M.P. Gómez-Carracedo

A tutorial on multivariate calibration in atomic spectrometry techniques

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.

Monitoring photooxidation of the Prestige's oil spill by attenuated total reflectance infrared spectroscopy.

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 Prestiges 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.

Multivariate prediction of eight kerosene properties employing vapour-phase mid-infrared spectrometry

Eight physico-chemical properties of kerosene (aviation jet fuel) are predicted employing vapour-phase generation, Fourier transform mid-infrared (FT-MIR) spectra and partial least squares regression (PLS). Two devices were implemented and studied in order to generate the kerosene vapour from 100 liquid samples from a Spanish refinery. One of them is very simple whilst the other one requires thermostatic and gas flow controls. The FT-MIR spectra are recorded and used to deploy PLS models for each property (distillation curve, flash point, freezing point, percentage of aromatics and viscosity) and each device. In general, the simplest device yields the more satisfactory models. Several criteria are used to evaluate their performance: the average prediction error (corrected to take into account the error in the reference values), the F-test to assess the absence of bias in the predictions, repeatability and reproducibility. In general, all the models provide unbiased predictions, with low average errors and good precision.

Development of a fast analytical tool to identify oil spillages employing infrared spectral indexes and pattern recognition techniques.

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 Prestiges 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 Prestiges fuel. Besides, a good differentiation on the weathering patterns of light and heavy products is obtained.

Screening the origin and weathering of oil slicks by attenuated total reflectance mid-IR spectrometry

The combination of attenuated total reflectance-fourier transform mid-infrared spectrometry (ATR-FTMIR) and multivariate pattern recognition is presented as a fast and convenient methodology to ascertain the source product an oil slick comes from and to evaluate the extent of its weathering. Different types of hydrocarbons (including crude oils, several heavy distillates and the Prestiges heavy fuel oil) were spilled on metallic containers designed ad hoc and their fate monitored by ATR-FTMIR. Not only environmental conditions were considered for weathering but artificial IR- and UV-irradiation. Pattern-recognition studies revealed that the different hydrocarbons clustered at different locations on the score plots and that the samples corresponding to each oil became ordered according to the extent of their weathering. Among them, fuel oil samples coming from the recent disaster of the Prestige tanker off the Galician shoreline showed a distinctive behaviour. Comparison of natural-, IR- and UV-weathering of a crude oil showed that IR solar radiation can be important in oil-weathering, in addition to broadly-reported UV degradation.

Evaluation of the Pure Apple Juice Content in Commercial Apple Beverages Using FTMIR‐ATR and Potential Curves

Abstract An attenuated total reflectance Fourier‐transform mid‐infrared procedure (FTMIR‐ATR, 1250 to 900 cm−1) has been developed to evaluate the amount of pure apple juice employed to prepare commercial apple juice‐based beverages. Samples were classified according to their percentage of pure apple juice using a multivariate technique called potential curves. The two classification patterns defined by the loadings are associated with the general sugar content and the sucrose vs. fructose‐plus‐glucose “ratio.” In order to apply the methodology to some commercial beverages synthetic blanks were needed to correct for sugar added to the original juice. The procedure was validated using either laboratory prepared juices (2%, 4%, 6%, 8%, 10%, 16%, 20%, 25%, 50%, 70%, and 100% v/v apple juices), commercial 100% pure juices or commercial soft drinks. The contents of the three main sugars ranged from 0.03–4.65 g 100 mL−1 for sucrose, 0.05–6.25 g 100 mL−1 for glucose, and 0.13–6.5 g 100 mL−1 for fructose. The methodology is fast, precise, and offers useful qualitative information.

SELECTION OF VARIABLES BY GENETIC ALGORITHMS TO CLASSIFY APPLE BEVERAGES BY ARTIFICIAL NEURAL NETWORKS

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.

A 10-year survey of trace metals in sediments using self-organizing maps

Self‐organizing maps (SOMs) (in particular, Matrix reOrganization Layout to Map Analytical Patterns (MOLMAP)) were used to unravel the main patterns in a three‐way dataset after a preliminary unfolding of the cube. Eleven sites of the ría of Vigo (NW of Spain) were monitored during the last decade (from 2000 to 2010) to assess pollution trends in this area. Twelve trace metals (Hg, Pb, Cd, Cu, Zn, Cr, As, Li, Fe, Al, Ni and Mn), the total organic carbon and the percentage of fine particles were measured. Results from MOLMAP, the SOM‐based approach, were compared to those of three established alternatives: parallel factor analysis, matrix‐augmented principal component analysis and generalized Procrustes rotation, the latter two employing unfolding as well. MOLMAP showed the best capabilities to differentiate groups of samples. The spatial and temporal trends, as well as the analytical variables causing them, were almost the same for all methods, which confirms MOLMAP as a simple and reliable methodology to treat three‐way environmental datasets. Copyright

Objective chemical fingerprinting of oil spills by partial least-squares discriminant analysis

An objective method based on partial least-squares discriminant analysis (PLS-DA) was used to assign an oil lump collected on the coastline to a suspected source. The approach is an add-on to current US and European oil fingerprinting standard procedures that are based on lengthy and rather subjective visual comparison of chromatograms. The procedure required an initial variable selection step using the selectivity ratio index (SRI) followed by a PLS-DA model. From the model, a “matching decision diagram” was established that yielded the four possible decisions that may arise from standard procedures (i.e., match, non-match, probable match, and inconclusive). The decision diagram included two limits, one derived from the Q-residuals of the samples of the target class and the other derived from the predicted y of the PLS model. The method was used classify 45 oil lumps collected on the Galician coast after the Prestige wreckage. The results compared satisfactorily with those from the standard methods.

Comparing roadsoils pollution patterns extracted by MOLMAP and classical three-way decomposition methods.

A recent approach based on self-organizing maps (SOMs) to extract patterns from three-way data, named MOLMAP, was applied in a four-seasons study on soil pollution and its results compared with three different conventional approaches: Parallel factor analysis (PARAFAC), matrix augmented principal components analysis (MA-PCA) and Procrustes rotation. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Zn, loss on ignition, pH and humidity). It was found that all techniques yielded highly similar results as the samples became organized in two major groups, each with a differentiated pollution pattern. This confirmed MOLMAP as a reliable option to handle environmental three-way datasets and to extract accurate pollution patterns.