A fast (16min) procedure to assess the bioaccessible metallic fraction of Cd, Cr, Cu, Ni, Pb and Zn simultaneously extracted (SEM) from marine sediments plus an indirect approach to determine acid volatile sulfides (AVS) are presented. For the extraction process magnetic agitation was compared with ultrasonic stirring (using a bath and a probe), and several stirring times were assayed. The proposed SEM procedure uses an ultrasonic probe and 1mL of HCl. It dramatically minimizes the turnaround time and the residues. AVS were evaluated as the difference between the amounts of sulphur in the solid residue after the extraction and total sulphur in the original sample. These procedures are fast, easy to implement and cost-effective to assess the potential risk posed by metals in marine sediments. They were tested using several CRMs and applied to sediments from two Galician Rias (NW Spain); their SEM-AVS differences indicated no biological risk.

A fast, simple and straightforward procedure to decide on the best model to calculate the mass discrimination factor in Isotope Dilution Inductively Coupled Plasma Mass Spectrometry (ID-ICP-MS) is proposed. It is based on the study of the residuals of the different models that are proposed commonly, viz., the linear, the exponential, the power and Russell's models. However, it can be generalized to evaluate any model proposed to linearize the relationship between the theoretical/measured isotope ratios and the mass. The procedure does not involve laboratory extra work, it is rooted on basic statistics associated with the least squares fit, and can be applied easily by the analysts so that decision making is fast and reliable. The procedure was exemplified with four different examples where Cd, Cr, Nd and Sm were determined by ID-ICP-MS.

Datasets with missing data ratios ranging from 24% to 4%, corresponding to three air quality monitoring studies, were used to ascertain whether major differences occur when five currently used imputation methods are applied (four single imputation methods and a multiple imputation one). Unrotated and Varimax-rotated factor analyses performed on the imputed datasets were compared. All methods performed similarly, although multiple imputation yielded more disperse imputed values. Main differences occurred when a variable with missing values correlated poorly to the other features and when a variable had relevant loadings in several unrotated factors, which sometimes changed the order of the rotated factors.

Two simple, fast and green procedures based on sample emulsification were developed to determine V, Ni, Cu, Cr, Pb, Mo and Ag by ETAAS in used lubricating oils; they will be termed “emulsion” and “three-component solution”. Reagent consumption and residues got reduced dramatically as solutions were prepared directly in 1mL autosampler cups. The emulsions were stable enough to perform the overall analysis without intermediate agitation between replicates. Quantitation was performed using aqueous standards and the standard additions method, except for Cu and Cr when the three-component solutions were employed. The preparation time for the two methods was 15min/emulsion and 30min/5 three-component solutions. Method validation was carried out with three reference materials: SRM 1084a “Wear-Metals in Lubricating Oil”, LO-010698 “Lubricating Oil” and “Used Oil” HU-1. The three-component solutions procedure was selected and applied to analyse several used lubricating oils.

Identifying petroleum-related products released into the environment is a complex and difficult task. To achieve this, polycyclic aromatic hydrocarbons (PAHs) are of outstanding importance nowadays. Despite traditional quantitative fingerprinting uses straightforward univariate statistical analyses to differentiate among oils and to assess their sources, a multivariate strategy based on Procrustes rotation (PR) was applied in this paper. The aim of PR is to select a reduced subset of PAHs still capable of performing a satisfactory identification of petroleum-related hydrocarbons. PR selected two subsets of three (C(2)-naphthalene, C(2)-dibenzothiophene and C(2)-phenanthrene) and five (C(1)-decahidronaphthalene, naphthalene, C(2)-phenanthrene, C(3)-phenanthrene and C(2)-fluoranthene) PAHs for each of the two datasets studied here. The classification abilities of each subset of PAHs were tested using principal components analysis, hierarchical cluster analysis and Kohonen neural networks and it was demonstrated that they unraveled the same patterns as the overall set of PAHs.

Kohonen neural networks, or Self-Organizing Maps (SOMs), were used to study the data sets generated in a survey of soil pollution along a four-season study. Each sampling season comprised 89 road soil samples and 12 analytical variables; namely, nine heavy metals (Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, and Zn) and three physicochemical parameters (loss on ignition, pH and humidity). The SOMs provided a rapid and intuitive means to recogniz3e four different groups of samples: roadside of a highway, highway transects, roadside of a main avenue and urban gardens. They became defined essentially by the physical characteristics of the sampling sites and by the intensity of the road traffic. In order to simplify the chemical understanding of the patterns defining the different groups of samples, to avoid noisy and/or redundant variables and to reduce the time required to develop a suitable SOM, the usefulness of a previous variable selection step using CART, Classification and Regression Trees, was investigated.

