Multivariate data analysis (MVDA) is getting popular across the spectroscopic community. To assess accurate results, the obtained data should be preprocessed prior to utilization of any MVDA algorithm. The process of data normalization or “internal standardization” is widely used across a broad range of applications. In this manuscript we investigate the utilization of Laser-Induced Breakdown Spectroscopy (LIBS) coupled with MVDA. However, many articles regarding the use of MVDA on data from LIBS do not provide any information about the data pretreatment. This work describes the impact of LIBS data normalization approaches on MVDA classification accuracy. Also, the impact of classical data preprocessing (mean centering and scaling) exploiting the prior utilization of MVDA was studied. This issue was investigated exploiting simple soft independent modelling of class analogies algorithm. The findings were generalized for three sample matrices (steel, Al alloys, and sedimentary ores). Furthermore, the selection of an appropriate normalization algorithm is not trivial since the spectrum of each sample matrix is composed of a different number of elements and corresponding elemental lines.

Laser-Induced Breakdown Spectroscopy; Uranium ore; Elements distribution; Self-organizing maps;

This paper presents a novel approach for processing the spectral information obtained from high-resolution elemental mapping performed by means of Laser-Induced Breakdown Spectroscopy. The proposed methodology is aimed at the description of possible elemental associations within a heterogeneous sample. High-resolution elemental mapping provides a large number of measurements. Moreover, typical laser-induced plasma spectrum consists of several thousands of spectral variables. Analysis of heterogeneous samples, where valuable information is hidden in a limited fraction of sample mass, requires special treatment. The sample under study is a sandstone-hosted uranium ore that shows irregular distribution of ore elements such as zirconium, titanium, uranium and niobium. Presented processing methodology shows the way to reduce the dimensionality of data and retain the spectral information by utilizing self-organizing maps (SOM). The spectral information from SOM is processed further to detect either simultaneous or isolated presence of elements. Conclusions suggested by SOM are in good agreement with geological studies of mineralization phases performed at the deposit. Even deeper investigation of the SOM results enables discrimination of interesting measurements and reveals new possibilities in the visualization of chemical mapping information. Suggested approach improves the description of elemental associations in mineral phases, which is crucial for the mining industry.

Laser-induced breakdown spectroscopy; Raman spectroscopy; Chemometrics; Bacteria;

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods.

Laser-Induced Breakdown Spectroscopy; ;

In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.

Laser-induced breakdown spectroscopy; tissue ablation; soft tissue; classification; principal component analysis; k nearest neighbors; confusion matrix; pork;BIOMEDICAL APPLICATIONS

Classification of pork soft tissues, including skin, fat, loin, tenderloin and ham muscles, was achieved using combination of laser-induced breakdownspectroscopy, principal component analysis and k nearest neighbors classification.

LIBS; Bacteria; Neural network; K-means; Spectroscopy;LIBS

LIBS technique was used to obtain spectra of Escherichia coli and Staphylococcus aureus for identifying characteristic emission lines and then they were analyzed by K-means classifier for neural network feasibility. The potential of this method for bacteria identification was demonstrated.

MACHINE REGRESSION-MODEL; CONVEX-OPTIMIZATION; COEFFICIENTS; LIBS

In quantitative laser-induced breakdown spectroscopy analysis, spectral signals are usually represented by the linear combination of characteristic peaks with useful spectral information and unwanted noise components. All of the existing regression analysis methods are related to a spectral data matrix, which is composed of certified samples with different spectral intensity. Therefore, spectral data matrix processing is critical for quantitative LIBS analysis. A prevalent assumption when constructing a matrix approximation is that the partially observed matrix is of low-rank. Moreover, the low-rank structure always reflects the useful information and is regarded as a powerful data preprocessing method. In this paper, a novel and quantitative LIBS analysis method based on a sparse low-rank matrix approximation via convex optimization is proposed. Based on the sparsity of the spectral signals, we present a convex objective function consisting of a data-fidelity term and two parameterized penalty terms. To improve the accuracy of the quantitative analysis, a new non-convex and non-separable penalty based on the Moreau envelope is proposed. Then, the alternating direction method of multipliers algorithm was utilized to solve the optimization problem. The proposed method was applied to the quantitative analysis of 23 high alloy steel samples. Both of the performances of the Partial Least Squares and Support Vector Machine regression models are improved by using the low-rank matrix approximation scheme, which proves the effectiveness of the proposed method.

INDUCED BREAKDOWN SPECTROSCOPY; SUPPORT VECTOR MACHINES; PARTIAL LEAST-SQUARES; DISCRIMINANT-ANALYSIS; SUBSET-SELECTION; LASER; CLASSIFICATION; SAMPLES; IDENTIFICATION; ALGORITHM

In recent years, LIBS quantitative analysis based on multivariate regression has received considerable attention, and variable selection is critical for improving accuracy of multivariate regression analysis of LIBS. In the present study, sequential backward selection combined with random forest was proposed to improve detection accuracy of sulfur and phosphorus in steel. First, LIBS spectrum line of S and P was identified by the NIST database. Second, input variables for RF calibration model were selected and optimized by SBS, and RF model parameters and m) were optimized by out-of-bag estimation. Finally, optimized input variables and model parameters were employed to build an SBS-RF calibration model for quantitative analysis of P and S in steel. Results showed that the SBS-RF model provided good predictions for S and P compared with those provided by the univariate method, PLS model and traditional RF model. Thus, LIBS coupled with SBS-RF is an effective method for quality supervision and control of steel products.

ARTIFICIAL NEURAL-NETWORK; ICP-MS; LASER; LIBS; PLASMA; MATRIX; MODEL

Laser-induced breakdown spectroscopy can be used for measuring the concentrations or ratios of various trace elements in solid samples. A laser pulse with a 532 nm wavelength, 5 ns pulse duration, 100 mm beam spot diameter, and 30 mJ single pulse energy is used for ablating the United States Geological Survey series of geological standard samples. The concentrations of iron in different geological samples are obtained using artificial neural networks. In addition, the influence of matrix effects on the measurement error is analyzed. Experimental results reveal that the relative errors of BCR-1G, BHVO-2G, BIR-1G, GSD-1G, and GSE-1G between the measured concentrations and the certified concentrations are 1.86%, 5.73%, 0.27%, 3.86%, and 2.63%, respectively, which indicate that the LIBS method using back propagation artificial neural networks can effectively determine the iron concentrations of USGS geological standard samples. The matrix effects of standard samples are analyzed, which shows that the matrix effects of GSD-1G and GSE-1G are more different than others. As a result, some significant measurement errors in concentration determination resulted from different matrix effects.

Portable laser-induced breakdown spectroscopy (LIES); Chemometric analysis; Hazardous elements (HEs); Suspended particulates matter (SPM); Storm event;SURFACE BOTTOM SEDIMENTS; SPECTROSCOPY; CALCIUM; FLUXES; METALS; LIBS

The main objective of this work is to provide researchers with a fast methodology of analysis capable to assess water quality in an urban river catchment during extreme rainfall events without previous elemental quantification. The analytical methodology combines measurement with portable laser-induced breakdown spectroscopy (LIBS) and direct chemometric treatment of the LIBS spectra. With this aim, suspended particular matter (SPM) samples were collected during five storm events, every 2 h with an automatic water sampler in three control points (gauging stations) of an urban river (Deba River, Basque Country, Spain). SPM samples were analyzed in situ by a handheld laser induced breakdown spectrometry (HH-LIBS), and the complete LIBS spectra were statistically analyzed by Principal Component Analysis (PCA) after outlier identification by k-nearest neighbors (kNN). The PCA results showed differentiation between monitored localizations and the period when the storm event occurred. It was also possible to identify important discriminant variables, some of them corresponding to hazardous elements such as Pb, Cr, Ni, and Cu. Basing on the correlation between variables, it was possible to identify their sources (urban/municipal contamination, anthropogenic activities, etc.). Moreover, thanks to this methodology, it was possible to predict the mobilization of hazardous elements at the end of a storm event and determine the environmental risk assessment in an urban river.

QUANTITATIVE-ANALYSIS; CARBON CONTENT; LIBS; TEMPERATURE; TRANSFORM; PLASMAS; LINES

To minimize the impact of spectral interference on laser-induced breakdown spectroscopy (LIBS) quantitative analyses, an algorithm based on wavelet transform was developed for simultaneous correction of spectral interference and continuum background. The root-mean-square error of calibration (RMSEC) of the univariate regression model for the element of interest was applied to determine the wavelet function, decomposition level, and scaling factor alpha. When the interference-free analytical lines of the elements of interest cannot be directly obtained from the measured spectra, they can be extracted from the spectra with the developed method for quantitative analysis. This method was applied for LIBS analyses of chromium (Cr), silicon (Si), titanium (Ti), and manganese (Mn) with continuum backgrounds and spectral interference in low alloy steel samples. The root-mean-square errors of cross-validation (RMSECV) of elements Cr, Si, Ti, and Mn were 0.0295, 0.0140, 0.0183, and 0.0558 wt%, respectively. The results demonstrated that the developed algorithm contributed to accuracy improvement for LIBS quantitative analyses with the presence of spectral interference.

Laser induced breakdown spectroscopy; Explosive detection; Principal component analysis; Multivariate analysis;LIBS; CLASSIFICATION

We report the 'standoff detection' of explosives at 1 m in laboratory conditions, for the first time in India, using Laser Induced Breakdown Spectroscopycombined with multivariate analysis. The spectra of a set of five secondary explosives were recorded at a distance of 1 m from the focusing as well as collection optics. The plasma characteristics viz., plasma temperature and electron density were estimated from Boltzmann statistics and Stark broadening respectively. Plasma temperature was estimated to be of the order of (10.9 +/- 2.1) x 10(3) K and electron density of (3.9 +/- 0.5) x 10(16) cm(-3). Using a ratiometric approach, C/H and H/O ratios showed a good correlation with the actual stoichiometric ratios and a partial identification success could be achieved. Finally employing principle component analysis, an excellent classification could be attained.

COD; LIBS; Prediction model; Measurement;WASTE-WATER

Using spectroscopy sensing technology to measure water COD is the trend of development of modern environmental monitoring. Compared to the traditional chemical analysis is has the benefits of online continuous detection of environmental water samples for real-time monitoring of water COD. This paper collected real water samples, using laser-induced breakdown spectroscopy (LIBS) to obtain water samples of spectral data. Establish water sample COD quantitative prediction model combining Partial Least Squares regression (PLS) by different spectral pretreatment method, then quantitative prediction of LIES spectrum measurement method of water COD and the relevant model parameters were analyzed. Found that the baseline correction superimposed S-Golay derivative partial least-squares model had better prediction results. The determination coefficient of calibration samples were 0. 995 8, while the determination coefficient of prediction were 0. 975 3, with RMSEC of 4. 438 7 and RMSEP of 9. 733 9. The experimental results showed that spectrum sensing technology can be used in the actual environment of water COD quantitative predictive analysis, laid the theoretical foundation for the development of portable water testing equipment.

Laser-induced breakdown spectroscopy; LIBS; Variable angle; Incidence angle; Collection angle; Partial least-squares analysis; PLS;

Laser-induced breakdown spectroscopy has become a popular tool for rapid elemental analysis of geological materials. However, quantitative applications of LIBS are plagued by variability in collected spectra that cannot be attributed to differences in geochemical composition. Even under ideal laboratory conditions, variability in LIBS spectra creates a host of difficulties for quantitative analysis. This is only exacerbated during field work, when both the laser-sample distance and the angle of ablation/collection are constantly changing. A primary goal of this study is to use empirical evidence to provide a more accurate assessment of uncertainty in LIBS-derived element predictions. We hope to provide practical guidance regarding the angles of ablation and collection that can be tolerated without substantially increasing prediction uncertainty beyond that which already exists under ideal laboratory conditions. Spectra were collected from ten geochemically diverse samples at angles of ablation and collection ranging from 0 degrees to +/- 60 degrees. Ablation and collection angles were changed independently and simultaneously in order to isolate spectral changes caused by differences in ablation angle from those due to differences in collection angle. Most of the variability in atomic and continuum spectra is attributed to changes in the ablation angle, rather than the collection angle. At higher angles, the irradiance of the laser beam is lower and produces smaller, possibly less dense plasmas. Simultaneous changes in the collection angle do not appear to affect the collected spectra, possibly because smaller plasmas are still within the viewing area of the collection optics, even though this area is reduced at higher collection angles. A key observation is that changes in the magnitude of atomic and total emission are <5% and 10%, respectively, in spectra collected with the configuration that most closely resembles field measurements (W) at angles <20 degrees. In addition, variability in atomic and continuum emission is strongly dependent upon sample composition. Denser, more Fe/Mg-rich rocks exhibited much less variability with changes in ablation and collection angles than Si-rich felsic rocks. Elemental compositions of our variable angle data that were predicted using a much larger but conventionally-collected calibration suite show that accuracy generally suffers when the incidence and collection angles are high. Prediction accuracy (for measurements acquired with varying collection and ablation angles) varies from +/- 1.28-1.86 wt% for Al2O3, +/- 1.25-1.66% wt for CaO, +/- 1.90-2.21 wt% for Fe2O3T, +/- 0.76-0.94 wt% for K2O, +/- 2.85-3.61 wt% MgO, +/- 0.15-0.17 wt% for MnO, +/- 0.68-0.78 wt% for Na2O, +/- 0.33-0.42 wt% for TiO2, and +/- 2.94-4.34 wt% SiO2. The ChemCam team is using lab data acquired under normal incidence and collection angles to predict the compositions of Mars targets at varying angles. Thus, the increased errors noted in this study for high incidence angle measurements are likely similar to additional, unacknowledged errors on ChemCam results for non-normal targets analyzed on Mars. Optimal quantitative analysis of LIBS spectra must include some knowledge of the angle of ablation and collection so the approximate increase in uncertainty introduced by a departure from normal angles can be accurately reported. (C) 2017 Elsevier B.V. All rights reserved.

