LIBS as a simple, fast and cost effective technique to perform in situ qualitative analysis of milk samples in dairy farms without any need to transport samples to the laboratories.