A case study is presented in which advanced chemical fingerprinting and data interpretation techniques are used to characterize and compare the weathering processes of six oil spillages made under controlled conditions (including the heavy oil released after the Prestige tanker sunk off the Galician coast-NW Spain on 2002). A tiered analytical approach using gas chromatography-flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS) was applied along with two different approaches for 3-way analyses; namely, generalized Procrustes rotation, and matrix-augmented principal components analysis. Results showed that the two 3-way chemometric techniques leaded to essentially the same conclusions when analyzing three sets of compounds fingerprinting the spilled hydrocarbons (aliphatic hydrocarbons, polycyclic aromatic hydrocarbons (PAHs) and a set of diagnostic ratios). A steady evolution on the weathering of the oils was observed with both techniques, but for the diagnostic ratios. The variables involved on the weathering were the lightest aliphatic hydrocarbons and a general combination of the PAHs, which differentiated mostly among the light and the heavy products (fuel oils).

A set of 34 crude oils was analysed by GC-MS (SIM mode) and a suite of 28 diagnostic ratios (DR) calculated. They involved 18 ratios between biomarker molecules (hopanes, steranes, diasteranes and triaromatic steroids) and 10 quotients between polycyclic aromatic hydrocarbons. Three unsupervised pattern recognition techniques (i.e., principal components analysis, heatmap hierarchical cluster analysis and Kohonen neural networks) were employed to evaluate the final dataset and, thus, ascertain whether the crude oils grouped as a function of their geographical origin. In addition, an objective variable selection procedure based on Procrustes Rotation was undertaken to select a reduced set of DR that comprised for most of the information in the original data without loosing relevant information. A reduced set of four DR (namely; TA21, D2/P2, D3/P3 and B(a)F/4-Mpy) demonstrated to be sufficient to characterize the crude oils and the groups they formed.

This paper compares the weathering patterns of two similar fuel oils: a fuel oil spilled after a ship accident (Prestige-Nassau, off the Galician coast -NW Spain-) and a fuel designed to cope with the numerous quests for samples to carry out scientific studies (IFO). Comparative studies were made to evaluate the capability of common fingerprinting analytical techniques to differentiate the fuels, as well as their capabilities to monitor their weathering. The two products were spilled under controlled conditions during ca. four months to assess how they evolved on time. Mid-IR spectrometry and gas chromatography (flame ionization and mass spectrometry detectors) were used. IR indexes related to total aromaticity, type of substituents (branched or linear chains) and degree of aromatic substitution reflected well the differences between the fuels during weathering. Regarding the chromatographic measurements, the n-alkanes became highly reduced for both fuel oils and it was found that the PAHs of the synthetic fuel (IFO) were more resistant to weathering. Regarding biomarkers, the different profiles of the steranes, diasteranes and triaromatic steroids allowed for a simple differentiation amongst the two products. The %D2/P2 ratio differentiated both products whereas the %N3/P2 one ordered the samples according to the extent of their weathering.

The capability of single-reflection near-IR (NIR) spectroscopy to differentiate, characterize and monitor the fate of a set of hydrocarbons spilled in the marine environment was compared with that of multiple-reflection horizontal attenuated total reflection mid-IR (ATR-MIR) spectroscopy. Multivariate pattern recognition techniques [principal component analysis (PCA), multivariate polynomial regression, cluster analysis and potential curves] were applied to unravel the major trends of the weathering processes of four generic types of crude oils and two heavy fuel oils spilled under controlled conditions for almost 4 months. A chemical interpretation of the NIR spectra related the weathering processes and the PCA loadings, which had not already been done in the literature. Weathering for both light and heavy products was characterized by a contrast among the linear aliphatic structures (more volatile and easy to degrade) and the branched and aromatic structures (more recalcitrant). Potential curves were applied to model each product and determine objectively whether unknown samples could be classified correctly. Polynomial regression on the PCA scores was employed to evaluate the time elapsed from the oil spillage to its sampling; this represents a new approach to assess the age of a hydrocarbon lump. In general, NIR spectroscopy yielded good results when light crude oils were studied, whereas ATR-MIR spectroscopy led to satisfactory results for both light and heavy products.