Laser-induced breakdown spectroscopy; Plastic; Partial least squares; Classification;IDENTIFICATION; LIBS; DISCRIMINATION; POLYMERS

The traditional ways of waste plastics processing mainly use the burning landfill, which lead to environmental pollution and the waste of resources. Waste plastic recycling is very important on the circulation economy and the sustainable development. The traditional instruments have some shortcomings in plastic classification, such as lower precision, higher cost, the influence of the sample color and a serious threat to operating personnel's health. Laser inducedbreakdown spectroscopy has many advantages, such as simultaneous multielement detection of elements, free from sample preparation, rapid and real-time analysis, slight damages to sample and no impact on the sample color. The method of Chemometrics combined with LIBS technique is applied to the plastic, which improves the accuracy of plastic classification. But at present, the classification has many problems, such as more parameters and the poor universality. Using on a self built LIBS instrument, we can study the laser energy, delay time, integration time and the angle of the optical fiber, which can achieve a better experiment condition. With the experimental platform, we analyze the 2 200 sample points and choose the partial least squares to analyze the spectral data. In order to achieve the correlation between the sample label and the data, we discuss the better ratio of the training set and validation set. The experimental results show that replacing the interference spectra, classification accuracy of all 11 plastic is increased to 100%, while the validation set's accuracy is only 99.8% and the test set is 99.09% without replacing the interference spectra. It can be seen that the laser induced breakdown spectroscopy combined with partial least squares method can be successfully used for the plastic sample classification.

laser-induced breakdown spectroscopy; fireworks; elemental analysis; multivariate analysis; environmental hazards;LANTERN FESTIVAL; AIR-QUALITY; REGRESSION; PARTICLES; EVENTS; TAIWAN; COINS

Different types of fireworks are analyzed using the laser-induced breakdown spectroscopy (LIBS) technique. The system employed for spectral acquisition consists of a Nd:YAG laser (532 nm, FWHM = 4 ns) and an Andor Mechelle ME 5000 echelle spectrometer. The presence of Ba, Ca, Mg, Fe, Na, Sr, Si, and Al is identified in the LIBS spectra of different fireworks. These elements can mix easily into the surroundings and thus pollute the environment. In combination with LIBS, multivariate statistical methods, such as principal component analysis and partial least square discriminant analysis, are employed for qualitative classification, regression, and prediction purposes. These methods show good applicability for the classification and prediction of a large data set.

laser-induced breakdown spectroscopy; phosphate rocks; organomineral fertilizers; principal components analysis; partial least squares regression;ELEMENTAL ANALYSIS; PLANT MATERIALS; SOIL; CHEMOMETRICS

A number of phosphate rocks and organomineral P fertilizers was analyzed comparatively by laser-induced breakdown spectroscopy (LIBS) in both single- and double-pulse modes associated with two chemometric methods, i.e., principal components analysis (PCA) and partial least squares regression (PLSR). PCA was demonstrated to be a valuable method for the identification of spectral differences between similar samples with only minor compositional differences. The raw and normalized LIBS spectra were able to provide effective identification and discrimination at a 95% confidence level and in good agreement with the reference concentrations. Results obtained confirm the promising potential of LIBS for the rapid classification of P fertilizers in situ.

Laser-induced breakdown spectroscopy; X-ray fluorescence; Reflectance spectroscopy; Data fusion; Mineral interpretation; Partial least squares;INDUCED BREAKDOWN SPECTROSCOPY; QUANTITATIVE MINERALOGY; RAMAN-SPECTROSCOPY; WESTERN-AUSTRALIA; WAVELENGTH; DEPOSIT

This article is extension of the earlier work (Khajehzadeh et al., 2016), where quantitative mineralogical information of slurry samples was achieved using an on-stream LIES analyzer. Despite the great advances in the analytical methods and laser-based measurement techniques, the industrial developers are still demanding novel ideas enabling differentiation between minerals having similar elemental contents such as hematite and magnetite or silicon-bearing minerals such as quartz and other mixed silica minerals since they have different flotation properties. The available analytical techniques for LIES spectral analysis (including the earlier work of this research) could not distinguish between such minerals with identical elemental contents. This work at first presents data fusion of LIES and reflectance spectroscopy and then discusses the data fusion of reflectance spectroscopy and X-ray fluorescence (XRF) measurement techniques operating on the same slurry samples. The results will show that such data integrations enable on-stream and quantitative identification of slurry mineral contents specially for hematite, magnetite, quartz and ferrorichterite which are important minerals in iron ore beneficiation.

SPATIAL CONFINEMENT; BEAM GEOMETRY; SPECTROSCOPY; IMPROVEMENT; EXPOSURE

The inevitable problems in laser induced breakdown spectroscopy are matrix effect and statistical fluctuation of the spectral signal, which can be partly avoided by utilizing a proper confined unit. The dependences of spectral signal enhancement on relative permittivity were studied by varying materials to confine the plasma, which include polytetrafluoroethylene( PTFE), nylon/dacron, silicagel, and nitrile-butadiene rubber (NBR) with the relative permittivity 2.2, similar to 3.3, 3.6, 8 similar to 13, 15 similar to 22. We found that higher relative permittivity rings induce stronger enhancement ability, which restricts the energy dissipation of plasma better and due to the reflected electromagnetic wave from the wall of different materials, the electromagnetic field of plasma can be well confined and makes the distribution of plasma more orderly. The spectral intensities of the characteristic lines Si I 243.5 nm and Si I 263.1 nm increased approximately 2 times with relative permittivity values from 2.2 to similar to 20. The size dependent enhancement of PTFE was further checked and the maximum gain was realized by using a confinement ring with a diameter size of 5 mm and a height of 3 mm (D5mmH3mm), and the rings with D2mmH1mm and D3mmH2mm also show higher enhancement factor. In view of peak shift, peak lost and accidental peaks in the obtained spectra were properly treated in data progressing; the spectral fluctuation decreased drastically for various materials with different relative permittivities as confined units, which means the core of plasma is stabilized, attributing to the confinement effect. Furthermore, the quantitative analysis in coal shows wonderful results-the prediction fitting coefficient R-2 reaches 0.98 for ash and 0.99 for both volatile and carbon. (C) 2017 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

DATA NORMALIZATION; DOMINANT FACTOR; CLASSIFICATION; MODEL; LIBS; REGRESSION; SAMPLES; STEEL; RAMAN; NIR

Laser-induced breakdown spectroscopy (LIBS) as a rapid and green method was used to detect heavy metals Cr and Pb in pork contaminated in the lab. The laser-induced plasma was generated by a Q-switched Nd:YAG laser, and the LIBS signal was collected by a spectrometer with a charge-coupled device detector. The traditional calibration curves (CC) and multivariate partial least squares (PLS) algorithm were applied and compared to validate the accuracy in predicting the content of heavy metals in samples. The results demonstrated that the correlation coefficient of CC is poor by the classical univariate calibration method, so the univariate calibration analysis cannot effectively serve the quantitative purpose in analyzing heavy metals' residue in pork with a complex matrix. The analysis accuracy was improved effectively by the PLS method, and the correlation coefficient is 0.9894 for Cr and 0.9908 for Pb. The concentration of Cr and Pb in samples from a prediction set was obtained using the PLS calibration method, and the average relative errors for the 21 samples in the prediction set are lower than 6.53% and 7.82% for Cr and Pb, respectively. The investigated results display that the matrix effect would be reduced effectively during the quantitative analysis of pork by a LIBS-combined PLS model, and the predictive accuracy would be improved greatly compared to traditional univariate analysis. (C) 2017 Optical Society of America

INDUCED BREAKDOWN SPECTROSCOPY; SPECTRUM STANDARDIZATION METHOD; PARTIAL LEAST-SQUARES; MICROCHIP LASER; CARBON ANALYSIS; MN; SPECTROMETRY; CONFINEMENT; IMPROVEMENT; REGRESSION

Laser-induced breakdown spectroscopy (LIBS) was named a future superstar. Nevertheless, in the last couple of years, several commercial instruments of LIBS and its different variants have become available on a limited scale due to the confinement of quantitative analysis. In this paper, quantitative analysis of various steel samples using a specially designed handheld mu-LIBS instrument and dominant factor based partial-least squares (PLS) regression with spectral standardization is presented. Average absolute measurement errors of 0.019, 0.039, 0.013 and 0.001% for the elements of Si, Cr, Mn and Ni, respectively, were achieved. The average relative standard deviation (RSD) for these elements was less than 5%. A linear calibration response was obtained for C which is difficult to achieve using traditional PLS and for Cu whose concentration in the samples is very low. The results are better than the results of previous LIBS studies using mu J lasers or portable handheld devices, comparable to those obtained using conventional benchtop LIBS systems and better than those of commercial handheld XRF instruments. The present work presents the best results for steel analysis using portable LIBS systems so far and shows the feasibility of the technology for real industrial applications.

INDEPENDENT COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES; NEURAL-NETWORK WNN; QUANTITATIVE-ANALYSIS; RANDOM FOREST; FLY-ASH; WAVELET-TRANSFORM; UNBURNED CARBON; SIGNAL; MODEL

The classification and identification of coal ash contributes to recycling and reuse of metallurgical waste. This work explores the combination of the laser-induced breakdown spectroscopy (LIBS) technique and independent component analysis-wavelet neural network (ICA-WNN) for the classification analysis of coal ash. A series of coal ash samples were compressed into pellets and prepared for LIBS measurements. At first, principal component analysis (PCA) was used to identify and remove abnormal spectra in order to optimize the training set for the WNN model. And then, ICA was employed to select and optimize input variables for the WNN model. The classification of coal ash was carried out by using the WNN model with optimized model parameters (the number of hidden neurons (NHN), the number of iterations (NI), the learning rate (LR) and the momentum) and input variables optimized by ICA. Under the optimized WNN model parameters, the coal ash samples for test sets were identified and classified by using WNN and artificial neural network (ANN) models, and the WNN model shows a better classification performance. It was confirmed that the LIBS technique coupled with the WNN method is a promising approach to achieve the online analysis and process control of the coal industry.

RARE-EARTH-ELEMENTS; SPACE EXPLORATION; IRON OBJECTS; STEEL WORKS; ICP-MS; LIBS; SPECTROMETRY; UNIVARIATE; SAMPLES; CURVES

Laser-induced breakdown spectroscopy (LIBS) was used for the analysis of charcoal blast furnace slags. Plasma was generated by an application of a 1064 nm wavelength Nd:YAG laser beam to the surface of pellets created from the slags. The presence of Al, Ca, Fe, K, Mg, Mn, and Si was determined by identifying their characteristic spectral signatures. Multivariate analysis was performed for the quantification of these elements. The predicted LIBS results were found in agreement with the inductively coupled plasma optical emission spectrometry analysis. The limit of detection for Al, Ca, Fe, K, Mg, Mn, and Si was calculated to be 0.10%, 0.22%, 0.02%, 0.01%, 0.01%, 0.005%, and 0.18%, respectively. (C) 2017 Optical Society of America

Spectrum Analysis [E05.1962.867]; Laser-Induced Breakdown-Spectroscopy; Tissues [A10]; Optical Tissue Differentiation and Identification;NORMAL HUMAN ENAMEL; HUMAN TOOTH ENAMEL; LINGUAL NERVE; MINERAL-COMPOSITION; FEEDBACK MECHANISM; PERIPHERAL-NERVES; CHEMICAL-ANALYSIS; LIBS PROSPECTS; SURGICAL LASER; DENTAL-CARIES

Compared to conventional techniques, Laser surgery procedures provide a number of advantages, but may be associated with an increased risk of iatrogenic damage to important anatomical structures. The type of tissue ablated in the focus spot is unknown. Laser-Induced Breakdown-Spectroscopy (LIBS) has the potential to gain information about the type of material that is being ablated by the laser beam. This may form the basis for tissue selective laser surgery. In the present study, 7 different porcine tissues (cortical and cancellous bone, nerve, mucosa, enamel, dentine and pulp) from 6 animals were analyzed for their qualitative and semiquantitative molecular composition using LIBS. The so gathered data was used to first differentiate between the soft-and hard-tissues using a Calcium-Carbon emission based classifier. The tissues were then further classified using emission-ratio based analysis, principal component analysis (PCA) and linear discriminant analysis (LDA). The relatively higher concentration of Calcium in the hard tissues allows for an accurate first differentiation of soft-and hard tissues (100% sensitivity and specificity). The ratio based statistical dif-ferentiation approach yields results in the range from 65% (enamel-dentine pair) to 100% (nerve-pulp, cancellous bone-dentine, cancellous bone-enamel pairs) sensitivity and specificity. 

dental caries; ultra-short lasers; thermal effects; atomic spectroscopy;INDUCED BREAKDOWN SPECTROSCOPY; QUANTITATIVE-ANALYSIS; REPETITION-RATE; TISSUE; FLUORESCENCE; CARIES; LIBS; BONE; IDENTIFICATION; ENAMEL

The goal of this work is to investigate the thermal effects of femtosecond laser (fs-laser) ablation for the removal of carious dental tissue. Additional studies identify different tooth tissues through femtosecond laser induced breakdown spectroscopy (fsLIBS) for the development of a feedback loop that could be utilized during ablation in a clinical setting. Scanning Election Microscope (SEM) images reveal that minimal morphological damages are incurred at repetition rates below the carbonization threshold of each tooth tissue. Thermal studies measure the temperature distribution and temperature decay during laserablation and after laser cessation, and demonstrate that repetition rates at or below 10kHz with a laser fluence of 40 J/cm(2) would inflict minimal thermal damage on the surrounding nerve tissues and provide acceptable clinical removal rates. Spectral analysis of the different tooth tissues is also conducted and differences between the visible wavelength fsLIBS spectra are evident, though more robust classification studies are needed for clinical trans-lation. These results have initiated a set of precautionary recommendations that would enable the clinician to utilize femtosecond laser ablation for the removal of carious lesions while ensuring that the solidity and utility of the tooth remain intact. 

