Laser-induced Breakdown Spectroscopy Spectral Data Research Articles

Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy

Analysis of emission from laser-induced plasma has a unique capability for quantifying the major and minor elements present in any type of samples under optimal analysis conditions. Chemometric techniques are very effective and reliable tools for quantification of multiple components in complex matrices. The feasibility of laser-induced breakdown spectroscopy (LIBS) in combination with multivariate analysis was investigated for the analysis of environmental reference materials (RMs). In the present work, different (Certified/Standard) Reference Materials of soil and plant origin were analyzed using LIBS and the presence of Al, Ca, Mg, Fe, K, Mn and Si were identified in the LIBS spectra of these materials. Multivariate statistical methods (Partial Least Square Regression and Partial Least Square Discriminant Analysis) were employed for quantitative analysis of the constituent elements using the LIBS spectral data. Calibration models were used to predict the concentrations of the different elements of test samples and subsequently, the concentrations were compared with certified concentrations to check the authenticity of models. The non-destructive analytical method namely Instrumental Neutron Activation Analysis (INAA) using high flux reactor neutrons and high resolution gamma-ray spectrometry was also used for intercomparison of results of two RMs by LIBS.

Effect of Sample Preparation on the Discrimination of Bacterial Isolates Cultured in Liquid Nutrient Media Using Laser-Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is used as the basis for discrimination between two genera of gram-negative bacteria and two genera of gram-positive bacteria representing pathogenic threats commonly found in poultry processing rinse waters. Because LIBS-based discrimination relies primarily upon the relative proportions of inorganic cell components including Na, K, Mg, and Ca, this study aims to determine the effects of trace mineral content and pH found in the water source used to isolate the bacteria upon the reliability of the resulting discriminant analysis. All four genera were cultured using tryptic soy agar (TSA) as the nutrient medium, and were grown under identical environmental conditions. The only variable introduced is the source water used to isolate the cultured bacteria. Cultures of each bacterium were produced using deionized (DI) water under two atmosphere conditions, reverse osmosis (RO) water, tap water, phosphate buffered saline (PBS) water, and TRIS buffered water. After 3 days of culture growth, the bacteria were centrifuged and washed three times in the same water source. Bacteria were then freeze dried, mixed with microcrystalline cellulose, and a pellet was made for LIBS analysis. Principal component analysis (PCA) was used to extract related variations in LIBS spectral data among the four bacteria genera and six water types used to isolate the bacteria, and Mahalanobis discriminant analysis (MDA) was used for classification. Results indicate not only that the four genera can be discriminated from each other in each water type, but that each genus can be discriminated by water type used for isolation. It is concluded that in order for LIBS to be a reliable and repeatable method for discrimination of bacteria grown in liquid nutrient media, care must be taken to insure that the water source used in purification of the culture be precisely controlled regarding pH, ionic strength, and proportionate amounts of mineral cations present.

An Approach of Laser-Induced Breakdown Spectroscopy to Detect Toxic Metals in Crushed Ice Ball

This paper deals the application of laser-induced breakdown spectroscopy (LIBS) to toxic metals used as pigment in crushed ice-ball samples. The present work highlights the advantages of LIBS as in situ, real-time analytical tool for rapid detection of toxic or heavy metals like lead (Pb) and chromium (Cr) and non toxic elements like carbon (C), nitrogen (N), magnesium (Mg), calcium (Ca), sodium (Na), and potassium (K) in crushed ice-ball of different colors (red, green, yellow, pale yellow, and orange) collected from five different areas, with minimal sample preparation. For rapid surveillance of toxic metals we have used multivariate analysis, that is, principal component analysis (PCA) with the LIBS spectral data of ice-ball samples. This study suggests that LIBS coupled with PCA may be an instant diagnostic tool for identification and classification of adulterated and nonadulterated samples.

Detection of Pesticides and Dioxins in Tissue Fats and Rendering Oils Using Laser-Induced Breakdown Spectroscopy (LIBS)

In laser-induced breakdown spectroscopy (LIBS), a series of powerful laser pulses are directed at a surface to form microplasmas from which light is collected and spectrally analyzed to identify the surface material. In most cases, no sample preparation is needed, and results can be automated and made available within seconds to minutes. Advances in LIBS spectral data analysis using multivariate regression techniques have led to the ability to detect organic chemicals in complex matrices such as foods. Here, the use of LIBS to differentiate samples contaminated with aldrin, 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin, chlorpyrifos, and dieldrin in the complex matrices of tissue fats and rendering oils is described. The pesticide concentrations in the samples ranged from 0.005 to 0.1 μg/g. All samples were successfully differentiated from each other and from control samples. Sample concentrations could also be differentiated for all of the pesticides and the dioxin included in this study. The results presented here provide first proof-of-principle data for the ability to create LIBS-based instrumentation for the rapid analysis of pesticide and dioxin contamination in tissue fat and rendered oils.

