Javier Moros

Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy. Atoms are excited from the lower energy level to high energy level when they are in the high energy status. The conventional excitation energy source can be a hot flame, light or high temperature plasma. The excited energy that holds the atom at the higher energy level will be released and the atom returns to its ground state eventually. The released energy is welldefined for the specific excited atom, and this characteristic process utilizes emission spectroscopy for the analytical method. LIBS employs the laser pulse to atomize the sample and leads to atomic emission. Compared to the conventional flame emission spectroscopy, LIBS atomizes only the small portion of the sample by the focused laser pulse, which makes a tiny spark on the sample. Because of the short-life of the spark emission, capturing the instant light is a major skill to collect sufficient intensity of the emitting species. Three major parts of the LIBS system are a pulse laser, sample, and spectrometer. Control system is usually needed to manage timing and the spectrum capturing. Figure 1 illustrates those three major components and a computer in the conventional LIBS.

Simultaneous Raman Spectroscopy-Laser-Induced Breakdown Spectroscopy for Instant Standoff Analysis of Explosives Using a Mobile Integrated Sensor Platform

A novel experimental design combining Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) in a unique integrated sensor is described. The sensor presented herein aims to demonstrate the applicability of a hybrid dual Raman-LIBS system as an analytical tool for the standoff analysis of energetic materials. Frequency-doubled 532 nm Nd:YAG nanosecond laser pulses, first expanded and then focused using a 10x beam expander on targets located at 20 m, allowed simultaneous acquisition of Raman-LIBS spectra for 4-mononitrotoluene (MNT), 2,6-dinitrotoluene (DNT), 2,4,6-trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), C4 and H15 (plastic explosives containing 90% and 75% of RDX by weight, respectively), and Goma2-ECO (Spanish denominated dynamite class high explosive mainly composed of ammonium nitrate, nitroglycol, and dinitrotoluene among other compounds), sodium chlorate, and ammonium nitrate. With the use of a Cassegrain telescope, both Raman and LIBS signals from the same laser pulses were collected and conducted through a bifurcated optical fiber into two identical grating spectrographs coupled to intensified charge-coupled device (iCCD) detectors. With the use of the appropriate timing for each detection mode, adjustment of the laser power on the beam focal conditions is not required. The ability of the present single hybrid sensor to simultaneously acquire, in real time, both molecular and multielemental information from the same laser pulses on the same cross section of the sample at standoff distances greatly enhances the information power of this approach.

Use of Reflectance Infrared Spectroscopy for Monitoring the Metal Content of the Estuarine Sediments of the Nerbioi-Ibaizabal River (Metropolitan Bilbao, Bay of Biscay, Basque Country)

Multivariate partial least-squares (PLS) calibration models have been developed for the spatial and seasonal simultaneous monitoring of 14 trace elements (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V, and Zn) in sediments from 117 samples taken in the estuary of the Nerbioi-Ibaizabal River. Models were based on the chemometric treatment of diffuse reflectance near-infrared (NIR) and attenuated total reflectance (ATR) mid infrared (MIR) spectra, obtained from samples previously lyophilized and sieved with a particle size lower than 63 microm. Vibrational spectra were scanned in both, NIR and MIR regions. Developed PLS models, based on the interaction between trace elements and organic mater provide good screening tools for the prediction of trace elements concentration in sediments.

The use of near-infrared spectrometry in the olive oil industry.

The enormous possibilities offered by near-infrared (NIR) spectroscopy for the (on/in/at-line) quality control process of olive fruits, pastes, and oils are summarized throughout this paper. Special attention has been paid to the combination of NIR and chemometric treatments for the on-line analysis of olive fruits and also for the quality parameters evaluation on olive oils and pastes which can enhance the production of a high quality olive oil and the selection of olive fruit with superior properties. The implementation of NIR sensors in olive mills with successful results has also been reviewed and the commercial olive fruit and oil analyzers highlighted.

Standoff detection of explosives: critical comparison for ensuing options on Raman spectroscopy–LIBS sensor fusion

In general, any standoff sensor for the effective detection of explosives must meet two basic requirements: first, a capacity to detect the response generated from only a small amount of material located at a distance of several meters (high sensitivity) and second, the ability to provide easily distinguishable responses for different materials (high specificity). Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) are two analytical techniques which share similar instrumentation and, at the same time, generate complementary data. These factors have been taken into account recently for the design of sensors used in the detection of explosives. Similarly, research on the proper integration of both techniques has been around for a while. A priori, the different operational conditions required by the two techniques oblige the acquisition of the response for each sensor through sequential analysis, previously necessary to define the proper hierarchy of actuation. However, such an approach does not guarantee that Raman and LIBS responses obtained may relate to each other. Nonetheless, the possible advantages arising from the integration of the molecular and elemental spectroscopic information come with an obvious underlying requirement, simultaneous data acquisition. In the present paper, strong and weak points of Raman spectroscopy and LIBS for solving explosives detection problems, in terms of selectivity, sensitivity, and throughput, are critically examined, discussed, and compared for assessing the ensuing options on the fusion of the responses of both sensing technologies.