Laser-induced breakdown spectroscopy; Matrix-match standard; Principal component analysis; Heat-treated natural zeolite; Sediment samples;HEAVY-METALS; ELEMENTAL ANALYSIS; LIBS LIMIT; SOILS; SAMPLES; SPECTROMETRY; ALLOYS; TOOL

The dependence of laser-induced breakdown spectroscopy (LIBS) to the matrix of the sample remains an important consideration in performing quantitative analysis. In this study, a new matrix was introduced in the preparation of solid powder calibration curves. Heat-treated natural zeolite and KBr were mixed separately into high purity metal powders to generate calibration curves using a univariate approach. A LIBS technique was used in the detection and quantitative analysis of Cr, Cu and Pb in river sediment samples. The relative percent difference (RPD) was calculated to describe the variability measurements made using ICP/OES and LIBS as well as to evaluate the accuracy of the method. Calculated limits of detection in the matrices prepared were comparable with literature values and ranged from 0.41 to 6.1 ppm. The resulting metal concentrations indicate that the natural zeolite matrix were closer to the reference values compared to the KBr matrix. By employing principal component analysis (PCA), heat treated zeolite was shown as a potential diluent or binder for generating calibration curves and could provide matrix-matched standards in identification of target metals from unknown sediment samples. The natural zeolite appeared to closely simulate the ablation behavior and property of the samples, and it is found to be a potential suitable matrix for the quantitative LIBS analysis of sediments. (C) 2017 Elsevier B.V. All rights reserved.

BUILDING EXPERT-SYSTEMS; FUEL PROPERTIES; SPECTROSCOPY; MODEL

A quiet but interesting trend has been occurring in material analysis, coincident with the rise of artificial intelligence (Al) and so-called ""deep"" machine learning methods. Astute spectroscopists have always known that there is more information in the spectra that they obtain than simply the molecular or atomic peaks that are directly measured. Particularly with methods such as infrared, Raman, and laser-induced breakdown spectroscopy (LIBS), the spectral background contains a wealth of information about the sample, and analytical combinations of the peaks can provide material properties. Traditionally, such analytical combinations of peaks were performed explicitly by analysts, but now information about material properties embedded in the spectra can be derived implicitly by Al and machine learning algorithms. This column introduces these ideas and touches on recent results indicative of what more may be coming in this direction.

Laser-induced breakdown spectroscopy; Proximate analysis of coal; Spectral fitting; Support vector machine;LIBS; SYSTEM

Online accurate proximate analysis of coal is vitally important to the optimization of industrial production and reduction in coal consumption. However, due to the ""matrix effect"" caused by the complex and diverse coal species in China, the measurement accuracy needs to be improved by using laser-inducedbreakdown spectroscopy (LIBS). In our experiment, both the spectral pretreatment method and the calibration model for the conversion of laser induced coal plasma spectra to the coal proximate analysis results were optimized. Experimental results showed that, compared with the traditional method, the proposed single or multiple-peak Lorentzian spectral fitting for spectral line intensity calculation reduced the mean RSD from 12. 1% to 9. 7%. For kernel function parameters optimization, the mean absolute error (MAE) of the particle swarm optimization (PSO) was smaller than that of the grid parameter (Grid) and the genetic algorithm (GA). The root mean square error (RMSEP) of support vector machine (SVM) regression model based on PSO parameter optimization was less than that of partial least squares regression (PLS). By combining the single- or multiple-peak Lorentzian spectral fitting method with the PSO based SVM for regression modeling, the average absolute errors (AAE) of predicted proximate analysis results were certified to be: 1. 37% for coal ash content of 16%similar to 30%, 1. 77% for coal ash content of 30% or more, 0. 65 MJ . kg(-1) for calorific value of 9 similar to 24 MJ . kg(-1), 1. 09% for volatile matter of 20% or less, and 1. 02% for volatile matter of 20% or more.

LIBS stratigraphy; Galvanic coatings; Ablation rate; Plasma shielding; Light-plasma interaction; Depth profiling;INDUCED BREAKDOWN SPECTROMETRY; ION-BEAM ANALYSIS; PULSED-LASER DEPOSITION; REDUCED PRESSURE; JEWELRY INDUSTRY; PURE METALS; ABLATION; SPECTROSCOPY; FILMS; THICKNESS

Laser-induced depth profiling was applied to the investigation of galvanised steel sheets as a typical modern multi-layer coating system for environmental corrosion protection. The samples were ablated stepwise by the use of two different wavelengths of a frequency-converted Nd:YAG-laser, 266nm and 532nm, with a pulse duration of tau = 4ns at fluences ranging from F=50 to 250 J cm(-2). The emission light of the resulting plasma was analysed as a function of both penetration depth and elemental spectrum in terms of linear correlation analysis. Elemental depth profiles were calculated and compared to EDX-cross sections of the cut sample. A proven mathematical algorithm designed for the reconstruction of layer structures from distorted emission traces caused by the Gaussian ablation profile can even resolve thin intermediate layers in terms of depth and thickness. The obtained results were compared to a purely thermally controlled ablation model. Thereby light-plasma coupling is suggested to be a possible cause of deviations in the ablation behaviour of Al. The average ablation rate h as a function of fluence F for Ni ranges from 1 to 3.5 mu m/pulse for lambda = 266nm as well as for lambda =532nm. In contrast, the range of h for Al differs from 2 to 4 mu m/pulse for lambda = 532 nm and 4 to 8 mu m/pulse for lambda = 266 nm in the exact same fluence range on the exact same sample. (C) 2016 Elsevier B.V. All rights reserved.

Laser Induced Breakdown Spectroscopy; Milk adulteration; Melamine; Neural networks;NEURAL-NETWORKS; POWDER ADULTERATION; VEGETABLE-OILS; BETA-CAROTENE; DISCRIMINATION; CLASSIFICATION; LYCOPENE; FATS; MS

The present work focuses on the development of a fast and cost effective method based on Laser Induced Breakdown Spectroscopy (LIBS) to the quality control, traceability and detection of adulteration in milk. Two adulteration cases have been studied; a qualitative analysis for the discrimination between different milk blends and quantification of melamine in adulterated toddler milk powder. Principal Component Analysis (PCA) and neural networks (NN) have been used to analyze LIBS spectra obtaining a correct classification rate of 98% with a 100% of robustness. For the quantification of melamine, two methodologies have been developed; univariate analysis using CN emission band and multivariate calibration NN model obtaining correlation coefficient (R-2) values of 0.982 and 0.999 respectively. The results of the use of LIBS technique coupled with chemometric analysis are discussed in terms of its potential use in the food industry to perform the quality control of this dairy product. (C) 2017 Elsevier Ltd. All rights reserved.

SUPPORT VECTOR MACHINES; ELEMENTAL ANALYSIS; IRON-ORES; CLASSIFICATION; SPECTROMETRY; SAMPLES; PLASMA; INDUSTRY; ALLOYS; MODEL

The basicity of sintered ore, which is related to the melting point of the sinter, is vital to ore mining and blastfurnace smelting. Laser-induced breakdownspectroscopy (LIBS) with random forest regression (RFR) has been applied for measuring the basicity of sintered ore, which can be defined by the concentrations of oxides: CaO, SiO2, Al2O3 and MgO. In this work, thirty sintered ore samples are used, of which twenty samples are used for the calibration set to construct the random forest regression (RFR) calibration model for the above-mentioned oxides and ten samples are used for the test set. The characteristic lines of the main components in the sintered ore are identified using the National Institute of Standards and Technology (NIST) database. Two model parameters (the number of decision trees -ntree and the number of random variables - m(try)) of the RFR were optimized by out-of-bag (OOB) error estimation for improving the predictive accuracy of the RFR model. The RFR model was applied to sample measurements and the results were compared with partial least squares regression (PLSR) models. The RFR model has shown better predictive capabilities than the PLSR model. In order to verify the stability of the RFR model, fifty measurements were made and the relative standard deviation (RSD) of the data is between 0.27% and 0.59%. Therefore, LIBS combined with RFR could be a promising method for realtime online, rapid analysis in mining and mineral processing industries.

INDUCED BREAKDOWN SPECTROSCOPY; OPTICAL-EMISSION-SPECTROMETRY; TRACE-ELEMENTS; MICRONUTRIENTS; MEMBRANE; CADMIUM; WATER

A novel calibration method was developed for the determination of macronutrients in suspension fertilizer samples by laser-induced breakdown spectroscopy(LIBS). The fertilizer samples were mixed with 10% m/v polyvinyl alcohol (PVA) and dried for approximately 2 h at 50 degrees C, resulting in a solid polymer film with the immobilized liquid sample. This study describes the use of LIBS and 12 normalization strategies to determine Cu, K, Mg, Mn and Zn as well as As, B, Ca, Cd, Cr, Fe, Na, P and Pb in suspension fertilizers by inductively coupled plasma optical emission spectrometry (ICP OES) and inductively coupled plasma mass spectrometry (ICP-MS) analyses. LIBS equipment parameters were chosen following optimization with a Doehlert design. The results clearly demonstrate a good agreement between the LIBS predicted concentrations and the reference concentrations determined by ICP OES after microwave-assisted acid digestion (AOAC 2006.03 Official Method). Correlations of R-2 = 0.9958, 0.9489, 0.9992, 0.9968 and 0.9809 for Cu, K, Mn, Mg and Zn, respectively, were obtained. Significant levels of potentially toxic elements such as 10 mg L-1 As, 4.0 mg L-1 Cd, 8.0 mg L-1 Cr and 20 mg L-1 Pb were detected after ICP-MS analysis. The results with low standard errors of prediction for LIBS (from 0.02 to 0.06%) indicate that this proposed suspension sample preparation procedure exhibits significant potential for the development and implementation of methods for determining essential nutrients in suspension fertilizers and other samples for which mineralization is difficult.

laser-induced breakdown spectroscopy; soil; magnesium; nonlinear multivariate calibration;RARE-EARTH-ELEMENTS; SAMPLES; LIBS; QUANTIFICATION; DIAGNOSIS; METALS

Laser-induced breakdown spectroscopy (LIBS) coupled with the nonlinear multivariate regression method was applied to analyze magnesium (Mg) contents in soil. The plasma was generated using a 100 mJ Nd:YAG pulsed laser, and the spectra were acquired using a multi-channel spectrometer integrated with a CCD detector. The line at 383.8 nm was selected as the analysis line for Mg. The calibration model between the intensity of characteristic line and the concentration of Mg was constructed. The traditional calibration curve showed that the concentration of Mg was not only related to the line intensity of itself, but also to other elements in soil. The intensity of characteristic lines for Mg (Mg I 383.8 nm), manganese (Mn) (Mn I 403.1 nm), and iron (Fe) (Fe I 407.2 nm) were used as input data for nonlinear multivariate calculation. According to the results of nonlinear regression, the ternary nonlinear regression was the most appropriate of the studied models. A good agreement was observed between the actual concentration provided by inductively coupled plasma mass spectrometry (ICP-MS) and the predicted value obtained using the nonlinear multivariate regression model. The correlation coefficient between predicted concentration and the measured value was 0.987, while the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were reduced to 0.017% and 0.014%, respectively. The ratio of the standard deviation of the validation to the RMSEP increased to 8.79, and the relative error was below 1.21% for nine validation samples. This indicated that the multivariate model can obtain better predicted accuracy than the calibration curve. These results also suggest that the LIBS technique is a powerful tool for analyzing the micro-nutrient elements in soil by selecting calibration and validation samples with similar matrix composition.

Laser-induced breakdown spectroscopy; Soil; Calibration curve; Chemometrics analysis;LIBS; CALIBRATION

Abundance or deficiency of soil elements is an expression of soil fertility. Rapid detection of elements in soil is a key point of front information acquirement tools in precision agriculture, and it also provides a theoretical basis for pollution prevention of soil heavy metal and sustainable development of agriculture. This research focused on using laser-induced breakdown spectroscopy LIBS) technique combined with calibration curve and chemometrics method to conduct the simultaneous quantitative analysis of multi-elements (Al, Fe, Mg, Ca, Na and K) in soil. First of all, five certified reference materials (CRM) of soil numbered GBW07446, GBW07447, GBW07454, GBW07455, GBW07456 were ablated by a laboratorial LIBS setup in air. 50 LIBS spectra of each type of soil were averaged to reduce the error in experiment process. By integrating the acquired LIBS emission spectra and atomic spectra database from national institute of standards and technology (NIST), analytical spectral lines and corresponding spectral regions were identified. Then, calibration curves of the intensity of a peak and integrated intensity of a peak or several peaks (peak area) coupling with the element content s were fitted. The results indicated that the linear relation from the calibration curves fitted by peak areas and element contents were superior to the calibration curves fitted using intensity of a peak and element contents (except the Fe). Meanwhile, partial least-squares regression (PLSR) was employed to build the quantitative model by using the selected spectral regions and corresponding element contents, which offered a promising result with relatively high RP and showed more advantages than the calibration curve method. The approach revealed that LIES technology combined with chemometrics methods displayed a bight prospect in the field of spectrochemical analysis. The achievements of the research not only provide a guide for detecting soil nutrient spatial distribution and precision fertilization technique, but also lay a theoretical foundation for developing the portable LIBS detector used in the field.

