Differentiation of closely related mineral phases in Mars atmosphere using frequency domain laser-induced plasma acoustics.

The combination of data yielded by laser-induced breakdown spectroscopy (LIBS) and laser-induced plasma acoustics (LIPAc) is a topic of many prospective applications as these coexisting phenomena can cover different sample traits. Among the most interesting features that LIPAc could add to the expanded target picture is information concerning structure and geophysical characteristics elusive to LIBS. In the present work, frequency spectra of minerals were explored to discriminate between chemically similar mineralogical phases. Several replicas of four different Fe-based minerals were analyzed to identify spectral traits linked to their chemistry in the frequency domain. First, the similarity between replicas of the same mineral family was verified and then, the cosine and Euclidian distances to minerals of different species were calculated to evaluate the discrimination capabilities of frequency spectra with results being compared to those obtained by LIBS. A partial least-squares one-vs-all model is described seeking to demonstrate sample classification by frequency means exclusively. As the use of LIBS-LIPAc for in-field mineral sorting has sparked interest, experiments reported were performed in stand-off within a thermal vacuum chamber (TVC). The TVC allowed data acquisition under Earth and Mars-like conditions, with the latter serving as a test of high relevance to assess the general applicability of the conclusions reached in Earth environment. Thorough discussion of data treatment is included with a focus on the impact of interference patterns arising from the laser-induced shockwave interaction with the medium surrounding the sample to avoid non-sample related information in the data processing schemes.

A systematic evaluation on the impact of sample-related and environmental factors in the analytical performance of acoustic emission from laser-induced plasmas.

Acoustics recordings from laser-induced plasmas are becoming increasingly regarded as a complementary source of information from the inspected sample. The propagation of these waves is susceptible to be modified by the physicochemical traits of the sample, thus yielding specific details that can be used for sorting and identification of targets. Still, the relative fragility of the acoustic wave poses major challenges to the applicability of laser-induced acoustics. Echoes and reflections sourcing from intrasample parameters as well as from interactions of the acoustic wave with the surroundings of the inspected target can dilute the analytical information directly related to the object contained within the recordings. The present work aims to experimentally scrutinize the impact of different parameters internal and external to the sample into the final acoustic signal from laser-induced plasmas in order to accurately use this information source for characterization purposes. Variables inherent to the sample such as dimensions, porosity and absorption coefficient, which guides the laser-matter coupling process, have been, for the first time, systematically studied using ad-hoc solids to thoroughly isolate their influence on the signal. Moreover, modulation of soundwave induced by the surroundings of the probed target and the anisotropy of the acoustic signal because of the angle at which the plasma is formed, have been evaluated.

Pressure Effects on Simultaneous Optical and Acoustics Data from Laser-Induced Plasmas in Air: Implications to the Differentiation of Geological Materials.

The shockwave generated alongside the plasma is an intimately linked, yet often neglected additional input for the characterization of solid samples by laser-induced breakdown spectroscopy (LIBS). The present work introduces a dual LIBS-acoustics sensor that takes advantage of the analysis of the acoustic spectrum yielded by shockwaves produced on different geological samples to enhance the discrimination power of LIBS in materials featuring similar optical emission spectra. Six iron-based minerals were tested at a distance of 2 m using 1064 nm laser light and under pressure values ranging from 7 to 1015 mbar. These experimental parameters were selected to assess the effects of pressure, one of the main factors conditioning the propagation of sound as well as a commonly investigated influence in LIBS experiments. Moreover, precise values for carrying out the analyses were set based on one of the most exciting scenarios in which LIBS data may be enhanced by laser-induced acoustics: space exploration. This is exemplified by the tasks performed by the Mars 2020 SuperCam instrument located onboard the Perseverance rover. Authors evaluated the use of acoustic signals both in the time-domain and frequency-domain in sensitive cases for the distinguishing of minerals exhibiting LIBS spectra featuring almost the same emission lines using PCA schemes for each pressure setting. Thus, we report herein the impact of the surrounding pressure level upon this diagnostic tool. Overall, this paper seeks to show how the analytical potential of simultaneous phenomena taking place during a laser-produced plasma event is subjected to the defined operational conditions.

Refractory residues classification strategy using emission spectroscopy of laser-induced plasmas in tandem with a decision tree-based algorithm.

