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

Laser-induced breakdown spectroscopy (LIBS) has been evaluated for measurement of As, Hg, S, and Se in aqueous samples. Single pulse (SP) and double pulse (DP) LIBS, with a 1064 nm Nd: YAG laser source, were utilized to generate micro plasma from the surface of a liquid jet containing these elements. Plasma emission was collected at a 45° angle with the laser beam for As and Se detection in UV wavelength range. However, the confocal mode was used to collect the atomic emission for Hg and S detection in visible and near IR range, respectively. Atomic emission lines 228.81 nm for As, 546.07 nm for Hg, 921.2 nm for S, and 196.09, 203.98 and 206.32 nm for Se were detected in the LIBS spectra. Calibration curves were developed to estimate the limit of detection for each analyte and found to be 2, 16, 16, and 29 ppm for As, Hg, S, and Se, respectively, with SP LIBS and 0.4, 8, 8 and 11 ppm, respectively, with DP LIBS. The study has implications in monitoring of these pollutants in flue gas desulfurization (FGD) wastewater.

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

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.

In the present work a method was proposed for the direct determination of Ca, Fe, K, and Mg and qualitative analysis of anthropized and cabruca soil samples from different depths (0–20 and 20–40 cm) by laser-induced breakdown spectroscopy (LIBS). The LIBS instrumental parameters were evaluated using a central composite design with a central point. The variables evaluated were as follows: delay time in five levels (0.5, 0.7, 1.2, 1.7, and 1.9 μs) and fluence at five levels (1448, 1811, 2659, 3514, and 3820 J·cm–2). The best results obtained were with a delay time of 1.2 μs and fluence of 2659 J × cm–2. The proposed calibration model for Ca, Fe, K, and Mg obtained using LIBS data presented a good correlation with the reference values obtained by inductively coupled plasma atomic emission spectrometry (ICP OES). In addition, multivariate data analysis from LIBS spectra in the region of 186–1042 nm was performed using principal component analysis (PCA). From the LIBS spectra it was possible to discriminate the different analyzed samples, showing that the combination of LIBS with PCA is an excellent option for the discrimination of soils.

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) 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 of major and minor oxides in steel slags: Influence of detection geometry and signal normalization

Laser Induced Breakdown Spectroscopy (LIBS) analysis by Nd:YAG laser emitting at 355nm were performed on different clean and dirty surfaces of marble fragments collected from ancient quarries in Greece, Turkey and Italy, in order to determine semi-quantitavely the atomic composition of the bulk material and encrustation. The method here developed for element concentrations retrieval could be applied during laser cleaning process to supply the information about the effective crust composition at different depths and the point where the process should be interrupted. The knowledge of the crust composition along successive layers is also important for determining the restoration procedures. The elements measured in the encrustations, such as Si, Al, Ca, C, Ti, Mn, Mg, Na, Ba, Sr and Cu are also present in the bulk, but at different concentrations whose determination allows for the process monitoring. The only element here observed in the crusts and not detected in the bulk materials is Chromium, whose progressive disappearance from LIBS spectra could be used as another indicator of the laser cleaning effectiveness. On a sample from Turkey also Vanadium was detected in the encrustation. The present LIBS measuring method was validated by SEM-EDX and ICP analyses. The clean marble surface and encrustations were further analysed by Laser Induced Fluorescence (LIF), which could be used as an alternative technique for the on-line control of the cleaning effectiveness. Better discrimination between dirty and clean marble surface was obtained when 266nm excitation was applied instead of 355 nm. Characteristic LIF spectral signatures allows for the discrimination between different type of the natural stones, even under the water.

Geographical analysis of “conflict minerals” utilizing laser-induced breakdown spectroscopy

Fusion of NIR and LIBS spectra via interpolation for plastic classification.

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 this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements constituting the alloy. The typical approach to Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis uses calibration curves, suitably built based on appropriate reference standards. More recently, statistical methods relying on the principles of ANNs are increasingly used. In particular, an ANN can be used for a preliminary exploration of the LIBS spectra in order to find out the most significant areas of the spectrum, which will be used by another ANN dedicated to the calibration. In this paper we will show that the use of ANNs to deal with LIBS spectra provides a viable, fast and robust method for LIBS quantitative analysis. Actually, this approach requires a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and can automatically analyze a large number of samples.

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.

Recognition of explosives fingerprints on objects for courier services using machine learning methods and laser-induced breakdown spectroscopy