Laser-induced breakdown spectroscopy; Vector space model; Geological cutting; Identification;SAMPLES

Vector Space Model (VSM) was originally used in document retrieval. It is characterized by extracting the texts from the document and converting the document into a text vector space. VSM compares the similarity between document text vectors and text retrieval text vectors. The document retrieval is accomplished according to the similarity based on the nearest template principle of template matching in pattern recognition. This article applied such principle into the identification of samples based on the characteristic of LIES spectrum. To create the database of characteristic peaks of training dataset, we obtained the spectra template of all kinds of samples from the training dataset and extracted the wavelength and intensity of peaks from the spectra template. In another hand, to create the database of characteristic peak vectors for all the training samples, we calculated the spectral characteristic peak weight and converted the spectra to peak vector space. The characteristic peak vector of the test data set was obtained by the same way. The cosine value between the characteristic peak vector of test data and every characteristic peak vector in the database were calculated and the maximum cosine was taken as the identification result. Geological cuttings, the research subject, were identified by the VSM in this paper. The result demonstrated that the VSM could rapidly identify the spectra from 4 kinds of geological cuttings' LIES spectral and the correct identification rate was 100% after the spectra of the test dataset were averaged. The proposed LIES spectral identification method based on VSM can be expanded to the identification of other spectral data.

LIBS; Trace-element premixes; Univariate analysis; Multivariate analysis; PLS;SPECTROCHEMICAL ANALYSIS; LIBS; ADULTERATION; COPPER; MILK; BEEF

Infant formula is a human milk substitute generally based upon fortified cow milk components. In order to mimic the composition of breast milk, trace elements such as copper, iron and zinc are usually added in a single operation using a premix. The correct-addition of premixes must be verified to ensure that the target levels in infant formulae are achieved. In this study, a laser-induced breakdown spectroscopy (LIBS) system was assessed as a fast validation tool for trace element premixes. LIBS is a promising emission spectroscopic technique for elemental analysis, which offers real-time analyses, little to no sample preparation and ease of use. LIBS was employed for copper and iron determinations of premix samples ranging approximately from 0 to 120 mg/kg Cu/1640 mg/kg Fe. LIES spectra are affected by several parameters, hindering subsequent quantitative analyses. This work aimed at testing three matrix-matched calibration approaches (simple-linear regression, multi-linear regression and partial least squares regression (PLS)) as means for precision and accuracy enhancement of LIBS quantitative analysis. All calibration models were first developed using a training set and then validated with an independent test set. PLS yielded the best results. For instance, the PLS model for copper provided a coefficient of determination (R-2) of 0.995 and a root mean square error of prediction (RMSEP) of 14 mg/kg. Furthermore, LIBS was employed to penetrate through the samples by repetitively measuring the same spot. Consequently, LIBS spectra can be obtained as a function of sample layers. This information was used to explore whether measuring deeper into the sample could reduce possible surface-contaminant effects and provide better quantifications. (C) 2017 Elsevier B.V. All rights reserved.

Coffee global quality; BGS; Classes discrimination; NIRS; LIBS;NEAR-INFRARED SPECTROSCOPY; MARKER COMPOUNDS; CUP QUALITY; BEANS; DEFECTS

Coffee is an important commodity and a very popular beverage around the world. Its economic value as well as beverage quality are strongly dependent of the quality of beans. The presence of defective beans in coffee blends has caused a negative impact on the beverage Global Quality (GQ) assessed by cupping tests. The main defective beans observed in the productive chain has been those Blacks, Greens and Sours (BGS). Chemical composition of BGS has a damaging impact on beverage GQ That is why analytical tools are needed for monitoring and controlling the GQ in coffee agro-industry. Near Infrared Spectroscopy (NIRS) has been successfully applied for assessment of coffee quality. Another potential technique for direct, clean and fast measurement of coffee GQ is Laser Induced Breakdown Spectroscopy (LIBS). Elements and diatomic molecules commonly present in organic compounds (structure) can be assessed by using LIBS. In this article is reported an evaluation of LIBS for the main interferents of GQ (BGS defects). Results confirm the great potential of LIBS for discriminating good beans from those with BGS defects by using emission lines of C, CN, C-2 and N. Most importantly, some emission lines presented strong linear correlation (r > 0.9) with NIRS absorption bands assigned to proteins, lipids, sugar and carboxylic adds, suggesting LIBS potential to estimate these compounds in unroasted and ground coffee samples. (C) 2017 Elsevier B.V. All rights reserved.

Wines; LIBS; Classification; Principal component analysis; Random forest; Matrix effect;LIQUID-LIQUID MICROEXTRACTION; ICP-MS; GEOGRAPHICAL ORIGIN; PROTECTED DESIGNATION; MULTIELEMENT ANALYSIS; SINGLE-PULSE; IDENTIFICATION; SAMPLES; LIBS; METALS

Laser-induced breakdown spectroscopy (LIBS) has been applied to classify French wines according to their production regions. The use of the surface-assisted (or surface-enhanced) sample preparation method enabled a sub-ppm limit of detection (LOD), which led to the detection and identification of at least 22 metal and nonmetal elements in a typical wine sample including majors, minors and traces. An ensemble of 29 bottles of French wines, either red or white wines, from five production regions, Alsace, Bourgogne, Beaujolais, Bordeaux and Languedoc, was analyzed together with a wine from California, considered as an outlier. A non-supervised classification model based on principal component analysis (PCA) was first developed for the classification. The results showed a limited separation power of the model, which however allowed, in a step by step approach, to understand the physical reasons behind each step of sample separation and especially to observe the influence of the matrix effect in the sample classification. A supervised classification model was then developed based on random forest (RF), which is in addition a nonlinear algorithm. The obtained classification results were satisfactory with, when the parameters of the model were optimized, a classification accuracy of 100% for the tested samples. We especially discuss in the paper, the effect of spectrum normalization with an internal reference, the choice of input variables for the classification models and the optimization of parameters for the developed classification models. (C) 2017 Elsevier B.V. All rights reserved.

Laser-induced breakdown spectroscopy; LIBS; multivariate analysis; soft independent modeling of class analogy; SIMCA; partial least squares discriminant analysis; PLS-DA; edible salt;INDUCED BREAKDOWN SPECTROSCOPY; SEA SALTS; LIBS; IDENTIFICATION; COMPACT; ORIGIN

We report soft independent modeling of class analogy (SIMCA) analysis of laser-induced plasma emission spectra of edible salts from 12 different geographical origins for their classification model. The spectra were recorded by using a simple laser-induced breakdown spectroscopy (LIBS) device. Each class was modeled by principal component analysis (PCA) of the LIBS spectra. For the classification of a separate test data set, the SIMCA model showed 97% accuracy in classification. An additional insight could be obtained by comparing the SIMCA classification result with that of partial least squares discriminant analysis (PLS-DA). Different from SIMCA, the PLS-DA classification accuracy seems to be sensitive to addition of new sample classes to the whole data set. This indicates that the individual modeling approach (SIMCA) can be an alternative to global modeling (PLS-DA), particularly for the classification problems with a relatively large number of sample classes.

IWDTF; LIBS; Animal manure composts; Cu; Zn;HEAVY-METAL CONTENTS; MULTIELEMENT ANALYSIS; ACCURACY IMPROVEMENT; MINERAL FERTILIZERS; LIVESTOCK FEEDS; TRACE-ELEMENTS; SPECTROMETRY; LIBS; CONTAMINANTS; TRANSFORM

Spectral denoising offers a basis for effective and accurate quantitative modeling of laser-induced breakdown spectroscopy (LIBS). To provide a solution for the defects of the traditional hard threshold function (HTF) and the soft threshold function (STF) for wavelet denoising methods, an improved wavelet dual threshold function (IWDTF) has been developed. Simulation analyses proved that the IWDTF could provide abetter denoising performance than the traditional functions. The IWDTF was applied to LIBS denoising and quantitative modeling of the Cu and Zn contents in representative Chinese aerobic composting samples. The results showed that the best spectral denoising effects for the Cu and Zn models were achieved when the adjustment parameters k(0), a, and theta were 0.9, 0.7 x lambda(1), and 0.1 x pi, and 0.2, 0.2 x lambda(1), and 0.1 x pi, respectively, and corresponding models' performances for Cu and Zn were both greatly improved, yielding R-p(2) of 0.9807 and 0.9177, RMSEP of 67.49 mg/kg and 84.92 mg/kg, and RPD of 3.12 and 3.07, respectively. (C) 2017 Published by Elsevier B.V.

Laser induced breakdown spectroscopy (LIBS); Quantitative analysis; Matrix effect; Self-absorption;INDUCED PLASMA SPECTROSCOPY; ARTIFICIAL NEURAL-NETWORK; LEAST-SQUARES REGRESSION; ELEMENTAL ANALYSIS; MULTIVARIATE-ANALYSIS; EMISSION-SPECTROSCOPY; SPECTRAL-LINE; SOIL SAMPLES; IRON-ORE; CHEMCAM INSTRUMENT

This paper reviews methods to compensate for matrix effects and self-absorption during quantitative analysis of compositions of solids measured using LaserInduced Breakdown Spectroscopy (LIBS) and their applications to in-situ analysis. Methods to reduce matrix and self-absorption effects on calibration curves are first introduced. The conditions where calibration curves are applicable to quantification of compositions of solid samples and their limitations are discussed. While calibration-free LIBS (CF-LIBS), which corrects matrix effects theoretically based on the Boltzmann distribution law and Saha equation, has been applied in a number of studies, requirements need to be satisfied for the calculation of chemical compositions to be valid. Also, peaks of all elements contained in the target need to be detected, which is a bottleneck for in-situ analysis of unknown materials. Multivariate analysis techniques are gaining momentum in LIBS analysis. Among the available techniques, principal component regression (PCR) analysis and partial least squares (PLS) regression analysis, which can extract related information to compositions from all spectral data, are widely established methods and have been applied to various fields including in-situ applications in air and for planetary explorations. Artificial neural networks (ANNs), where non-linear effects can be modelled, have also been investigated as a quantitative method and their applications are introduced. The ability to make quantitative estimates based on LIBS signals is seen as a key element for the technique to gain wider acceptance as an analytical method, especially in in-situ applications. In order to accelerate this process, it is recommended that the accuracy should be described using common figures of merit which express the overall normalised accuracy, such as the normalised root mean square errors (NRMSEs), when comparing the accuracy obtained from different setups and analytical methods. (C) 2017 The Authors. Published by Elsevier B.V.

MULTIVARIATE-ANALYSIS; WOOD

The black cottonwood poplar (Populus trichocarpa) leaf ionome (inorganic trace elements and mineral nutrients) is an important aspect for determining the physiological and developmental processes contributing to biomass production. A number of techniques are used to measure the ionome, yet characterizing the leaf spatial heterogeneity remains a challenge, especially in solid samples. Laser-induced breakdown spectroscopy (LIBS) has been used to determine the elemental composition of leaves and is able to raster across solid matrixes at 10 pm resolution. Here, we evaluate the use of LIBS for solid sample leaf elemental characterization in relation to neutron activation. In fact, neutron activation analysis is a laboratory-based technique which is used by the National Institute of Standards and Technology (NIST) to certify trace elements in candidate reference materials including plant leaf matrices. Introduction to the techniques used in this research has been presented in this manuscript Neutron activation analysis (NAA) data has been correlated to the LIBS spectra to achieve quantification of the elements or ions present within poplar leaves. The regression coefficients of calibration and validation using multivariate analysis (MVA) methodology for six out of seven elements have been determined and vary between 0.810 and 0.998. LIBS and NM data has been presented for the elements such as, calcium, magnesium, manganese, aluminum, copper, and potassium. Chlorine was also detected but it did not show good correlation between the LIBS and NAA techniques. This research shows that LIBS can be used as a fast, high-spatial resolution technique to quantify elements as part of large-scale field phenotyping projects. (C) 2017 Elsevier B.V. All rights reserved.

Laser-induced breakdown spectroscopy; SF6 oxygen concentration; Wavelet transform; Partial least square; Quantitative analysis;DISCHARGE

Sulfur hexafluoride (SF6) is widely used in gas-insulated high-voltage equipments due to its excellent insulation and arc extinguishing performance. However, when impurities such as trace water and oxygen are present in SF6, the by-products, which are formed due to equipment faults and partial discharge, can react with these impuriti to form stable sulfur oxyfluorides. The equipment insulation performance can be degraded significantly by those stable sulfur oxyfluorides, causing threat to the safety of the power system. The detection and analysis of the impurities such as water, oxygen and decompositions in SF6 are therefore of great importance. In this paper, laser-induced breakdown spectroscopy is employed to measure trace O in SF6. CaF2 is used as window material to solve the degradation of the excitation energy caused by the corrosion of the window material by SF6 decompositions, to eliminate the pollution problem caused by the reaction between the window material and the breakdown products of SF6 gas, and to reduce the change of plasma state caused by the change of excitation condition. By correcting the spectral baseline with an iterative wavelet transform and suppressing the noise with a wavelet transform with soft thresholding, a limit of detection of 38ppm is achieved from the experimental calibration curve. Furthermore, a quantitative model based on partial least squares (PLS) is developed to achieve better stability and precision.