The recycling of refractory scraps began to be forged just over a decade ago. Until then, virtually all refractory scraps were disposed off in landfill sites without any application. Over these past few years, a growing interest and a gain steady momentum of the circular economy, the emergent framing around waste and resource management that promotes the notions of their productive cycling, has been the driving force towards the "zero waste" culture across the spectrum of refractory users and producers. In this way, the circular economy, operated following strategies such as, but not limited to, reusing, recycling, and remanufacturing, has played the pillar role in the different essential value chains of the refractory industry to the entering the new era of secondary raw material supply. In any case, prior to starting any sustainable process, it is really necessary to know the wastes and to classify them. In this context, the present research focused on a refractory residue-classification strategy based on combined laser-induced breakdown spectroscopy (LIBS) and a decision tree algorithm for a qualitative analytical performance. This tandem approach allowed the categorization of a rich set of residues in up to 10 different refractory groups. By choosing original LIBS emission intensities and intensity ratios involving the most relevant constituent elements (Al, Mg, C ‒through its related-species CN‒, Si and Zr) of various refractory wastes, a decision tree with multiple nodes that decided how to classify inputs was designed and trained. Categorization performed from LIBS emission spectra of "blind" refractory residues showed that LIBS data combined with this supervised machine learning algorithm provided good refractory scraps-classification performance, with a classification accuracy of up to 75%. However, some more than justified decisions of the algorithm on allegedly misclassified residues showed that scores for the decision tree could found to be far superior to those obtained. The results achieved support the strategy designed for its industrial implementation, either directly in the iron and steel industry, as the major end-user of refractories, in the refractory waste management industry, or in both.

LIBS-Acoustic Mid-Level Fusion Scheme for Mineral Differentiation under Terrestrial and Martian Atmospheric Conditions.

The shockwave produced alongside the plasma during a laser-induced breakdown spectroscopy event can be recorded as an acoustic pressure wave to obtain information related to the physical traits of the inspected sample. In the present work, a mid-level fusion approach is developed using simultaneously recorded laser-induced breakdown spectroscopy (LIBS) and acoustic data to enhance the discrimination capabilities of different iron-based and calcium-based mineral phases, which exhibit nearly identical spectral features. To do so, the mid-level data fusion approach is applied concatenating the principal components analysis (PCA)-LIBS score values with the acoustic wave peak-to-peak amplitude and with the intraposition signal change, represented as the slope of the acoustic signal amplitude with respect to the laser shot. The discrimination hit rate of the mineral phases is obtained using linear discriminant analysis. Owing to the increasing interest for in situ applications of LIBS + acoustics information, samples are inspected in a remote experimental configuration and under two different atmospheric traits, Earth and Mars-like conditions, to validate the approach. Particularities conditioning the response of both strategies under each atmosphere are discussed to provide insight to better exploit the complex phenomena resulting in the collected signals. Results reported herein demonstrate for the first time that the characteristic sample input in the laser-produced acoustic wave can be used for the creation of a statistical descriptor to synergistically improve the capabilities of LIBS of differentiation of rocks.

Dual-Spectroscopy Platform for the Surveillance of Mars Mineralogy Using a Decisions Fusion Architecture on Simultaneous LIBS-Raman Data.

A single platform, integrated by a laser-induced breakdown spectroscopy detector and a Raman spectroscopy sensor, has been designed to remotely (5 m) and simultaneously register the elemental and molecular signatures of rocks under Martian surface conditions. From this information, new data fusion architecture at decisions level is proposed for the correct categorization of the rocks. The approach is based on a decision-making process from the sequential checking of the spectral features representing the cationic and anionic counterparts of the specimen. The scrutiny of the LIBS response by using a moving-window algorithm informs on the diversity of the elemental constituents. The output rate of emission lines allows projecting in a loop the elements as the cationic counterpart of the rock. In parallel, the Raman response of the unknown is compared with all the molecular counterparts of the hypothesized cation that are stored in a spectral library. The largest similarity rate unveils the final identity of the unknown. The identification capabilities of the architecture have been underscored through blind tests of 10 natural rocks with different origins. The great majority of forecasts have matched with the real identities of the inspected targets. The strength of this platform to simultaneously acquire the multielemental and the molecular information from a specimen by using the same laser events greatly enhances the "on-surface" missions for the surveillance of mineralogy.

Remotely Exploring Deeper-Into-Matter by Non-Contact Detection of Audible Transients Excited by Laser Radiation.

