Mass spectrometry parameters optimization for the 46 multiclass pesticides determination in strawberries with gas chromatography ion-trap tandem mass spectrometry

Multiclass analysis method was optimized in order to analyze pesticides traces by gas chromatography with ion-trap and tandem mass spectrometry (GC-MS/MS). The influence of some analytical parameters on pesticide signal response was explored. Five ion trap mass spectrometry (IT-MS) operating parameters, including isolation time (IT), excitation voltage (EV), excitation time (ET),maximum excitation energy or “q” value (q), and isolationmass window (IMW) were numerically tested in order to maximize the instrument analytical signal response. For this, multiple linear regression was used in data analysis to evaluate the influence of the five parameters on the analytical response in the ion trap mass spectrometer and to predict its response. The assessment of the five parameters based on the regression equations substantially increased the sensitivity of IT-MS/MS in the MS/MS mode. The results obtained show that for most of the pesticides, these parameters have a strong influence on both signal response and detection limit.Using the optimized method, a multiclass pesticide analysis was performed for 46 pesticides in a strawberry matrix. Levels higher than the limit established for strawberries by the European Union were found in some samples.

The effect of the angle of incidence on the aqueous corrosion of ion implanted M50 steel substrates

Following work on tantalum and chromium implanted flat M50 steel substrates, this work reports on the electrochemical behaviour of M50 steel implanted with tantalum and chromium and the effect of the angle of incidence. Proposed optimum doses for resistance to chloride attack were based on the interpretation of results obtained during long-term and accelerated electrochemical testing. After dose optimization from the corrosion viewpoint, substrates were implanted at different angles of incidence (15°, 30°, 45°, 60°, 75°, 90°) and their susceptibility to localized corrosion assessed using open-circuit measurements, step by step polarization and cyclic voltammetry at several scan rates (5–50 mV s-1). Results showed, for tantalum implanted samples, an ennoblement of the pitting potential of approximately 0.5 V for an angle of incidence of 90°. A retained dose of 5 × 1016 atoms cm-2 was found by depth profiling with Rutherford backscattering spectrometry. The retained dose decreases rapidly with angle of incidence. The breakdown potential varies roughly linearly with the angle of incidence up to 30° falling fast to reach -0.1 V (vs. a saturated calomel electrode (SCE)) for 15°. Chromium was found to behave differently. Maximum corrosion resistance was found for angles of 45°–60° according to current densities and breakdown potentials. Cr+ depth profiles ((p,γ) resonance broadening method), showed that retained doses up to an angle of 60° did not change much from the implanted dose at 90°, 2 × 1017 Cr atoms cm-2. The retained implantation dose for tantalum and chromium was found to follow a (cos θ)8/3 dependence where θ is the angle between the sample normal and the beam direction.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The gambin model provides a superior fit to species abundance distributions with a single free parameter : evidence, implementation and interpretation

The species abundance distribution (SAD) has been a central focus of community ecology for over fifty years, and is currently the subject of widespread renewed interest. The gambin model has recently been proposed as a model that provides a superior fit to commonly preferred SAD models. It has also been argued that the model's single parameter (α) presents a potentially informative ecological diversity metric, because it summarises the shape of the SAD in a single number. Despite this potential, few empirical tests of the model have been undertaken, perhaps because the necessary methods and software for fitting the model have not existed. Here, we derive a maximum likelihood method to fit the model, and use it to undertake a comprehensive comparative analysis of the fit of the gambin model. The functions and computational code to fit the model are incorporated in a newly developed free-to-download R package (gambin). We test the gambin model using a variety of datasets and compare the fit of the gambin model to fits obtained using the Poisson lognormal, logseries and zero-sum multinomial distributions. We found that gambin almost universally provided a better fit to the data and that the fit was consistent for a variety of sample grain sizes. We demonstrate how α can be used to differentiate intelligibly between community structures of Azorean arthropods sampled in different land use types. We conclude that gambin presents a flexible model capable of fitting a wide variety of observed SAD data, while providing a useful index of SAD form in its single fitted parameter. As such, gambin has wide potential applicability in the study of SADs, and ecology more generally.

Desenvolvimento de uma plataforma SIG para apoio ao estudo da sismicidade em Portugal

Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo de cooperação entre o ISEL e o LNEC

Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Development and validation of a novel method for the analysis of chlorinated pesticides in soils using microwave-assisted extraction–headspace solid phase microextraction and gas chromatography–tandem mass spectrometry

A new procedure for determining eleven organochlorine pesticides in soils using microwave-assisted extraction (MAE) and headspace solid phase microextraction (HS-SPME) is described. The studied pesticides consisted of mirex, α- and γ-chlordane, p,p’-DDT, heptachlor, heptachlor epoxide isomer A, γ-hexachlorocyclohexane, dieldrin, endrin, aldrine and hexachlorobenzene. The HS-SPME was optimized for the most important parameters such as extraction time, sample volume and temperature. The present analytical procedure requires a reduced volume of organic solvents and avoids the need for extract clean-up steps. For optimized conditions the limits of detection for the method ranged from 0.02 to 3.6 ng/g, intermediate precision ranged from 14 to 36% (as CV%), and the recovery from 8 up to 51%. The proposed methodology can be used in the rapid screening of soil for the presence of the selected pesticides, and was applied to landfill soil samples.