Laser-induced breakdown spectroscopy (LIES); Soil; Heavy metal; Chemometrics methods; Quantitative analysis;NEURAL-NETWORK; LIBS

A large number of farm lands are contaminated by heavy metals in the process of industrialization and urbanization. Precise detection of heavy metals in soil offers valid reference for prevention and recovery of heavy metals in the field. In this research, Laser induced breakdown spectroscopy (LIBS) and chemometrics methods were employed to conduct quantitative analysis of heavy metals Pb and Cd in soil. Based on the pollution extent, soil samples with 15 concentration gradients of Pb and Cd were manually made up. Then, the LIBS emission lines of all soil samples were collected firstly. In order to eliminate errors and noise of spectral data, preprocessing methods called removal of abnormal data and normalization were used. Then, characteristic lines and spectral regions of Pb and Cd were determined based on our LIES spectra and Atomic Spectra Database (ASD) of National Institute of Standards and Technology (NIST). Quantity regression models based on multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN) were set up and their results were compared. As a result, models based on non-linear methods (LS-SVM and BP-ANN) offered a promising results than the linear methods of MLR and PLSR. The probable reason was that non-linear methods had an advantage to deal with matrix effects of soil automatically. The results indicated that LIBS coupled with multiple chemometrics methods provided a brand-new analysis approach for heavy metals accurate detection in soil and it could be considered as an effective theoretical foundation of making protection and recovery decision for soil contaminated by heavy metals.

laser-induced breakdown spectroscopy; real-time chemical analysis; geochemical fingerprinting; stratigraphic correlation; provenance determination; mineral exploration;PARTIAL LEAST-SQUARES; DIAMOND EXPLORATION; GOLD MINERALIZATION; YUKON-TERRITORY; SOLID-SOLUTIONS; TRACE-ELEMENTS; RAPID ANALYSIS; ICP-MS; MINERALS; GARNET

A broad suite of geological materials was studied a using a handheld laser-induced breakdown spectroscopy (LIBS) instrument. Because LIBS is simultaneously sensitive to all elements, the full broadband emission spectrum recorded from a single laser shot provides a chemical fingerprint' of any material - solid, liquid or gas. The distinguishing chemical characteristics of the samples analysed were identified through principal component analysis (PCA), which demonstrates how this technique for statistical analysis can be used to identify spectral differences between similar sample types based on minor and trace constituents. Partial least squares discriminant analysis (PLSDA) was used to distinguish and classify the materials, with excellent discrimination achieved for all sample types. This study illustrates through four examples (carbonate minerals and rocks, the oxide mineral pair columbite-tantalite, the silicate mineral garnet and native gold) how portable, handheld LIBS analysers can be used for real-time chemical analysis under simulated field conditions for element or mineral identification, plus such applications as stratigraphic correlation, provenance determination and natural resource exploration.

Polyacrylate polymer; Fourier transform infrared photoacoustic spectroscopy; Laser-induced breakdown spectroscopy; Principal component analysis;FILM FORMATION; FERTILIZER; LATEX; IDENTIFICATION; COMPLEXES

Polyacrylate polymer (PA) has been widely applied in coating products for decades. Recently, it has been used in controlled-release fertilizers. Nano Fe-III-tannic acid modified PA (PA-Fe) provides a better nutrient controlled release performance than conventional PA. In this work, a preliminary database of molecular and elemental information about the polymer was obtained using FTIR-PAS (Fourier transform infrared photoacoustic spectroscopy) and LIBS (laser-induced breakdown spectroscopy), respectively. The PA-Fe polymer contained more hydrophobic groups (-CH3) and fewer hydrophilic groups (-COOR, -COOH) than PA. More elements were detected for PA-Fe than PA. LIBS was useful to identify and classify PA and PA-Fe samples using principal component analysis. The combination of spectroscopic results and a film formation process model explained the lower nutrient release rate of PA-Fe. These results showed the strong analytical capabilities of FTIR-PAS combined with LIBS for identifying and characterizing modified PA.

toxic heavy metal; laser-induced breakdown spectroscopy (LIBS); Tegillarca granosa; discrimination analysis; wavelet transform algorithm (WTA);INFRARED-SPECTROSCOPY; CULTURAL-HERITAGE; TRACE-METALS; LABEO-ROHITA; IDENTIFICATION; LIBS; CADMIUM; SAMPLES; MODEL; ZINC

Tegillarca granosa samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93.3% discrimination accuracy among those classifiers. Besides, the extracted characteristic information was used to reconstruct the original spectra by inverse WTA, and the corresponding attribution of the reconstructed spectra was then discussed. This work indicates that the healthy shellfish samples of Tegillarca granosa could be distinguished from the toxic heavy-metal-contaminated ones by pattern recognition analysis combined with LIBS technology, which only requires minimal pretreatments.

Laser-induced breakdown spectroscopy; Uranium; Phase identification;EMISSION; FEMTOSECOND; STRENGTHS; SPECTRA; PLASMA; LIBS

Nuclear forensics goals for characterizing samples of interest include qualitative and quantitative analysis of major and trace elements, isotopic analysis, phase identification, and physical analysis. These samples may include uranium oxides UO2, U3O8, and UO3, which play an important role in the front end of the nuclear fuel cycle, from mining to fuel fabrication. The focus of this study is to compare the ratios of the intensities of uranium and oxygen emission lines which can be used to distinguish between different uranium oxide materials using Laser-Induced Breakdown Spectroscopy (LIBS). Measurements at varying laser powers were made under an argon atmosphere at 585 Torr to ensure the oxygen emission intensity was originating from the sample, and not from the atmosphere. Fifteen uranium emission lines were used to compare experimental results with theoretical calculations in order to determine the plasma conditions. Using a laser energy of 26 mJ, the uranium lines 591.539 and 682.692 nm provide the highest degree of discrimination between the uranium oxides. The study presented here suggests that LIBS is useful for discriminating uranium oxide phases, UO2, U3O8, and UO3. (C) 2017 Elsevier B.V. All rights reserved.

LIBS; Minced beef; Rubidium; PLSR; Chemometrics;MEAT; CLASSIFICATION; LIBS

This study evaluates the potential of laser induced breakdown spectroscopy (LIBS) coupled with chemometrics to develop a quantification model for rubidium (Rb) in minced beef. A LIBSCAN 150 system was used to collect LIBS spectra of minced beef samples. Beef liver was used to spike the Rb levels in minced beef. All samples were dried, powdered and pelleted using a hydraulic press. Measurements were conducted by scanning 100 different locations with an automated XYZ sample chamber. Partial least squares regression (PLSR) was used to develop the calibration model, yielding a calibration coefficient of determination (R-c(2)) of 0.99 and a root mean square error of calibration (RMSEC) of 0.05 ppm. The model also showed good results with leave-one-out cross validation, yielding a cross-validation coefficient of determination (R-c(2)) of 0.90 and a root mean square error of cross-validation (RMSECV) of 0.22 ppm. The current study shows the potential of LIBS as a rapid analysis tool for the meat processing industry.

PLASMA SPECTROSCOPY; ELEMENTAL ANALYSIS; LIBS

Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction, set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.

Laser induced breakdown spectroscopy; Coffee beans; Caffeine; Partial least squares; BP neural network;LIBS

The feasibility of fast detection of caffeine content in coffee beans based on laser induced breakdown spectroscopy (LIES) combined with chemometrics methods was studied. The coffee beans were grinded. 0.5 g of powerd material was transformed into a disk by manual tableting machine. 60 disks of coffee bean material were prepared for LIBS data acquiring. The samples were pretreated by acid wet digestion, and actual caffeine content of each sample was obtained by automic absorption spectrometer (AAS). Baseline correction was applied on the original spectral data to eliminate the negative values. Wavelet transform (WT) was used to reduce the noise, wavelet basis function is Daubechies 5 (db5) and decomposition level is 10. Normalization were employed to deal with variations caused by matrix effects and experimental conditions. The partial least squares (PLS) model on full data appeared to over-fitting. Regression coefficients and principal component analysis (PCA) were used to select characteristic variables, respectively. PLS models and back propagation (BP) neural network model were built by the variables selected. In the PLS model on the variables selected by regression coefficients, correlation coefficient of calibration set (R-c) was 0.96, correlation coefficient of prediction set (R-p) was 0.91. In the PLS model on the variables selected by PCA, R-c=0.94, R-p=0.90. In the BP neural network model on variables selected by PCA, R-c=0.96, R-p=0.96. The characteristic variables selected by two methods correspond to C, H, O, N, Na, Mg, Ca, Fe, Mn. The PLS models on the variables selected by regression coefficients and PCA both performed well on prediction samples. It demonstrated the certain relationship existing between the elements and caffeine content and the selected variables were effective. But the precise relationship bwtween C, H, O, N, Na, Mg, Ca, Fe, Mn and caffeine content needs further study. The BP neural network model on variables selected by PCA performed better than the PLS model, which demonstrated the selected variables were suitable for different modeling methods. The study showed LIBS could be applied to fast determination of caffeine content within coffee bean combined with chemometrics methods. The method of caffeine content detection presented by this study is innovative.

Laser-induced breakdown spectroscopy; Characteristic spectral lines of non-metallic elements; Support vector machine; Plastics identification;ARTIFICIAL NEURAL-NETWORKS; INDUCED PLASMA; EMISSION; POLYMERS

Laser-induced breakdown spectroscopy (LIPS) combined with support vector machine (SVM) was adopted to identify 20 kinds of different colored industrial plastics from different manufacturers in open air. The experimental parameters of spectral acquisition were optimized firstly. 100 spectra recorded under optimum conditions were randomly and equally divided into training set and test set. 6 non-metallic characteristic spectral lines were used to avoid the interference with metallic lines. And the training time of SVM model was reduced. The results show that 996 of 1000 test spectra were identified correctly and the average classification accuracy is reached to 99.6%. The classification efficiency is improved with 6 non-metallic characteristic spectral lines. The research demonstrates that, when fewer of major non-metallic characteristic spectral lines are used, laser-induced breakdown spectroscopy technique with support vector machine can identify more kinds of plastics with high accuracy and efficiency.

Baseline removal; Raman spectroscopy; laser-induced breakdown spectroscopy; LIBS; very near; infrared spectroscopy; VNIR;X-RAY-SPECTRA; LEAST-SQUARES; RAMAN-SPECTRA; NMR-SPECTRA; RECOGNITION; SUBTRACTION; REGRESSION; ALGORITHM

The task of proper baseline or continuum removal is common to nearly all types of spectroscopy. Its goal is to remove any portion of a signal that is irrelevant to features of interest while preserving any predictive information. Despite the importance of baseline removal, median or guessed default parameters are commonly employed, often using commercially available software supplied with instruments. Several published baseline removal algorithms have been shown to be useful for particular spectroscopic applications but their generalizability is ambiguous. The new Custom Baseline Removal (Custom BLR) method presented here generalizes the problem of baseline removal by combining operations from previously proposed methods to synthesize new correction algorithms. It creates novel methods for each technique, application, and training set, discovering new algorithms that maximize the predictive accuracy of the resulting spectroscopic models. In most cases, these learned methods either match or improve on the performance of the best alternative. Examples of these advantages are shown for three different scenarios: quantification of components in near-infrared spectra of corn and laser-induced breakdown spectroscopydata of rocks, and classification/matching of minerals using Raman spectroscopy. Software to implement this optimization is available from the authors. By removing subjectivity from this commonly encountered task, Custom BLR is a significant step toward completely automatic and general baseline removal in spectroscopic and other applications.

LIBS; Mineral composition; Analytical technique; Food; Chemometrics;PLANT MATERIALS; WHEAT-FLOUR; CLASSIFICATION; ADULTERATION; SPECTROMETRY; IDENTIFICATION; CHEMOMETRICS; VALIDATION; EMISSION; STANDARD

Background: Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopic technique which uses a focused pulsed laser beam to generate plasma from the material. The plasma contains atoms, ions and free electrons which emit electromagnetic radiation as the plasma cools down. The emitted light is resolved by a spectrometer to form a spectrum. Recently, LIBS has become an emerging analytical technique for characterisation and identification of materials; its multi-elemental analysis, fast response, remote sensing, little to no sample preparation, low running cost and ease of use make LIBS a promising technique for the food sector. Scope and approach: The present article reviews the feasibility of LIBS for food analysis. It presents recent progress and applications of LIBS as an efficient and reagent-free, at-line tool capable of replacing traditional time-consuming analytical methods for assessing the quality and composition of food products. An overview of LIBS fundamentals, instrumentation and statistical data analysis is also provided. Key findings and conclusions: Although LIBS technology shows many advantages, challenges remain in terms of sample preparation, matrix effects, spectral pre-processing, model calibration and instrument development. (C) 2017 Elsevier Ltd. All rights reserved.