An acoustic spectroscopic approach to detect contents within different packaging, with substantially wider applicability than other currently available subsurface spectroscopies, is presented. A frequency-doubled Nd:YAG (neodymium-doped yttrium aluminum garnet) pulsed laser (13 ns pulse length) operated at 1 Hz was used to generate the sound field of a two-component system at a distance of 50 cm. The acoustic emission was captured using a unidirectional microphone and analyzed in the frequency domain. The focused laser pulse hitting the system, with intensity above that necessary to ablate the irradiated surface, transferred an impulsive force which led the structure to vibrate. Acoustic airborne transients were directly radiated by the vibrating elastic structure of the outer component that excited the surrounding air in contact with. However, under boundary conditions, sound field is modulated by the inner component that modified the dynamical integrity of the system. Thus, the resulting frequency spectra are useful indicators of the concealed content that influences the contributions originating from the wall of the container. High-quality acoustic spectra could be recorded from a gas (air), liquid (water), and solid (sand) placed inside opaque chemical-resistant polypropylene and stainless steel sample containers. Discussion about effects of laser excitation energy and sampling position on the acoustic emission events is reported. Acoustic spectroscopy may complement the other subsurface alternative spectroscopies, severely limited by their inherent optical requirements for numerous detection scenarios.

Standoff monitoring of aqueous aerosols using nanosecond laser-induced breakdown spectroscopy: droplet size and matrix effects.

Nanosecond laser-induced breakdown spectroscopy has been examined for the analysis of suspended matter in a free stream of air. The real-time monitoring of this scenario poses major challenges for an accurate categorization due to its changing characteristics such as composition, size, and density of particles. The effects of particle size and matrix in the optical emission responses registered from such scenarios have been evaluated. Distant (10 m) plasmas of saline solutions, containing either NaCl or Na2SO4 at different concentrations, have been induced by nanosecond laser pulses at a wavelength of 1064 nm. The effects of the droplet size and its concentration on differences in the laser-induced breakdown probability, the intensity of the characteristic lines, and the plasma emission continuum have been discussed. The quantification of sodium in distant water droplets has been proved. However, an initial knowledge on the average droplet size is required. The average droplet size could be determined from the slope of H I and O I lines versus the continuum plasma emission, which is only weakly influenced by the salt content in the droplets.

Molecular signatures in femtosecond laser-induced organic plasmas: comparison with nanosecond laser ablation.

During the last few years, laser-induced breakdown spectroscopy (LIBS) has evolved significantly in the molecular sensing area through the optical monitoring of emissions from organic plasmas. Large efforts have been made to study the formation pathways of diatomic radicals as well as their connections with the bonding framework of molecular solids. Together with the structural and chemical-physical properties of molecules, laser ablation parameters seem to be closely tied to the observed spectral signatures. This research focuses on evaluating the impact of laser pulse duration on the production of diatomic species that populate plasmas of organic materials. Differences in relative intensities of spectral signatures from the plasmas of several organic molecules induced in femtosecond (fs) and nanosecond (ns) ablation regimes have been studied. Beyond the abundance and origin of diatomic radicals that seed the plasma, findings reveal the crucial role of the ablation regime in the breakage pattern of the molecule. The laser pulse duration dictates the fragments and atoms resulting from the vaporized molecules, promoting some formation routes at the expense of other paths. The larger amount of fragments formed by fs pulses advocates a direct release of native bonds and a subsequent seeding of the plasma with diatomic species. In contrast, in the ns ablation regime, the atomic recombinations and single displacement processes dominate the contribution to diatomic radicals, as long as atomization of molecules prevails over their progressive decomposition. Consequently, fs-LIBS better reflects correlations between strengths of emissions from diatomic species and molecular structure as compared to ns-LIBS. These new results entail a further step towards the specificity in the analysis of molecular solids by fs-LIBS.

Direct determination of the nutrient profile in plant materials by femtosecond laser-induced breakdown spectroscopy.