Multiresidue method for the determination of organophosphorus pesticides in still wine and fortified wine using solid-phase microextraction and gas chromatography – Tandem mass spectrometry

A SPME-GC-MS/MS method for the determination of eight organophosphorus pesticides (azinphos-methyl, chlorpyriphos, chlorpyriphos-methyl, diazinon, fenitrothion, fenthion, malathion, and methidathion) in still and fortified wine was developed. The extraction procedure is simple, solvent free, and without any sample pretreatment. Limits of detection (LOD) and quantitation (LOQ) values in the range 0.1–14.3 lg/L and 0.2–43.3 lg/L, respectively, were obtained. The LOQ values are below the maximum residue levels (MRLs) established by European Regulation for grapes, with the exception of methidathion. Coefficients of correlation (R2) higher than 0.99 were obtained for the majority of the pesticides, in all different wines analyzed.

Determination of 24 pesticide residues in fortified wines by solid-phase microextraction and gas chromatography tandem mass spectrometry

The present work describes a solid-phase microextraction (SPME) gas chromatography_tandem mass spectrometry (MS/MS) method to quantify 24 pesticides in fortified white wine and fortified red wine. In this study “fortified wine” refers to a wine in which fermentation is arrested before completion by alcohol distillate addition, allowing sugar and alcoholic contents to be higher (around 80-100 g/L total sugars and 19-22% alcohol strength (v/v)). The analytical method showed good linearity, presenting correlation coefficients (R2) ≥ 0.989 for all compounds. Limits of detection (LOD) and quantitation (LOQ) in the ranges of 0.05-72.35 and 0.16-219.23 μg/L, respectively, were obtained. LOQs are below the maximum residue levels (MRL) set by European Regulation for grapes. The proposed method was applied to 17 commercial fortified wines. The analyzed pesticides were not detected in the wines tested.

Analysis of PCBs in soils and sediments by microwave-assisted extraction, headspace-SPME and high resolution gas chromatography with ion-trap tandem mass spectrometry

A procedure for the determination of seven indicator PCBs in soils and sediments using microwave-assisted extraction (MAE) and headspace solid-phase microextraction (HS-SPME) prior to GC-MS/MS is described. Optimization of the HS-SPME was carried out for the most important parameters such as extraction time, sample volume and temperature. The adopted methodology has reduced consumption of organic solvents and analysis runtime. Under the optimized conditions, the method detection limit ranged from 0.6 to 1 ng/g when 5 g of sample was extracted, the precision on real samples ranged from 4 to 21% and the recovery from 69 to 104%. The proposed method, which included the analysis of a certified reference material in its validation procedure, can be extended to several other PCBs and used in the monitoring of soil or sediments for the presence of PCBs.

Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Mousetrapping: the ordeals of interpretation

Tese de doutoramento, Estudos Literários (Teoria da Literatura), Universidade de Lisboa, Faculdade de Letras, 2012

Characterization of the volatile fraction emitted by phloems of four pinus species by solid-phase microextraction and gas chromatography–mass spectrometry

Pine forests constitute some of the most important renewable resources supplying timber, paper and chemical industries, among other functions. Characterization of the volatiles emitted by different Pinus species has proven to be an important tool to decode the process of host tree selection by herbivore insects, some of which cause serious economic damage to pines. Variations in the relative composition of the bouquet of semiochemicals are responsible for the outcome of different biological processes, such as mate finding, egg-laying site recognition and host selection. The volatiles present in phloem samples of four pine species, P. halepensis, P. sylvestris, P. pinaster and P. pinea, were identified and characterized with the aim of finding possible host-plant attractants for native pests, such as the bark beetle Tomicus piniperda. The volatile compounds emitted by phloem samples of pines were extracted by headspace solid-phase micro extraction, using a 2 cm 50/30 mm divinylbenzene/carboxen/polydimethylsiloxane table flex solid-phase microextraction fiber and its contents analyzed by high-resolution gas chromatography, using flame ionization and a non polar and chiral column phases. The components of the volatile fraction emitted by the phloem samples were identified by mass spectrometry using time-of-flight and quadrupole mass analyzers. The estimated relative composition was used to perform a discriminant analysis among pine species, by means of cluster and principal component analysis. It can be concluded that it is possible to discriminate pine species based on the monoterpenes emissions of phloem samples.

Fractional derivatives: probability interpretation and frequency response of rational approximations

The theory of fractional calculus (FC) is a useful mathematical tool in many applied sciences. Nevertheless, only in the last decades researchers were motivated for the adoption of the FC concepts. There are several reasons for this state of affairs, namely the co-existence of different definitions and interpretations, and the necessity of approximation methods for the real time calculation of fractional derivatives (FDs). In a first part, this paper introduces a probabilistic interpretation of the fractional derivative based on the Grünwald-Letnikov definition. In a second part, the calculation of fractional derivatives through Padé fraction approximations is analyzed. It is observed that the probabilistic interpretation and the frequency response of fraction approximations of FDs reveal a clear correlation between both concepts.