LIBS; Beef; Offal; Copper; PLSR; Chemometrics;MOTION CONDITIONS; TRACE-ELEMENTS; MEAT; SPECTROMETRY; LIBS; CLASSIFICATION; PREDICTION; SAMPLES

Laser induced breakdown spectroscopy (LIBS) is an emerging technique in the field of food analysis which provides various advantages such as minimal sample preparation, chemical free, rapid detection, provision of spatial information and portability. In this study, LIBS was employed for quantitative analysis of copper content in minced beef samples spiked with beef liver over three independent batches. Copper content was determined with graphite furnace atomic absorption spectroscopy (GFAAS) in order to obtain reference values for modelling. Partial least square regression (PLSR) was performed to build a calibration and validation model. A calibration model with a high R-cv(2) of 0.85 and a RMSECV of 43.5 ppm was obtained, confirming a good fit for the model. The validation model showed a good prediction accuracy with a high R-p(2) of 0.85 and RMSEP of 36.8 ppm. Moreover, on a further study to evaluate the spatial capabilities, LIBS was able to successfully map copper content within a pellet, indicating the suitability of LIBS to provide spatial information and therefore potential use on heterogeneous samples. Overall, it can be concluded that LIBS combined with chemometrics demonstrates potential as a quality monitoring tool for the meat processing industry.

INDUCED BREAKDOWN SPECTROSCOPY; QUANTITATIVE-ANALYSIS; STEEL SAMPLES; CLASSIFICATION; PLASMA; RECOGNITION; TEMPERATURE; EXPLOSIVES; MODELS; SOILS

Laser induced breakdown spectroscopy with chemometric methods has been employed for sorting zamak alloy (zinc based alloy) samples. Plasma plumes were created on the sample surface using 50 mJ Nd:YAG laser pulses at its fundamental wavelength (lambda = 1064 nm). Six types of zinc alloy samples with different elemental compositions have been used to perform the LIBS measurements under optimized experimental conditions. On the obtained LIBS data three different chemometric classification models were applied, namely K Nearest Neighbor (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). The effect of the input database choice and its normalization on the classification model efficiency has been investigated and found to play a crucial role. It was found that KNN, SVM and ANNs permit an appropriate classification of different zinc alloys using LIBS spectra and the best results were obtained from the largest input database. The effect of data normalization has been discussed for each model. The obtained results demonstrate the feasibility of using chemometric methods associated with the LIBS technique as an industrial tool for in situ zamak sorting.

MULTIVARIATE-ANALYSIS; TRACE-METALS; CLASSIFICATION; POLLUTION; CHEMOMETRICS; COMBINATION; DIGESTION; SAMPLES

Laser-induced breakdown spectroscopy (LIBS) is an emerging, multi-elemental and green technique, particularly suitable for the direct analysis of solid samples. In this study, thirty-two superficial sediments collected from Cienfuegos Bay in 2011-2013 were analyzed by LIBS without requiring sample digestion. Al, Ca, Fe, K, Mg, Mn, Na and Ti emission lines were detected in all samples. The full LIBS spectra of the samples were subjected to principal component analysis (PCA) and hierarchical cluster analysis (HCA). Most samples showed a similar emission signal pattern, whereas two samples collected in 2012 and another two in 2013 showed anomalously higher emission signals for Na, Mg and Ca, Mg, respectively. In three of the sediments, the impact of external factors on the ecosystem is the presumable cause of the observed anomalies. The combination of LIBS with multivariate data analysis demonstrated a high capacity for the fast detection of anomalous metal compositions in marine sediments.

LIBS; POLLUTION; CHINA

In this work, a mobile laser-induced breakdown spectroscopy (LIBS) system has been successfully applied to in situ analysis of heavy metals in soil samples. The LIBS system had two working methods, which were a fixed measuring method and a handheld method. For the fixed measuring method, a simple sample pretreatment was needed to reduce the soilmatrix effect generated by moisture and porosity. Experiments proved that this method could be used to semi-quantitatively detect heavy metals when combined with the traditional calibration curve method. The limits of detection for copper, lead, and zinc were all below 10 mg/kg, which satisfied the need of heavy metal detection in soil. Principal component analysis was used for soil classification, which helped to build appropriate calibration curves. On the basis of soil classification, accurate and rapid detection of heavy metals in soil is feasible. For the handheld method, spectrum intensity and stability decreased significantly compared with the fixed measuring method. However, by using the internal standard method, the stability of LIBS data was improved significantly to 6%. For soil samples with serious heavy-metal pollution, the measurement errors were less than 12%, which indicated that handheld LIBS was effective to monitor heavy-metal pollution in soil. The research results provide application support for rapid and on-site monitoring of heavy metals in soil. (C) 2017 Optical Society of America

NIR SPECTROSCOPY; DATA NORMALIZATION; MOTION CONDITIONS; INFANT FORMULA; DIETARY-SODIUM; BLOOD-PRESSURE; MEAT; LIBS; CLASSIFICATION; PREDICTION

Beef is a rich source of important minerals, with potassium (K) being the most abundant mineral quantitatively except in cured meats where sodium (Na) from the added salt predominates. This study evaluates the capability of LIBS for quantification of the Na and K contents of minced beef as a potential method of detecting beef kidney adulteration. Additionally, the study aims to demonstrate the ability of LIBS to provide spatial mineral information of minced beef. A LIBS system was employed to collect spectral information of adulterated minced beef samples. Atomic absorption spectroscopy (AAS) was used to obtain reference values for Na and K. The chemometric technique of partial least squares regression (PLSR) was used to build the prediction models. Spatial mineral maps of minced beef samples were generated based on the predicted percentages of Na and K. The models for Na and K yielded calibration coefficients of determination (R-c(2)) of 0.97 and 0.91 respectively. Similarly, a good calibration model was obtained for adulteration yielding a R-c(2) of 0.97. Good prediction accuracy was observed for all models. Spatial mapping provided two major advantages: (a) representative measurements of samples and (b) spatial distribution of multi-elements. The results observed illustrate the ability of LIBS combined with chemometrics as a potential monitoring tool for mineral quantification as well as adulteration detection for the meat processing industry.

INDUCED BREAKDOWN SPECTROSCOPY; INDUCTIVELY-COUPLED PLASMA; MOLECULAR ISOTOPIC SPECTROMETRY; FLIGHT MASS-SPECTROMETRY; LA-ICP-MS; DEPTH PROFILE; LIBS; SAMPLES; STEEL

Laser ablation has become a dominant technology for direct solid sampling in analytical chemistry. Laser ablation refers to the process in which an intense burst of energy delivered by a short laser pulse is used to sample (remove a portion of) a material. The advantages of laser-ablation chemical analysis include direct characterization of solids, no chemical procedures for dissolution, reduced risk of contamination or sample loss, analysis of very small samples not separable for solution analysis, and determination of spatial distributions of elemental composition. This short review describes the most common approaches-laser-induced breakdown spectroscopy (LIBS) and laser-ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS)-and provides an introduction to laser-ablation molecular isotopic spectrometry (LAMIS).

Laser-induced breakdown spectroscopy; Matcha; Green tea powder; Linear discriminant analysis (LDA);LIBS

The feasibility of using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics methods for fast identification of matcha and green tea powder was investigated in this paper. The main differences between matcha and green tea powder are varieties of tea plants, cultivation management, growth time and processing technology. LIBS spectra between 230 to 880 nm of matcha produced by different manufacturers and green tea powder made with different fixation methods were selected and min-max normalization was the measure for preprocessing. Characteristic wavelengths were selected according to the X-variables loadings on the basis of principal component analysis (PCA), and then linear discriminant analysis (LDA) models were built based on characteristic wavelengths. The results showed that the LDA model based on characteristic wavelengths could identify matcha and green tea powder rapidly. Four characteristic wavelengths belong to C(I) 247. 94 nm, Mg(II) 279. 60 nm, Ca(II) 393. 45 nm and Fe(II) 766. 68 nm. The accuracy of the calibration and the prediction set all reached 100%. Laser-induced breakdown spectroscopy could accurately identify matcha produced by different manufacturers and green tea powder made with different fixation methods.

LIBS; Mobile LIBS; Chemometrics; PLS-R; Building materials;CONCRETE STRUCTURES; CALIBRATION; REGRESSION; TUTORIAL; LIMIT

For the damage assessment of reinforced concrete structures the quantified ingress profiles of harmful species like chlorides, sulfates and alkali need to be determined. In order to provide on-site analysis of concrete a fast and reliable method is necessary. Low transition probabilities as well as the high ionization energies for chlorine and sulfur in the near-infrared range makes the detection of CI I and S I in low concentrations a difficult task. For the on-site analysis a mobile LIBS-system (lambda = 1064 nm, E-pulse <= 3 mJ, tau = 1.5 ns) with an automated scanner has been developed at BAM. Weak chlorine and sulfur signal intensities do not allow classical univariate analysis for process data derived from the mobile system. In order to improve the analytical performance multivariate analysis like PLS-R will be presented in this work. A comparison to standard univariate analysis will be carried out and results covering important parameters like detection and quantification limits (LOD, LOQ) as well as processing variances will be discussed (Allegrini and Olivieri, 2014 [1]; Ostra et al., 2008 [2]). It will be shown that for the first time a low cost mobile system is capable of providing reproducible chlorine and sulfur analysis on concrete by using a low sensitive system in combination with multivariate evaluation. (C) 2017 Elsevier B.V. All rights reserved.

RISK-ASSESSMENT; PLANT MATERIALS; LIBS; PELLETS

In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdownspectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration employing intensity of single Cd line and Cd concentration exposed the weakness of this calibration method. Furthermore, the accuracy of linear calibration can be improved a little by triple Cd lines as characteristic variables, especially after the spectra were pretreated. However, it is not enough in predicting Cd in samples. Therefore, partial least-squares regression (PLSR) was utilized to enhance the robustness of quantitative analysis. The results of the PLSR model showed that the prediction accuracy of the Cd target can meet the requirement of determination in food safety. This investigation presented that LIBS is a promising and emerging method in analyzing toxic compositions in agricultural products, especially combined with suitable chemometrics. (C) 2017 Optical Society of America

medicinal herbs; essential and toxic metals; LIBS; ICP OES; chemometrics;INDUCED BREAKDOWN SPECTROSCOPY; PRINCIPAL COMPONENT ANALYSIS; TRACE-ELEMENTS; NEUTRON-ACTIVATION; INFUSIONS; INFORMATION; FINGERPRINT; CALCIUM; PLANTS; HONEY

Essential and toxic metals were determined in eighteen samples of medicinal herbs from Poland, Lithuania and Serbia by means of laser-induced breakdownspectroscopy (LIBS) and inductively coupled plasma optical emission spectrometry (ICP OES). Calcium, K and Mg concentrations were obtained in % (m/m) and other metals (Na, Co, Cu, Fe, Mn, Ni, Zn, Cd, Cr and Pb) in mg kg-1. The fact that the herb samples analyzed belonged to specific plant species and represented different morphological plant parts explained the characteristic distribution in two-dimensional scores plots obtained by principal component analysis (PCA). A strong correlation of LIBS results was achieved in comparison to those obtained by ICP OES for Ca, K and Mg. Differences in the types of infusions were observed, in that leaves are related to Zn and Ni concentrations, leaves and flowers to Co, Ca and Mn concentrations and flowers to K, Na, Mg and Fe content.

Laser ablation; Raman spectroscopy; Chemometrics; Complementarity; Polymers;INDUCED BREAKDOWN SPECTROSCOPY; INDUCED PLASMA SPECTROSCOPY; ARTIFICIAL NEURAL-NETWORKS; POLYMER IDENTIFICATION; CLASSIFICATION; MICROSCOPY; PIGMENTS; SPECTRA; COPPER; NIR

Classification of plastics is of great importance in the recycling industry as the littering of plastic wastes increases day by day as a result of its extensive use. In this paper, we demonstrate the efficacy of a combined laser-induced breakdown spectroscopy (LIBS)-Raman system for the rapid identification and classification of post-consumer plastics. The atomic information and molecular information of polyethylene terephthalate, polyethylene, polypropylene, and polystyrene were studied using plasma emission spectra and scattered signal obtained in the LIBS and Raman technique, respectively. The collected spectral features of the samples were analyzed using statistical tools (principal component analysis, Mahalanobis distance) to categorize the plastics. The analyses of the data clearly show that elemental information and molecular information obtained from these techniques are efficient for classification of plastics. In addition, the molecular information collected via Raman spectroscopy exhibits clearly distinct features for the transparent plastics (100% discrimination), whereas the LIBS technique shows better spectral feature differences for the colored samples. The study shows that the information obtained from these complementary techniques allows the complete classification of the plastic samples, irrespective of the color or additives. This work further throws some light on the fact that the potential limitations of any of these techniques for sample identification can be overcome by the complementarity of these two techniques.

Laser-induced breakdown spectrometry; Polymers recycling; E-waste; Chemometrics;ELECTRONIC EQUIPMENT WEEE; MASS-SPECTROMETRY; PLASTICS; LIBS; INFORMATION; FRACTIONS; EFFICIENT

In the recycling of polymer e-waste, there is a pressing need for rapid measurement technologies for the simple identification and classification of these materials. The goal of this work was to instantly identify e-waste polymers by laser-induced breakdown spectrometry (LIBS). The studied polymers were acrylonitrile-butadiene-styrene (ABS), polystyrene (PS), polyethylene (PE), polycarbonate (PC), polypropylene (PP), and polyamide (PA). Emission lines were selected for C (247), H (656), N (742 + 744 + 747), and 0 (777), as well as the molecular band of C2 (516), and the ratios of the emission lines and molecular band were utilized. Classification models, k-nearest neighbors (KNN) and soft independent modeling of class analogy (SIMCA), were used to rank the polymers. Both constructed models gave satisfactory results for the validation samples, with average accuracies of 98% for KNN and 92% for SIMCA. These results prove the predictive analytical capabilities of the LIBS technique for plastic identification and classification. (C) 2017 Elsevier Ltd. All rights reserved.