Femtosecond laser-induced breakdown spectroscopy (fs-LIBS) has been used for the first time for quantitative determination of nutrients in plant materials from different crops. A highly heterogeneous population of 31 samples, previously analyzed by inductively coupled plasma optical emission spectroscopy, covering a wide range of matrices was interrogated. To tackle the analysis, laser-induced plasmas under argon atmosphere of pellets prepared from sieved cryogenically ground leaves were studied. Predictive functions based on univariate and multivariate modeling of optical emissions associated to macro- (Ca, Mg, and P) and micronutrients (Cu, Fe, Mn and Zn) were designed. Hierarchical cluster analysis was performed to select representative calibration (n(cal)=17) and validation (n(val)=14) datasets. The predictive performance of calibration functions over fs-LIBS data was compared with that attained on spectral information from nanosecond LIBS (ns-LIBS) operating at different wavelengths (1064 nm, 532 nm, and 266 nm). Findings established higher accuracy and less uncertainty on mass fractions quantification from fs-LIBS, whatever the modeling approach. Quality coefficients below 20% for the accuracy error on mass fractions' prediction in unknown samples, and residual predictive deviations in general above 5, were obtained. In contrast, only multivariate modeling satisfactorily handled the non-linear variations of emissions in ns-LIBS, leading to 2-fold decrease in the root mean square error of prediction (RMSEP) of Ca, Mg, P, Cu, Fe, Mn and Zn in comparison with the univariate approach. But still, an averaged quality coefficient about 35% and residual predictive deviations below 3 were found. Similar predictive capabilities were observed when changing the laser wavelength. Although predicted values by ns-LIBS multivariate modeling exhibit better agreement with reference mass fractions as compared to univariate functions, fs-LIBS conducts better quantification of nutrients in plant materials since it is less dependent on the chemical composition of the matrices.

Sensing signatures mediated by chemical structure of molecular solids in laser-induced plasmas.

Laser ablation of organic compounds has been investigated for almost 30 years now, either in the framework of pulse laser deposition for the assembling of new materials or in the context of chemical sensing. Various monitoring techniques such as atomic and molecular fluorescence, time-of-flight mass spectrometry, and optical emission spectroscopy have been used for plasma diagnostics in an attempt to understand the spectral signature and potential origin of gas-phase ions and fragments from organic plasmas. Photochemical and photophysical processes occurring within these systems are generally much more complex than those suggested by observation of optical emission features. Together with laser ablation parameters, the structural and chemical-physical properties of molecules seem to be closely tied to the observed phenomena. The present manuscript, for the first time, discusses the role of molecular structure in the optical emission of organic plasmas. Factors altering the electronic distribution within the organic molecule have been found to have a direct impact on its ensuing optical emissions. The electron structure of an organic molecule, resulting from the presence, nature, and position of its atoms, governs the breakage of the molecule and, as a result, determines the extent of atomization and fragmentation that has proved to directly impact the emissions of CN radicals and C2 dimers. Particular properties of the molecule respond more positively depending on the laser irradiation wavelength, thereby redirecting the ablation process through photochemical or photothermal decomposition pathways. It is of paramount significance for chemical identification purposes how, despite the large energy stored and dissipated by the plasma and the considerable number of transient species formed, the emissions observed never lose sight of the original molecule.

Unveiling the identity of distant targets through advanced Raman-laser-induced breakdown spectroscopy data fusion strategies.

Data fusion is the process of combining data gathered from two or more sensors to produce a more specific, comprehensive and unified dataset of the inspected target. On this basis, much has been said about the possible benefits resulting from the use of molecular and atomic information for the detection of explosives. The orthogonal nature of the spectral and compositional information provided by Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) makes them suitable candidates for an optimal combination of their data, thus achieving inferences that are not feasible using a single sensor. The present manuscript evaluates several architectures for the combination of spectral outputs from these two sensors in order to compare the benefits and drawbacks of data fusion for improving the overall identification performance. From the simple assembling (concatenation or addition) of Raman and LIBS spectra to signals' processing on the basis of linear algebra (either the outer product or the outer sum), different identification patterns of several compounds (explosives, potential confusants and supports) have been built. The efficiency on target differentiation by using each of the architectures has been evaluated by comparing the identification yield obtained for all the inspected targets from correlation and similarity measurements. Additionally, a specific code integrated by several of these patterns to identify each compound has also been evaluated. This approach permits to obtain a better knowledge about the identity of an interrogated target, mainly in those decisive cases in which LIBS or Raman cannot be effective separately to reach a decision.

Range-adaptive standoff recognition of explosive fingerprints on solid surfaces using a supervised learning method and laser-induced breakdown spectroscopy.