Three different approaches for 3-way analyses, namely, Procrustes rotation, parallel factor analysis (PARAFAC) and matrix-augmented principal component analysis (MA-PCA), have been used to compare six different oil spillages made under controlled conditions (one of them corresponding to the heavy oil released after the sunk of the Prestige tanker off the Galician coast–NW Spain on November 2002). Each spillage was monitored during three and a half months by attenuated total reflectance (ATR) mid-IR spectroscopy. Ten characteristic band ratios were defined. Results showed that the three 3-way chemometric techniques lead to essentially the same conclusions, where from it was concluded that the most relevant pattern defining the oil weathering was related to ‘total aromaticity’, i.e., the total number of CC bonds in the molecules which form the products. In addition, weathering of the samples got clearly characterized by a steady evolution on the scores (sample weights), with a clear increase after 11–14 days. Differentiation of the products (slices of the data cube) was also possible due to their intrinsic characteristics as, in general, heavy products oppose to the lightest ones.

Coupling multivariate regression methods to atomic spectrometry is an emerging field from which important advantages can be obtained. These include lower workloads, increased laboratory turnarounds, economy, higher efficiency in method development, and relatively simple ways to take account of complex interferences. In this paper four typical regression methods ( ordinary multiple linear regression, principal components regression, partial least squares and artificial neural networks) are presented in a practice-oriented way. The main emphasis is placed on explaining their advantages, drawbacks, how to solve the latter and how atomic spectrometry can benefit from multivariate regression. Finally, a retrospective review considering the last sixteen years is made to present practical applications on: flame-, hydride generation-, electrothermal-atomic absorption spectrometry; inductively coupled plasma spectrometry and laser-induced breakdown spectrometry.

A set of 34 worldwide crude oils, 12 distilled products (kerosene, gas oils, and fuel oils) and 45 oil samples taken from several Galician beaches (NW Spain) after the wreckage of the Prestige tanker off the Galician coast was studied. Gas chromatography with flame ionization detection was combined with chemometric multivariate pattern recognition methods (principal components analysis, cluster analysis and Kohonen neural networks) to differentiate and characterize the Prestige fuel oil. All multivariate studies differentiated between several groups of crude oils, fuel oils, distilled products, and samples belonging to the Prestige’s wreck and samples from other illegal discharges. In addition, a reduced set of 13 n-alkanes out of 36, were statistically selected by Procrustes Rotation to cope with the main patterns in the datasets. These variables retained the most important characteristics of the data set and lead to a fast and cheap analytical screening methodology.

The Environmental Laboratories Automation Software System or PALMA (Spanish abbreviation) was developed by a multidisciplinary team in order to support the main tasks of heterogeneous air quality control networks. The software process for PALMA development, which can be perfectly applied to similar multidisciplinary projects, was (a) well-defined, (b) arranged between environmental technicians and informatics, (c) based on quality guides, and (d) clearly user-centred. Moreover, it introduces some interesting advantages with regard to the classical step-by-step approaches. PALMA is a web-based system that allows 'off-line' and automated telematic data acquisition from distributed inmission stations belonging not only to homogeneous but also to heterogeneous air quality control networks. It provides graphic and tabular representations for a comprehensive and centralised analysis of acquired data, and considers the daily work that is associated with such networks: validation of the acquired data, alerts with regard to (periodical) tasks (e.g., analysers verification), downloading of files with environmental information (e.g., dust forecasts), etc. The implantation of PALMA has provided qualitative and quantitative improvements in the work performed by the people in charge of the considered control network.

Three different approaches for 3-way analyses, namely, Procrustes rotation, parallel factor analysis (PARAFAC) and matrix-augmented principal component analysis, have been compared considering a four-seasons study on soil pollution. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (heavy metals, loss on ignition, pH and humidity). Results show that the three chemometric techniques lead to essentially the same conclusions. Hence, Procrustes rotation, a mathematical technique scarcely applied in analytical chemistry, revealed as a useful tool for 3-way data analysis with potential advantages, including its conceptual simplicity and straightforward interpretation of the results. A novel application of the consensus vectors allowed definition of "consensus scores" so that visualization of the samples and temporal patterns can be made. Results also suggested that the trilinearity assumption imbedded in PARAFAC is essentially fulfilled when studying the temporal evolution of an environmental system where no new pollution sources appear during the course of the study.

A fast analytical tool based on attenuated total reflectance mid-IR spectrometry is presented to evaluate the origin of spilled hydrocarbons and to monitor their fate on the environment. Ten spectral band ratios are employed in univariate and multivariate studies (principal components analysis, cluster analysis, density functions – potential curves – and Kohonen self organizing maps). Two indexes monitor typical photooxidation processes, five are related to aromatic characteristics and three study aliphatic and branched chains. The case study considered here comprises 45 samples taken on beaches (from 2002 to 2005) after the Prestige carrier accident off the Galician coast and 104 samples corresponding to weathering studies deployed for the Prestige's fuel, four typical crude oils and a fuel oil. The univariate studies yield insightful views on the gross chemical evolution whereas the multivariate studies allow for simple and straightforward elucidations on whether the unknown samples match the Prestige's fuel. Besides, a good differentiation on the weathering patterns of light and heavy products is obtained.