Laser-induced breakdown spectroscopy; LIBS; matrix effects; univariate calibration; minor elements; Cr; Ni; Co; Zn; Mn;GALE CRATER; MARS; SAMPLES

Obtaining quantitative chemical information using laser-induced breakdown spectroscopy is challenging due to the variability in the bulk composition of geological materials. Chemical matrix effects caused by this variability produce changes in the peak area that are not proportional to the changes in minor element concentration. Therefore the use of univariate calibrations to predict trace element concentrations in geological samples is plagued by a high degree of uncertainty. This work evaluated the accuracy of univariate minor element predictions as a function of the composition of the major element matrices of the samples and examined the factors that limit the prediction accuracy of univariate calibrations. Five different sample matrices were doped with 10-85 000 ppm Cr, Mn, Ni, Zn, and Co and then independently measured in 175 mixtures by X-ray fluorescence, inductively coupled plasma atomic emission spectrometry, and laser-induced breakdown spectroscopy, the latter at three different laser energies (1.9, 2.8, and 3.7 mJ). Univariate prediction models for minor element concentrations were created using varying combinations of dopants, matrices, normalization/no normalization, and energy density; the model accuracies were evaluated using root mean square prediction errors and leave-one-out cross-validation. The results showed the superiority of using normalization for predictions of minor elements when the predicted sample and those in the training set had matrices with similar SiO2 contents. Normalization also mitigates differences in spectra arising from laser/sample coupling effects and the use of different energy densities. Prediction of minor elements in matrices that are dissimilar to those in the training set can increase the uncertainty of prediction by an order of magnitude. Overall, the quality of a univariate calibration is primarily determined by the availability of a persistent, measurable peak with a favorable transition probability that has little to no interference from neighboring peaks in the spectra of both the unknown and those used to train it.

SITE QUANTITATIVE-ANALYSIS; LIBS SPECTRA; POWER-PLANTS; ASH CONTENT; WELL LOGS; VALUE GCV; CALIBRATION; PREDICTION; MODEL; PERFORMANCE

Online measurement for the gross calorific Value (GCV) of coal is important in the coal Utilization industry. This paper proposed a rapid GCV determination method that combined a laser-induced breakdown spectroscopy (LIBS) technique with artificial neural networks (ANNs) and genetic algorithm (GA). Input variables were selected according to the physical mechanism and mathematical significance to improve the prediction of the ANN. GA was applied to determine an,optirnal architecture for the network instead of a trial and error method. As a result, the mean standard deviatiori (MSD) of the GCV for four prediction set samples is,0.38 MJ/kg in 50 trials (repetitions of training the ANN with the same; input data but different random initial weights and biases), proving that the ANN model is able to provide high modeling repeatability in the GCV analysis. The mean absolute error (MAE) of the GCV for the prediction set is 039 MJ/kg. The result meets the requirements (0.8 MI/kg) for coal online analyses using the neutron activation method in the Chinese national standard (GB/T 29161-2012).

MOISTURE-CONTENT; LIBS; CLASSIFICATION; CHEMOMETRICS; PLANTS

Tobacco mosaic virus (TMV) is one of the most devastating viruses to crops, which can cause severe production loss and affect the quality of products. In this study, we have proposed a novel approach to discriminate TMV-infected tobacco based on laser-induced breakdown spectroscopy (LIBS). Two different kinds of tobacco samples (fresh leaves and dried leaf pellets) were collected for spectral acquisition, and partial least squared discrimination analysis (PLS-DA) was used to establish classification models based on full spectrum and observed emission lines. The influences of moisture content on spectral profile, signal stability and plasma parameters (temperature and electron density) were also analysed. The results revealed that moisture content in fresh tobacco leaves would worsen the stability of analysis, and have a detrimental effect on the classification results. Good classification results were achieved based on the data from both full spectrum and observed emission lines of dried leaves, approaching 97.2% and 88.9% in the prediction set, respectively. In addition, support vector machine (SVM) could improve the classification results and eliminate influences of moisture content. The preliminary results indicate that LIBS coupled with chemometrics could provide a fast, efficient and low-cost approach for TMV-infected disease detection in tobacco leaves.

PLASMA-MASS SPECTROMETRY; ICP-MS; EMISSION-SPECTROMETRY; PRECONCENTRATION; SAMPLES; ROCKS; SOIL; CHROMATOGRAPHY; SEPARATION; THORIUM

Univariate and multivariate analyses of six rare earth elements [cerium (Ce), europium (Eu), gadolinium (Gd), neodymium (Nd), samarium (Sm), and yttrium (Y)] have been performed using data from laser-induced breakdown spectroscopy (LIBS). Binary mixtures of oxide forms of each rare earth element in an Al2O3 matrix with their concentrations varying from 1% to 10% by weight in powder form were used as working samples for univariate analysis. For multivariate analysis, complex mixtures of oxides of all these six rare earth elements and Al2O3 in powder form, where the concentration of each element oxide was varied from 1% to 50% by weight one by one, were used to record LIBS spectra. Optimum values of gate delay, gate width, and laser energy were used to get spectra from these samples and spectra were used to develop calibration models. The limits of detection for Ce, Eu, Gd, Nd, Sm, and Y were calculated to be 0.098%, 0.052%, 0.077%, 0.047%, 0.250%, and 0.036%, respectively, from the calibration curves. (C) 2017 Optical Society of America

LIBS; Variation; Experimental design; Normalization; Statistics;INDUCED BREAKDOWN SPECTROSCOPY; FLOAT GLASS; SPECTROMETRY; DISCRIMINATION

Laser Induced Breakdown Spectroscopy (LIBS) is an analytical chemistry technique that has the potential to identify and measure the elements in a substance of interest. LIBS is applicable for any phase (solid, liquid or gas). LIBS has gained importance in the fields of material identification, biomedical science, forensics, military, art and archaeology in the recent years. In spite of its many advantages over other instruments, it has a few drawbacks such as poor precision and repeatability. That is, different spectra may be observed for the same sample over successive runs. This indicates poor precision of the instrument. This paper presents an experiment designed to investigate the claim that there is higher variation in the experiments conducted on different days as compared to the experiments conducted on the same day. This study examines the variability in the spectra between the runs on the same day, and the variability between runs on different days using samples from a standard reference glass, and discusses the conclusions that can be drawn from the results. (C) 2017 Elsevier B.V. All rights reserved.

Laser-induced breakdown spectroscopy; Support vector machine; Principal component analysis; Tissue classification;BIOMEDICAL APPLICATIONS; LIBS; IDENTIFICATION; ELEMENTS; IMPROVEMENT; PROGRESS; PORK; MN

To improve the classification accuracy of fresh meat species using laser. induced breakdown spectroscopy (LIBS), the support vector machine (SVM) and principal component analysis (PCA) were combined to classify fresh meat species (including pork, beef, and chicken). A simple sample preparation to flatten fresh meat by glass slides was proposed. For each meat sample, 150 spectra were recorded and randomly arranged. The first 75 spectra were used to train a model while the others were used for model validation. By analyzing the 49 normalized spectral lines (K, Ca, Na, Mg, Al, H, O, etc.) in the different tissues, the classification model was built. The results showed that the dimensionality of input variables was decreased from 49 to 10 and modeling time was reduced from 89. 11 s to 55. 52 s using PCA, thus improving the modeling efficiency. The mean classification accuracy of 89. 11% was achieved. The method and reference data are provided for further study of fresh meat classification by laser-induced breakdown spectroscopy technique.

Cultural heritage; Laser scanners; Fiber Bragg grating sensor; Laser-induced fluorescence; Laser-induced breakdown spectroscopy;INDUCED BREAKDOWN SPECTROSCOPY; PLASMA PARAMETERS; FLUORESCENCE

The recent development of laser scanners in environmental diagnostics made possible to implement their prototypes for remote characterization of cultural heritage (CH) surfaces. Specific data analysis algorithms have been developed to handle large data set acquired in high resolution operation. Non-invasive, or at least micro-invasive methodologies applied on real CH targets have shown the possibility to obtain information on surface appearance, morphology and composition. The added value of optical and spectroscopic integrated sensors is discussed for some significant case studies addressed both to a deep knowledge of the artifacts (materials and methods of realization) and to their structural characterization.

Laser induced breakdown spectroscopy; Chinese cabbage; Cadmiun; Variable screening;HEAVY-METALS; LIBS

Heavy metal residue in vegetables is a big concern in the whole world. The aim of this work is to explore the effect of multivariable selection on analyzing Cd in Chinese cabbage polluted in lab by collecting the spectra of laser induced breakdown spectroscopy (LIBS) from the samples. At the same time, the actual Cd content in samples was obtained by anodic stripping voltammetry (ASV). The LIBS spectral range in partial least square (PLS) model was screened by standard normal variable transformation (SNV), first derivative (FD), second derivative (SD) and center treatment (CT) for preprocessing spectra and the optimized method was used for the analysis of interval partial least square (iPLS) and synergy interval partial least square (SiPLS). The results indicated that the method of CT was the best as a comparison with PLS, iPLS and SiPLS. And the intervals of wavelength were 214.72-215.82 nm, 215.88-216.97 nm and 225.08 -226.35 nm by utilizing the optimized SiPLS. Here the root mean square error of cross validation (RMSECV) between real content and predicted ones was 1.487, the root mean squared error of prediction (RMSEP) was 1.094, the correlation coefficient (R) was 0.9942, and the average relative error (ARE) was 11.60%. The results displayed that LIBS could predict Cd in vegetables by multivariable selection of SiPLS and the accuracy could meet the requirement of rapid and green analysis of Cd in vegetables.

IMPROVEMENT; CHEMOMETRICS; SPECTROMETRY; CONFINEMENT; CALIBRATION; SELECTION; PLASMA; LIBS; SOIL

In this work, a genetic algorithm (GA) was employed to select the intensity ratios of the spectral lines belonging to the target and domain matrix elements, then these selected line-intensity ratios were taken as inputs to construct an analysis model based on an artificial neural network (ANN) to analyze the elements copper (Cu) and vanadium (V) in steel samples. The results revealed that the root mean square errors of prediction (RMSEPs) for the elements Cu and V can reach 0.0040 wt. % and 0.0039 wt. %, respectively. Compared to 0.0190 wt. % and 0.0201 wt. % of the conventional internal calibration approach, the reduction rates of the RMSEP values reached 78.9% and 80.6%, respectively. These results indicate that the GA combining ANN can excellently execute the quantitative analysis in laser-induced breakdown spectroscopy for steel samples and further improve analytical accuracy. (C) 2017 Optical Society of America

chemometrics; ion recombination; laser-induced breakdown spectroscopy; LIBS; MCR-ALS; mean field-independent components analysis; MFICA; molecular formation; multivariate curve resolution-alternating least squares; temporal evolution;ARTIFICIAL NEURAL-NETWORKS; QUANTITATIVE-ANALYSIS; TEMPORAL EVOLUTION; WAVELET-TRANSFORM; ABSORPTION-SPECTROSCOPY; ANALYTICAL-CHEMISTRY; CHEMOMETRIC METHODS; LIBS; IDENTIFICATION; OPTIMIZATION

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique allowing the determination of elemental concentrations in a variety of matrices in the solid, liquid, and gaseous phases. Because of the inherent complexity of the signal and to the high dimensionality of experimental data, chemometrics has been more and more applied in LIBS to perform samples identification or quantitative measurements. But multivariate methods can also be used for the description and physical interpretation of the plasma, particularly to exploit the temporal dimension of the LIBS signal, which is usually neglected in spectrochemical measurements. In this work, time-resolved spectra of a pure aluminum sample were treated with 2 methods, mean field-independent components analysis and multivariate curve resolution-alternating least squares, applying non-negativity constraints for scores and components in both cases. Results obtained were compared with reference univariate measurements of the emission of the species of interest (ions, neutral atoms, and molecules). The interpretation of scores and components provided a physical description of phenomena that take place between species in the plasma, like ionic recombination and molecules formation. Overall, mean field-independent components analysis and multivariate curve resolution-alternating least squares yield equivalent solutions with our dataset. This new approach is very promising for the treatment of time-resolved data obtained by LIBS.

Laser induced breakdown spectroscopy; Remote detection; Partial least squares;

As a kind of spectroscopic technique, the remote laser-induced breakdown spectroscopy (Remote LIBS) can qualitatively or quantitatively measure the elemental compositions of remote targets with high-power laser and focusing optical path. In this work, a Remote LIBS system was designed and established to probe the target from 2 to 10 meters. It is characterized with Cassegrain telescope structure and automatic focusing technique. Based on this system, a method to remotely retrieve the major elemental abundance of rocks is presented. With comparative experiment, the influences on spectral signal by pulse laser energy, acquisition delay time, integration time, accumulative pulse number are analyzed to find out the optimum parameters: the wavelength of laser is 1 064 nm, pulse energy is 120 mJ, delay time is 1. 5 mu s, integration time is 1 ms, and each spectrum data acquired with averaging 30 times detection. 48 pieces of rock specimens and 6 kinds of standard rock samples (shale, granite, andesite, basalt, gneiss and pegmatite) are selected for the experiment. As to the atomic spectra database, 8 characteristic spectral lines of the major elements (Si I 390. 55 nm, Al I 394. 40 nm, Al 1396. 15 nm, Ca II396. 85 nm, Fe I 404. 6 nm, Si I 500. 60nm, Mg I 518. 36nm, Na I 589. 59 nm) were extracted for analyses. Then a PLS model is constructed to quantitatively analyze the rock elements. 48 rock specimens were selected as the training sets to serve the model. The 6 standard samples were used to test the solved model. The testing results shows that the elemental abundance of Si and Al can be predicted accurately with average relative error of only 9. 4% and 9. 6% respectively.