The distance between the sensor and the target is a particularly critical factor for an issue as crucial as explosive residues recognition when a laser-assisted spectroscopic technique operates in a standoff configuration. Particularly for laser ablation, variations in operational range influence the induced plasmas as well as the sensitivity of their ensuing optical emissions, thereby confining the attributes used in sorting methods. Though efficient classification models based on optical emissions gathered under specific conditions have been developed, their successful performance on any variable information is limited. Hence, to test new information by a designed model, data must be acquired under operational conditions totally matching those used during modeling. Otherwise, the new expected scenario needs to be previously modeled. To facing both this restriction and this time-consuming mission, a novel strategy is proposed in this work. On the basis of machine learning methods, the strategy stems from a decision boundary function designed for a defined set of experimental conditions. Next, particular semisupervised models to the envisaged conditions are obtained adaptively on the basis of changes in laser fluence and light emission with variation of the sensor-to-target distance. Hence, the strategy requires only a little prior information, therefore ruling out the tedious and time-consuming process of modeling all the expected distant scenes. Residues of ordinary materials (olive oil, fuel oil, motor oils, gasoline, car wax and hand cream) hardly cause confusion in alerting the presence of an explosive (DNT, TNT, RDX, or PETN) when tested within a range from 30 to 50 m with varying laser irradiance between 8.2 and 1.3 GW cm(-2). With error rates of around 5%, the experimental assessments confirm that this semisupervised model suitably addresses the recognition of organic residues on aluminum surfaces under different operational conditions.

Advanced recognition of explosives in traces on polymer surfaces using LIBS and supervised learning classifiers.

The large similarity existing in the spectral emissions collected from organic compounds by laser-induced breakdown spectroscopy (LIBS) is a limiting factor for the use of this technology in the real world. Specifically, among the most ambitious challenges of today's LIBS involves the recognition of an organic residue when neglected on the surface of an object of identical nature. Under these circumstances, the development of an efficient algorithm to disclose the minute differences within this highly complex spectral information is crucial for a realistic application of LIBS in countering explosive threats. An approach cemented on scatter plots of characteristic emission features has been developed to identify organic explosives when located on polymeric surfaces (teflon, nylon and polyethylene). By using selected spectral variables, the approach allows to design a concise classifier for alerting when one of four explosives (DNT, TNT, RDX and PETN) is present on the surface of the polymer. Ordinary products (butter, fuel oil, hand cream, olive oil and motor oil) cause no confusion in the decisions taken by the classifier. With rates of false negatives and false positives below 5%, results demonstrate that the classification algorithm enables to label residues according to their harmful nature in the most demanding scenario for a LIBS sensor.

Adaptive approach for variable noise suppression on laser-induced breakdown spectroscopy responses using stationary wavelet transform.

Spectral signals are often corrupted by noise during their acquisition and transmission. Signal processing refers to a variety of operations that can be carried out on measurements in order to enhance the quality of information. In this sense, signal denoising is used to reduce noise distortions while keeping alterations of the important signal features to a minimum. The minimization of noise is a highly critical task since, in many cases, there is no prior knowledge of the signal or of the noise. In the context of denoising, wavelet transformation has become a valuable tool. The present paper proposes a noise reduction technique for suppressing noise in laser-induced breakdown spectroscopy (LIBS) signals using wavelet transform. An extension of the Donoho's scheme, which uses a redundant form of wavelet transformation and an adaptive threshold estimation method, is suggested. Capabilities and results achieved on denoising processes of artificial signals and actual spectroscopic data, both corrupted by noise with changing intensities, are presented. In order to better consolidate the gains so far achieved by the proposed strategy, a comparison with alternative approaches, as well as with traditional techniques, is also made.

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.

Partial least squares X-ray fluorescence determination of trace elements in sediments from the estuary of Nerbioi-Ibaizabal River.

The feasibility of partial least squares (PLS) regression modeling of X-ray fluorescence (XRF) spectra of estuarine sediments has been evaluated as a tool for rapid trace element content monitoring. Multivariate PLS calibration models were developed to predict the concentration of Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn in sediments collected from different locations across the estuary of the Nerbioi-Ibaizabal River (Metropolitan Bilbao, Bay of Biscay, Basque Country). The study was carried out on a set of 116 sediment samples, previously lyophilized and sieved with a particle size lower than 63 microm. Sample reference data were obtained by inductively coupled plasma mass spectrometry. 34 samples were selected for building PLS models through a hierarchical cluster analysis. The remaining 82 samples were used as a test set to validate the models. Results obtained in the present study involved relative root mean square errors of prediction varying from 21%, for the determination of Pb at hundreds microg g(-1) level, up to 87%, for Ni determination at little tens microg g(-1) level. An average prediction error of +/-37% for the 14 elements under study was obtained, being in all cases mean differences between predicted and reference results of the same order than the standard deviation of three replicates from a same sample. Residual predictive deviation values obtained ranged from 1.1 to 3.9.

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.