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; Generalized extreme value distribution; shot-to-shot statistics ;

The purpose of this work is to provide detailed study of statistical behavior of different types of analytical signals in typical of Laser-Induced Breakdown Spectroscopy (LIBS) measurements. The main goal of this work is to justify usage of arithmetic mean and standard deviation as statistical estimates of expected value of selected analytical signal. In contrary to the general assumption that LIBS data show Gaussian distribution, this paper deals with the hypothesis that the data rather demonstrate Generalized Extreme Value Distribution. The study is realized on 10 selected lines measured on NIST glass standard. In order to cover wide range of possible applications three different spectra internal standardization techniques and their influence on distribution were studied. Finally, assuming that the data comes from a single distribution and the central limit theorem is valid, the influence of accumulations on the line distribution is examined and discussed. Statistical tools used and described in this paper can be utilized by other researchers to confirm their hypotheses and verify utilization of Gaussian distribution or even novel data processing methods.

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; 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.

Quantum dots; Laser-induced breakdown spectroscopy; Chemical mapping; Filtration paper; Fluorescence microscopy;

In this study, the feasibility of Quantum dots (QDs) 2D distribution mapping on the substrate by Laser-Induced Breakdown Spectroscopy (LIBS) was examined. The major objective of this study was to describe phenomena occurring after applying aqueous solutions of QDs onto filtration paper. Especially, the influence of pH and presence of Cu2 + cations in QDs solutions on LIBS signal was investigated. Cadmium Telluride QDs (CdTe QDs) were prepared by formation of nanosized semiconductor particles in so called “one-pot” synthesis. CdTe QDs were capped by glutathione or by 3-mercaptopropionic acid. The technique described in this work allows detection of QDs injected on the selected substrate – filtration paper. Results obtained from LIBS experiments were collated with a comparative method, fluorescence microscopy, which showed variations in the distribution of QDs on the substrate surface and possibilities for quenching. Due to the immediate signal response, relatively simple instrumentation and automatization possibility, LIBS offers promising and fast alternative to other techniques, as it is able to detect also nanoparticles with no visible luminescence.

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; 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.

Braking tracks; Forensic; LIBS; Tire treads; Laser-Induced Breakdown Spectroscopy;

Identification of the position, length and mainly beginning of a braking track has proven to be essential for determination of causes of a road traffic accident. With the introduction of modern safety braking systems and assistance systems such as the Anti-lock Braking System (ABS) or Electronic Stability Control (ESC), the visual identification of braking tracks that has been used up until the present is proving to be rather complicated or even impossible. This paper focuses on identification of braking tracks using a spectrochemical analysis of the road surface. Laser-Induced Breakdown Spectroscopy (LIBS) was selected as a method suitable for fast in-situ element detection. In the course of detailed observations of braking tracks it was determined that they consist of small particles of tire treads that are caught in intrusions in the road surface. As regards detection of the “dust” resulting from wear and tear of tire treads in the environment, organic zinc was selected as the identification element in the past. The content of zinc in tire treads has been seen to differ with regard to various sources and tire types; however, the arithmetic mean and modus of these values are approximately 1% by weight. For in-situ measurements of actual braking tracks a mobile LIBS device equipped with a special module was used. Several measurements were performed for 3 different cars and tire types respectively which slowed down with full braking power. Moreover, the influence of different initial speed, vehicle mass and braking track length on detected signal is discussed here.

We present a report on the potential use of the Laser-Induced Breakdown Spectroscopy (LIBS) technique for direct investigation of Mg corrosion and related optimization of the table-top LIBS system. Moreover, the preliminary study to prove the capability of this LIBS technique for the estimation of corrosion grade is given. In order to simulate the real corrosive environment, Mg samples were prepared in a constant climate chamber. We show that the corrosive layer on the sample surface significantly affects the properties of laser–matter interactions, i.e. among other parameters causing the matrix effect. Consequently, the properties and persistence of laser-induced plasmas (LIPs) and their composition, generated on such degraded surfaces, essentially differ. Collected radiation of LIP is then analysed and ratios of ionic to atomic Mg spectral lines are correlated with the grade of magnesium corrosion, i.e. content of Mg(OH)2 on the sample surface. The content of Mg(OH)2 is also correlated with plasma temperature as well as with the electron number density of LIP. Additionally, X-ray diffraction (XRD) analysis and optical profilometry were utilized to obtain more comprehensive information about the degradation grade of high purity Mg samples.

LIBS; Laser-induced breakdown spectroscopy; Tire tread; Optimization ;

The objective of this paper is a study of the potential of laser induced breakdown spectroscopy (LIBS) for detection of tire tread particles. Tire tread particles may represent pollutants; simultaneously, it is potentially possible to exploit detection of tire tread particles for identification of optically imperceptible braking tracks at locations of road accidents. The paper describes the general composition of tire treads and selection of an element suitable for detection using the LIBS method. Subsequently, the applicable spectral line is selected considering interferences with lines of elements that might be present together with the detected particles, and optimization of measurement parameters such as incident laser energy, gate delay and gate width is performed. In order to eliminate the matrix effect, measurements were performed using 4 types of tires manufactured by 3 different producers. An adhesive tape was used as a sample carrier. The most suitable adhesive tape was selected from 5 commonly available tapes, on the basis of their respective LIBS spectra. Calibration standards, i.e. an adhesive tape with different area content of tire tread particles, were prepared for the selected tire. A calibration line was created on the basis of the aforementioned calibration standards. The linear section of this line was used for determination of the detection limit value applicable to the selected tire. Considering the insignificant influence of matrix of various types of tires, it is possible to make a simple recalculation of the detection limit value on the basis of zinc content in a specific tire.