The recent release of ca. 70,000 tonnes of a heavy fuel oil from the Prestige-Nassau carrier along the Spanish northern coast, mainly along Galicia, was monitored using attenuated total reflectance-mid IR spectrometry. The fuel was characterized and differentiated from 10 products commonly transported along the Galician coast (and their series of weathered samples) using factor analysis. The Prestige's fuel was weathered under natural conditions and under infrared radiation to study its evolution on time. A correlation was established using the 1690-1700 cm(-1) carbonyl peak, where from it was deduced that IR radiation weathered the product two times faster than natural conditions. The use of 10 weathering indexes was carried out to confirm the main patterns given by factor analysis and to seek out which main functional groups and structures increased or decreased during weathering. It was found that the carbonyl and sulphoxide indexes varied greatly, as well as the total aromaticity and long chains ones. The substitution-related indexes pointed out that highly substituted aromatic structures increased although the total amount of isolated CH groups in aromatic structures reached a plateau.

Analysis of solid samples by slurry-sampling-electrothermal atomic absorption spectrometry (SS-ETAAS) can imply spectral and chemical interferences caused by the large amount of concomitants introduced into the graphite furnace. Sometimes they cannot be solved using stabilized temperature platform furnace (STPF) conditions or typical approaches (previous sample ashing, use of chemical modifiers, etc.), which are time consuming and quite expensive. A new approach to handle interferences using multivariate calibrations (partial least squares, PLS, and artificial neural networks, ANN) is presented and exemplified with a real problem consisting on determining Sb in several solid matrices (soils, sediments and coal fly ash) as slurries by ETAAS. Experimental designs were implemented at different levels of Sb to develop the calibration matrix and assess which concomitants (seven ions were considered) modified the atomic signal mostly. They were Na+ and Ca2+ and they induced simultaneous displacement, depletion (enhancement) and broadening of the atomic peak. Here it is shown that these complex effects can be handled in a reliable, fast and cost-effective way to predict the concentration of Sb in slurry samples of several solid matrices. The method was validated predicting the concentrations of five certified reference materials (CRMs) and studying its robustness to current ETAAS problems. It is also shown that linear PLS can handle eventual non-linearities and that its results are comparable to more complex (non-linear) models, as those from ANNs.

Several slurry sampling procedures were evaluated for the electrothermal atomic absorption spectrometry (ETAAS) determination of medium (> 20-50 pg/g) to high (similar to 200 pg/g) Sb concentrations in two widely different solid environmental samples (atmospheric particulate matter and marine sediments). These procedures included (a) direct ultrasound slurry sampling (USS) (using large slurry volumes, At flow during atomization, and non-resonance lines), (b) direct vortex-mixing slurry sampling, and (c) ultrasound-assisted slurry extraction (USE). It was found that direct USS analysis using 2 mL of 0.5% (v/v) HNO3 for the liquid phase was appropriate to determine medium Sb concentrations (> 20-50 pg/g) with good recoveries (around 110%) and precision (RSD < 8%). The same protocol but employing the 231.2-nm alternative non-resonance line was required to accurately (recoveries in the 85-106% range) and precisely (RSD < 4.7%) quantitate high Sb concentrations (similar to 200 mu g/g). The USE procedure employing 20% (v/v) HF slurry acid extracts (1 mL) after ultrasound agitation was found to be a reliable although highly inconvenient method because of the potential risks associated with HF during its handling and damage to the Ti probes.

The importance of fruit beverages, and of apple juice in particular, in daily food habits makes juice authentication an important issue in order to avoid fraudulent practices and to protect human health. Among the instrumental techniques available in analytical laboratories, infrared spectrometry (IR) is a fast and convenient technique to perform screening studies in order to assess the quantity of pure juice in commercial beverages. The information gathered from the IR analyses has some "fuzzy" characteristics (random noise, unclear chemical assignment, etc.) and, therefore, advanced computation techniques (Artificial Neural Networks or ANNs) are needed to develop ad hoc classification models. Disappointingly, the large number of variables derived from IR spectrometry makes ANNs require too much training time. As a result, this work studies two different approaches to apply genetic algorithms as a suitable method to select a small subset of variables intended to optimize the development of the ANN models. Their performance will be compared among them and with several linear methods as well.