New Raman–Laser-Induced Breakdown Spectroscopy Identity of Explosives Using Parametric Data Fusion on an Integrated Sensing Platform

The principal goal of sensors for the detection of explosives is to establish the identity of the interrogated target as a key to threat assessment and decision making. Despite the fact that both Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) have shown their capability in standoff detection of explosives, such techniques are not exempt from certain limitations, in terms of sensitivity and selectivity, to carry out this purpose when they are used individually. For this reason, the idea for the fusion of data reported by these orthogonal techniques, Raman and LIBS, has been around for a while. The present manuscript proposes an approach for the combination of the spectral outputs of Raman and LIBS sensors (data fusion strategy) in order to obtain knowledge about the identity of compounds better than that achieved when each technique acts alone. After simple mathematical treatment, a new pattern of identification (two-dimensional, 2D, image) of several compounds (explosives, confusants, and supports) was generated from the assembly of their Raman and LIBS spectra. The efficiency of this strategy was evaluated by comparing the results obtained for differentiation between compounds using simple correlation coefficient values from the 2D images and those achieved using Raman and LIBS spectra separately. Additionally, the effect of two spectral pretreatments (autoscaling and normalization) on the generation of the 2D image was evaluated. The results derived from this study demonstrate that the 2D image improves the identification of compounds, mainly in those critical situations in which it is not easy to differentiate them when Raman spectroscopy or LIBS is used separately.

Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy.

It has been evaluated the potential of near-infrared (NIR) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a way for non-destructive measurement of trace elements at microg kg(-1) level in foods, with neither physical nor chemical pre-treatment. Predictive models were developed using partial least-square (PLS) multivariate approaches based on first-order derivative spectra. A critical comparison of two spectral pre-treatments, multiplicative signal correction (MSC) and standard normal variate (SNV) was also made. The PLS models built after using SNV provided the best prediction results for the determination of arsenic and lead in powdered red paprika samples. Relative root-mean-square error of prediction (RRMSEP) of 23% for both metals, arsenic and lead, were found in this study using 20 well characterized samples for calibration and 13 additional samples as validation set. Results derived from this study showed that NIR diffuse reflectance spectroscopy combined with the appropriate chemometric tools could be considered as an useful screening tool for a rapid determination of As and Pb at concentration level of the order of hundred microg kg(-1).

Testing of the Region of Murcia soils by near infrared diffuse reflectance spectroscopy and chemometrics

A partial least squares near infrared (PLS-NIR) method has been developed for the determination of several physicochemical parameters in soils from different locations of the Region of Murcia. The method was based on the proper chemometric treatment of diffuse reflectance spectra of soil samples. Reflectance spectra were scanned from samples stored in glass vials in the NIR region between 800 and 2600 nm, averaging 36 scans per spectrum at a resolution of 8 cm(-1). Models were built using reference data of 39 samples selected from a dendrogram obtained after hierarchical cluster analysis of NIR spectra of soils and prediction parameters were established from a validation set of 109 additional samples of the same area not considered to build the model. Organic matter, CaCO(3), pH, electrical conductivity (EC), together with several trace metals as Cr, Co, Ni, Cu, Zn, As, Se, Cd and Tl, were employed as characteristic parameters of the soils under study, and found results evidenced that PLS-NIR provides a valuable tool for screening purposes providing residual predictive deviations which ranged from 0.9 to 1.5 as a function of the considered parameter.

Nondestructive Direct Determination of Heroin in Seized Illicit Street Drugs by Diffuse Reflectance near-Infrared Spectroscopy

A new method has been developed for the fast and nondestructive direct determination of heroin in seized street illicit drugs using partial least-squares regression analysis of diffuse reflectance near-infrared spectra. Data were obtained from untreated samples placed in standard glass chromatography vials. A heterogeneous population of 31 samples, previously analyzed by a reference method, was employed to build the calibration model and to have a separated validation set. Based on the use of zero-order data for a calibration set of 21 samples, after standard normal variate and quadratic linear removed baseline correction (detrending), in the wavelength range from 1111 to 1647 nm, 8 PLS factors were enough to obtain a root-mean-square error of prediction of 1.3% w/w, with a quality coefficient of 10% for the estimation of the accuracy error in the prediction of heroin concentration in unknown samples and a residual predictive deviation of 5.4.

New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in illicit street drugs

A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. However, the repeatability of the number of selected variables was improved significantly.