NMR; LIBS; Surface relaxivity; Paramagnetic impurities; Porous media;NUCLEAR MAGNETIC-RELAXATION; POROUS-MEDIA WETTABILITY; TO-VOLUME RATIO; SEDIMENTARY-ROCKS; NMR RELAXOMETRY; PROTON NMR; PORE SIZES; DIFFUSION; VALIDATION; REGRESSION

Nuclear magnetic resonance (NMR) relaxometry is a common technique used to assess the pore size of fluid-filled porous materials in a wide variety of fields. However, the NMR signal itself only provides a relative distribution of pore size. To calculate an absolute pore size distribution from the NMR data, the material's surface relaxivity needs to be known. Here, a method is presented using laser-induced breakdown spectroscopy (LIBS) to evaluate surface relaxivity in sandstones. NMR transverse and longitudinal relaxation was measured on a set of sandstone samples and the surface relaxivity was calculated from the pore size distribution determined with MICP measurements. Using multivariate analysis, it was determined that the LIBS data can predict with good accuracy the longitudinal (R-2 similar to 0.84) and transverse (R-2 similar to 0.79) surface relaxivity. Analysis of the regression coefficients shows significant influence from several elements. Some of these are elements previously established to have an effect on surface relaxivity, such as iron and manganese, while others are not commonly associated with surface relaxivity, such as cobalt and titanium. Furthermore, LIBS provides advantages compared to current methods to calibrate surface relaxivity in terms of speed, portability, and sample size requirements. While this paper focuses on geological samples, the method could potentially be expanded to other types of porous materials. (C) 2016 Elsevier Inc. All rights reserved.

LIBS; Chemometrics; PLSR; Nuclear fuel; AHWR;INDUCED BREAKDOWN SPECTROSCOPY; NANOSECOND 266 NM; WAVELENGTH DEPENDENCE; GLASS; THORIUM; URANIUM; SPECTROMETRY

The determination of uranium with composition varying from 0% to 35 wt% in (Th-U)O-2 mixed oxide fuel using laser induced breakdown spectroscopy (LIBS) utilizing partial least square regression (PLSR) has been demonstrated. Good agreement between expected and experiment results using 266 nm, 532 nm and 1064 nm was shown. The analytical results at 266 nm of 2-3% precision and similar to 1% accuracy (bias) satisfy the acceptance criteria range for chemical analysis in the nuclear industry. (C) 2016 ElseVier B.V. All rights reserved.

Chemometrics; Classification; Laser-induced breakdown spectroscopy; Variety; Walnut powder;JUGLANS-REGIA L.; SUPPORT VECTOR MACHINE; CLASSIFICATION; LIBS; CHEMOMETRICS; VARIABILITY; EMISSION; SAMPLE; OILS

Rapid classification of varieties of walnut powder is important to protect consumers against potential fraud because different varieties of walnut powder vary dramatically in quality, taste, and price. Laser-induced breakdown spectroscopy (LIBS) is a promising technology for on-line application that has the advantages of no or little sample preparation, fast response (usually seconds to minutes), and remote detection capability. In this research, LIBS and multi-variable analysis methods were applied to classify varieties of walnut powder for the first time. Before LIBS analysis, a fairly flat surface was provided to reduce fluctuation, and no other sample preparation was needed. After acquiring the LIBS spectra, principal component analysis (PCA) was used to make qualitative discrimination, and classification models were developed with partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM). It was shown that three origins as well as different varieties from the same origin could be differentiated successfully by LIBS and chemometrics, while the discrimination of six different varieties with a single ""global"" model was not very successful. SVM could help to handle complex LIBS dataset, which improved the classified rate. The presented results provide the first proof-of-principle data for on-line application of the LIBS technique for classification of varieties of walnut powder.

laser-induced breakdown spectroscopy; principal component analysis; sedimentary rock; metamorphic rock; igneous rock;LIBS

This study deals with the analysis of rocks using laser-induced breakdown spectroscopy (LIBS) coupled with principal component analysis. The spectra of sedimentary, metamorphic, and igneous rock samples were recorded in the 200-900 nm spectral range. The atomic lines of elements such as Si, Ca, Mg, Fe, Na, and K along with lighter elements, namely C, H, N, and O, were observed in these spectra. Multivariate analysis in combination with LIBS was used to classify the samples. For principal component analysis, a 12 x 5849 data matrix was formed using the results of LIBS. The plot of the analysis revealed similarities between the sedimentary and metamorphic rock samples compared with the igneous rock sample. Thus, the present study demonstrates that LIBS coupled with principal component analysis can become an important tool for rapid classification and in-situ discrimination of rock samples.

Laser induced breakdown spectrometry; Partial Least Squares; PZT ceramics; Calibration strategies;ATOMIC EMISSION-SPECTROMETRY; ABSORPTION-SPECTROMETRY; CLASSIFICATION; CHEMOMETRICS

In PZT (Lead Zirconate Titanate) ceramics, stoichiometric composition is closely related with physical properties and applications. Laser-induced BreakdownSpectroscopy (LIBS) have shown to be a suitable technique for analyzing many types of solid samples, usually in combination with multivariate techniques. In this study, a method for quick determination of Pb, Zr, Ti and Sr in PZT ceramics using LIBS was proposed. Spectral data is analyzed with PLS (Partial Least Squares), providing results better than Multiple Linear Regression (MLR) and external calibration, when compared with previous determination of the same group of ceramics by PIXE (Particle Induced X-ray Emission). Most of predicted individual concentrations were between 98 and 102% of the reference value for Pb, and between 90 and 110% for the rest of the elements. (C) 2016 Elsevier B.V. All rights reserved.

Additive manufacturing; composition monitoring; optical emission spectroscopy (OES); support vector regression (SVR);INDUCED BREAKDOWN SPECTROSCOPY; METAL-DEPOSITION; MAGNESIUM ALLOY; ARC PLASMA; ELEMENTS; DEFECTS; NETWORK; SAMPLES; SYSTEM; LIBS

Laser additive manufacturing has gained widespread adoption in recent years. However, process diagnosis and process control lag behind the progresses of other key technologies, which make the product quality control a challenging problem. This work proposes an operating parameter conditioned support vector regression (SVR) method that uses processing parameter conditioned kernel function to achieve a processing parameter independent in-situ composition prediction. Two different features of laser-induced plasma, spectral line-intensity-ratio, and both spectral line-intensity-ratio and spectral integrated intensity were used to train the SVR. Composition measurements using a calibration curve method, partial least square regression, and artificial neural networks are also performed for comparison. The results show that the SVR with both spectral line-intensity-ratio and spectral integrated intensity as inputs has the best performance due to linearly separable point clusters in the high-dimensional space. Laser power independent composition prediction is achieved and real-time composition predictions are validated. It is proved that the operating parameter conditioned SVR provides a more accurate, a more universal, and an operating parameter independent prediction. Moreover, operating parameter conditioned SVR provides a potential data-driven-based approach for real-time composition monitoring of the laser additive manufacturing process.

Laser-Induced Breakdown Spectroscopy; Uranium; principal component analysis; Chemical mapping; Sandstone-hosted deposit; X-ray Fluorescence;

The goal of this work is to provide high resolution mapping of uranium in sandstone-hosted uranium ores using Laser-Induced Breakdown Spectroscopy (LIBS) technique. In order to obtain chemical image with highest possible spatial resolution, LIBS system in orthogonal double pulse (DP LIBS) arrangement was employed. Owing to this experimental arrangement the spot size of 50 μm in diameter resulting in lateral resolution of 100 μm was reached. Despite the increase in signal intensity in DP LIBS modification, the detection of uranium is challenging. The main cause is the high density of uranium spectral lines, which together with broadening of LIBS spectral lines overreaches the resolution of commonly used spectrometers. It results in increased overall background radiation with only few distinguishable uranium lines. Three different approaches in the LIBS data treatment for the uranium detection were utilized: i) spectral line intensity, ii) region of apparent background and iii) multivariate data analysis. By utilizing multivariate statistical methods, a specific specimen features (in our case uranium content) were revealed by processing complete spectral information obtained from broadband echelle spectrograph. Our results are in a good agreement with conventional approaches such as line fitting and show new possibilities of processing spectral data in mapping. As a reference technique to LIBS was employed X-ray Fluorescence (XRF). The XRF chemical images used in this paper have lower resolution (approximately 1–2 mm per image point), nevertheless the elemental distribution is apparent and corresponds to presented LIBS experiments.

Laser-Induced Breakdown Spectroscopy; Multivariate classification; Echelle spectrometer; Czerny-Turner spectrometer;

The objective of this work was to assess a part of echelle Laser-Induced Plasma spectra (ranging from 200 to 1000 nm) that could be most effectively employed for rocks classification. Therefore, a 60 nm wide spectral window mask was iteratively moved over the broadband echelle spectra. Each created narrow artificial spectral windows (60 nm) was used for the classification of rock samples using various Multivariate Data Analysis (MVDA) algorithms, reaching more than 99% of the overall accuracy in certain cases. Afterwards, the Czerny-Turner spectrometer (having higher sensitivity compared to the echelle spectrometer) was aligned to the a priori selected and the most effective spectral regions and rocks samples were re-measured. Consequently the MVDA analyses were utilized again, providing also satisfying classification results yielding more than 99% of the overall accuracy. Measurements of 28 sedimentary ores (certified reference materials) were done utilizing commercially available X-Trace device (AtomTrace), where spectrometers in both configurations (echelle and Czerny-Turner) were exploited.

Laser-Induced Breakdown Spectroscopy, LIBS; Outlier filtering; Principal Component Analysis, PCA; Linear correlation; Total spectral intensity; Soft Independent Modelling of Class Analogies, SIMCA;

In this manuscript we highlight the necessity of outlier filtering prior the multivariate classification in Laser-Induced Breakdown Spectroscopy (LIBS) analyses. For the purpose of classification we chose to analyse BAM steel standards that possess similar composition of major and trace elements. To assess the improvement in figures of merit we compared the performance of three outlier filtering approaches (based on Principal Component Analysis, linear correlation and total spectral intensity) already separately discussed in the LIBS literature. The truncated data set was classified using Soft Independent Modelling of Class Analogies (SIMCA). Yielded results showed significant improvement in the performance of multivariate classification coupled to filtered data. The best performance was observed for the total spectral intensity filtering approach gaining the analytical figures of merit (overall accuracy, sensitivity, and specificity) over 98%. It is noteworthy that the results showed relatively low sensitivity and high specificity of the SIMCA algorithm regardless of the presence of outliers in the data sets. Moreover, it was shown that the variance in the data topology of training and testing data sets has a great impact on the consequent data classification.

Stand-off LIBS; Laser-induced breakdown spectroscopy; Classification; PCA; LDA; Cultural heritage;

Focusing on historical aspect, during archeological excavation or restoration works of buildings or different structures built from Brick it is important to determine, preferably in-situ and in real-time, the locality of Brick origin. Fast classification of Brick on the base of Laser-Induced Breakdown Spectroscopy (LIBS) spectra is possible using multivariate statistical methods. Combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was applied in this case. LIBS was used to classify altogether the 29 brick samples from 7 different localities. Realizing comparative study using two different LIBS setups — stand-off and table-top it is shown that stand-off LIBS has a big potential for archeological in-field measurements.

Stand‐off LIBS; Laser-induced breakdown spectroscopy; PCA; LDA; ANN; Biomineral; Geomaterial; Archaeology;

The goal of this paper is to compare two selected statistical techniques used for identification of archeological materials merely on the base of their spectra obtained by stand-off laser-induced breakdown spectroscopy (stand-off LIBS). Data processing using linear discriminant analysis (LDA) and artificial neural networks (ANN) were applied on spectra of 18 different samples, some of them archeological and some recent, containing 7 types of material (i.e. shells, mortar, Brick, soil pellets, ceramic, teeth and bones). As the input data PCA scores were taken. The intended aim of this work is to create a database for simple and fast identification of archeological or paleontological materials in situ. This approach can speed up and simplify the sampling process during archeological excavations that nowadays tend to be quite damaging and time-consuming.

Laser-induced breakdown spectroscopy (LIBS) and laser ablation (LA) inductively coupled plasma (ICP) mass spectrometry (MS) were utilized for microspatial analyses of a prehistoric bear (Ursus arctos) tooth dentine. The distribution of selected trace elements (Sr, Ba, Fe) was measured on a 26 mm×15 mm26 mm×15 mm large and 3 mm3 mm thick transverse cross section of a canine tooth. The Na and Mg content together with the distribution of matrix elements (Ca, P) was also monitored within this area. The depth of the LIBS craters was measured with an optical profilometer. As shown, both LIBS and LA-ICP-MS can be successfully used for the fast, spatially resolved analysis of prehistoric teeth samples. In addition to micro chemical analysis, the sample hardness was calculated using LIBS plasma ionic-to-atomic line intensity ratios of Mg (or Ca). To validate the sample hardness calculations, the hardness was also measured with a Vickers microhardness tester.