Intact Soil Core Total, Inorganic, and Organic Carbon Measurement Using Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is an emerging elemental analysis technology with the potential to provide rapid, accurate, and precise analysis of soil constituents. We evaluated the accuracy of LIBS in measuring soil profile C for field-moist, intact soil cores by interrogating 78 intact soil cores from three Montana agricultural fields. Samples from each core were analyzed in the laboratory for total C (TC), inorganic C (IC), and soil organic C (SOC). Partial least squares 2 (PLS2) regression calibration models were derived using 58 cores (227 samples) and independently validated at the field scale with the remaining 20 cores (79 samples). We obtained the best LIBS validation predictions for IC (r2 = 0.66, standard error of prediction [SEP] = 5.3 g kg−1, ratio product differential [RPD] = 1.7) followed by TC (r2 = 0.63, SEP = 6.0 g kg−1, RPD = 1.6) and SOC (r2 = 0.22, SEP = 3.2 g kg−1, RPD = 1.1). Although the SEP for SOC was less than for TC and IC, low SOC variance limited our ability to evaluate LIBS SOC prediction capabilities. Regression coefficients from LIBS PLS2 models suggested a reliance on stoichiometric relationships between C and other elements related to total and inorganic C in the soil matrix (e.g., Ca, Mg, and Si) to discriminate TC from IC. Results indicate that LIBS spectral data collected on intact soil cores can be calibrated to accurately estimate and differentiate between soil total and inorganic C concentrations at the field scale.

Laser-Induced Breakdown Spectroscopy and Chemometrics: A Novel Potential Method to Analyze Wheat Grains

Laser-induced breakdown spectroscopy (LIBS) has been widely used to evaluate the elemental composition (e.g., minerals or metal accumulation) on vegetal tissues. The main objective of this work was to differentiate wheat outer tissues during the grain ablation using LIBS and univariate/multivariate analysis. A high resolution spectrometer and a Nd:YAG laser (532 nm, 5 ns) was first used in order to easily identify atomic wheat emission lines. Then a pulsed excimer laser ArF (193 nm, 15 ns) and a compact fiber optic spectrometer was used to acquire LIBS spectral data from each pulse. Univariate and multivariate analyses (MW2D, PLS-DA) were carried out to provide more in depth information from the LIBS experiment. The number of pulses needed to ablate wheat tissues was successfully predicted by the supervised pattern recognition procedure. LIBS used in conjunction with multivariate analysis could be an interesting technique for rapid structural analysis of vegetal material.

Laser-Induced Breakdown Spectroscopy of Steel: A Comparison of Univariate and Multivariate Calibration Methods

Laser-induced breakdown spectroscopy (LIBS) was carried out on twenty-three low to high alloy steel samples to quantify their concentrations of chromium, nickel, and manganese. LIBS spectral data were correlated to known concentrations of the samples and three calibration methods were compared. A standard LIBS calibration technique using peak area integration normalized by an internal standard was compared to peak area integration normalized by total light and the multivariate statistical technique of partial least squares. For the partial least squares analysis, the PLS-1 algorithm was used, where a predictive model is generated for each element separately. Partial least squares regression coefficients show that the algorithm correctly identifies the atomic emission peaks of interest for each of the elements. Predictive capabilities of each calibration approach were quantified by calculating the standard and relative errors of prediction. The performance of partial least squares is on par with using iron as an internal standard but has the key advantage that it can be applied to samples where the concentrations of all elements are unknown.

Extraction of information from laser-induced breakdown spectroscopy spectral data by multivariate analysis

Laser-induced breakdown spectroscopy (LIBS) is being proposed more and more as a high-throughput technology to assess the elemental composition of materials. When a specific element is of interest, semiquantification is possible by building a calibration model using the emission line intensity of this element for known samples. However, a unique element has usually more than one emission line, and there are many examples where several emission lines used in combination give dramatically better results than any of the individual variables used alone. With a multivariate approach, models can be constructed that take into account all the emission lines related to a specific element; therefore more robust models can be developed. In this work, chemometric methods such as principal component analysis and partial least squares are proposed to resolve and extract useful information from the LIBS spectral data collected on biological materials.

2-D analysis of Ge implanted SiO 2 surfaces by laser-induced breakdown spectroscopy

2-D elemental distribution of Ge in silicon oxide substrates with differing implantation doses of between 3 × 10 16 cm − 2 and 1.5 × 10 17 cm − 2 has been investigated by Laser-Induced Breakdown Spectroscopy (LIBS). Spectral emission intensity has been optimized with respect to time, crater size, ablation depth and laser energy. Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-Ray Spectroscopy (EDX) have been utilized to obtain crater depth, morphology and elemental composition of the sample material, respectively. LIBS spectral data revealed the possibility of performing 2-D distribution analysis of Ge atoms in silicon oxide substrate. EDX analysis results confirmed that LIBS is capable to detect Ge atoms at concentrations lower than 0.2% (atomic). LIBS as a fast semi-quantitative analysis method with 50 µm lateral and 800 nm depth resolution has been evaluated. Results illustrate the potential use of LIBS for rapid, on-line assessment of the quality of advanced technology materials during the manufacturing process.

Multi-element analysis of iron ore pellets by Laser-induced Breakdown Spectroscopy and Principal Components Regression

Laser-induced Breakdown Spectroscopy (LIBS) in combination with Principal Components Regression (PCR) has been applied to determine the elemental composition of a series of run-of-mine (ROM) iron ore samples. The samples were presented for measurement both as compressed pellets and as loose chipped material. The present paper details the results of the measurements of the compressed pellets. Results from ore chips will be reported separately. LIBS spectral data was recorded in three separate spectral regions to measure major, minor and trace components of the iron ore sample pellets. Background stripping, normalization and spectral cleaning were applied to minimize the relative standard deviations of the LIBS data. PCR analysis was then applied to produce calibration models for iron, aluminum, silicon, manganese, potassium and phosphorous. These calibration models were then validated using independent LIBS measurements. Robust calibration models were determined for iron, aluminum, silicon and potassium, whilst the results for manganese were encouraging. Phosphorous, present at low levels in the ores measured, remained the most difficult element to determine accurately. The combination of LIBS and PCR shows potential for in-situ on-line determination of ore composition.