F. Anabitarte

Laser-Induced Breakdown Spectroscopy: Fundamentals, Applications, and Challenges

Laser-induced breakdown spectroscopy (LIBS) is a technique that provides an accurate in situ quantitative chemical analysis and, thanks to the developments in new spectral processing algorithms in the last decade, has achieved a promising performance as a quantitative chemical analyzer at the atomic level. These possibilities along with the fact that little or no sample preparation is necessary have expanded the application fields of LIBS. In this paper, we review the state of the art of this technique, its fundamentals, algorithms for quantitative analysis or sample classification, future challenges, and new application fields where LIBS can solve real problems.

Sensor for the Detection of Protective Coating Traces on Boron Steel With Aluminium–Silicon Covering by Means of Laser-Induced Breakdown Spectroscopy and Support Vector Machines

Welding processes are one of the most widespread industrial activities, and their quality control, in both online and offline methods, is an important area of research. In the particular process of laser welding of boron steel with aluminium-silicon covering in the automotive industry, one problem is the presence of residual traces from the protective antioxidant coating, an aluminium-silicon alloy, which can result in a significant reduction of the welding seam strength. This work proposes a sensor system based on a laser induced breakdown spectroscopy (LIBS) setup to detect and discriminate aluminium residues in the welding area without destroying the sample before the welding procedure. A spectral algorithm based on support vector machines (SVMs) is used as a classifier to automatically identify areas with aluminum presence.

Support vector machines in hyperspectral imaging spectroscopy with application to material identification

A processing methodology based on Support Vector Machines is presented in this paper for the classification of hyperspectral spectroscopic images. The accurate classification of the images is used to perform on-line material identification in industrial environments. Each hyperspectral image consists of the diffuse reflectance of the material under study along all the points of a line of vision. These images are measured through the employment of two imaging spectrographs operating at Vis-NIR, from 400 to 1000 nm, and NIR, from 1000 to 2400 nm, ranges of the spectrum, respectively. The aim of this work is to demonstrate the robustness of Support Vector Machines to recognise certain spectral features of the target. Furthermore, research has been made to find the adequate SVM configuration for this hyperspectral application. In this way, anomaly detection and material identification can be efficiently performed. A classifier with a combination of a Gaussian Kernel and a non linear Principal Component Analysis, namely k-PCA is concluded as the best option in this particular case. Finally, experimental tests have been carried out with materials typical of the tobacco industry (tobacco leaves mixed with unwanted spurious materials, such as leathers, plastics, etc.) to demonstrate the suitability of the proposed technique.

Automatic classification of steel plates based on laser induced breakdown spectroscopy and support vector machines

Welding processes are one of the most widely spread industrial activities, and their quality control is an important area of research. The presence of residual traces from the protective antioxidant coating, is a problematic issue since it causes a significant reduction in the welding seam strength. In this work, a solution based on a Laser Induced Breakdown Spectroscopy (LIBS) setup and a Support Vector Machines (SVMs) classifier to detect and discriminate antioxidant coating residues in the welding area without destroying the sample before the welding procedure is proposed. This system could be an interesting and fast tool to detect aluminium impurities.

Spectral and Optimized Marks for Qualitative Material Discrimination

A method for automatic qualitative classification of liquid samples based on their absorption spectrum in the ultraviolet, visible and near-infrared regions is presented. A fiber-optic setup has been employed for material characterization. A simple technique based on image matching is proposed to perform spectra comparison. This alternative implementation of conventional spectrum matching methodologies can be easily translated to hardware platforms thus improving the time response of the classification system. The proposed method does not make any assumption on the probability density function of the data and it is also capable of automatic outlier removal. “Spectral marks” based on the polar representation of the absorption spectra and their “optimized marks” improved by means of Principal Component Analysis have been implemented. Preliminary discrimination results have been obtained on the classification of different oil samples from seeds and olives.

Normalization of laser-induced breakdown spectroscopy spectra using a plastic optical fiber light collector and acoustic sensor device

To estimate the acoustic plasma energy in laser-induced breakdown spectroscopy (LIBS) experiments, a light collecting and acoustic sensing device based on a coil of plastic optical fiber (POF) is proposed. The speckle perturbation induced by the plasma acoustic energy was monitored using a CCD camera placed at the end of a coil of multimode POF and processed with an intraimage contrast ratio method. The results were successfully verified with the acoustic energy measured by a reference microphone. The proposed device is useful for normalizing LIBS spectra, enabling a better estimation of the samples chemical composition.

Tunable fiber laser using concatenated non-adiabatic single-mode fiber tapers

An erbium doped fiber ring laser (EDFRL) that incorporates four non-adiabatic concatenated single-mode fiber tapers (acting as tunable filter in the laser cavity) is presented. These concatenated fiber tapers integrates a filter with a narrower band-pass and a higher modulation depth than a single taper. The tuning of this filter was implemented applying a controlled perturbation in the fiber taper. The proposed laser architecture was successfully demonstrated in the laboratory in which a tuning range of 20.8nm (1544.5nm-1565.3nm) were measured.

Focal beam position detection in a laser induced breakdown spectroscopy system by using a fiber Bragg grating sensor

Chemical analysis of dangerous materials entails a safety issue for the researchers. Laser Induced Breakdown Spectroscopy offers the possibility to analyze these materials away from them using Stand-Off Set-ups. To optimize the plasma induction, the remote focalization of the laser beam is of paramount importance. A custom Fiber Bragg Grating sensor system able to correct the laser beam focalization errors is proposed and experimentally checked. The optical transducer architecture and the preliminary obtained results are reported in this paper.

Integral temperature hybrid laser sensor

An integral temperature sensor based on Brillouin laser ring that is feed by a Fourier Domain mode-locking (FDML) laser is here proposed. The source FDML laser ring emits at 1532 nm within a range of 5 nm. The working wavelength is given by tuning the offset voltage applied to a Fabry-Perot tunable filter (FFP-TF). In the present work, the FDML laser linewidth is set at 0.136 nm. This linewidth allows a more efficient Brillouin response in the optical fiber without increasing the Brillouin threshold. The FDML laser linewidth is controlled by setting the amplitude modulation of the drive signal offset applied on the FFP-TF.

Spectral marks for qualitative discriminant analysis

In this paper, a method for the automatic qualitative discrimination of liquid samples based on their absorption spectrum in the ultraviolet, visible and near-infrared regions is presented. An alternative implementation of conventional spectrum matching methodologies is proposed working towards the improvement of the response time of the discrimination system. The method takes advantage of not making assumptions on the probability density function of the data and it is also capable of automatic outlier removal. Preliminary discrimination results have been evaluated on the classification of different oil samples from seeds and olives. The system here proposed could be easily and efficiently implemented in hardware platforms, improving in this way the system performance.