Effect of Chemical Pretreatments on the Physical Properties of Kiwi

In this work the effect of pre-treatments on the physical properties of fresh kiwi was studied. For that, a set of tests using chemical pretreatments was used, in which the samples were subjected to aqueous solutions of ascorbic acid and potassium metabisulfite at concentrations of 0.25% and 1% (w/v) for periods of 30 and 60 minutes, in order to understand the implications of the treatments in the color and texture of the kiwi as compared to its original properties. The results showed that the kiwi treated with ascorbic acid changed its color very intensively when compared to the fresh product, and this trend was intensified after storage. Contrarily, when potassium metabisulfite was used, the changes in color were quite negligible right after the treatment and even lower after the storage period of 6 days under refrigeration. After the treatments with both solutions, the kiwi texture was drastically changed, diminishing hardness considerably and increasing elasticity for all treatments. The same could be observed after six days of refrigeration.

ß-Lactams: chemical structure, mode of action and mechanisms of resistance

This synopsis summarizes the key chemical and bacteriological characteristics of β-lactams, penicillins, cephalosporins, carbanpenems, monobactams and others. Particular notice is given to first-generation to fifth-generation cephalosporins. This review also summarizes the main resistance mechanism to antibiotics, focusing particular attention to those conferring resistance to broad-spectrum cephalosporins by means of production of emerging cephalosporinases (extended-spectrum β-lactamases and AmpC β-lactamases), target alteration (penicillin-binding proteins from methicillin-resistant Staphylococcus aureus) and membrane transporters that pump β-lactams out of the bacterial cell.

Comparison of Py-GC/FID and Wet Chemistry Analysis for Lignin Determination in Wood and Pulps from Eucalyptus globulus

The kraft pulps produced from heartwood and sapwood of Eucalyptus globulus at 130 degrees C, 150 degrees C, and 170 degrees C were characterized by wet chemistry (total lignin as sum of Klason and soluble lignin fractions) and pyrolysis (total lignin denoted as py-lignin). The total lignin content obtained with both methods was similar. In the course of delignification, the py-lignin values were higher (by 2 to 5%) compared to Klason values, which is in line with the importance of soluble lignin for total lignin determination. Pyrolysis analysis presents advantages over wet chemical procedures, and it can be applied to wood and pulps to determine lignin contents at different stages of the delignification process. The py-lignin values were used for kinetic modelling of delignification, with very high predictive value and results similar to those of modelling using wet chemical determinations.

Chemical and biological studies from an Azorean macroalga: Ulva rigida

3rd Portuguese Meeting on Medicinal Chemistry and 1st Portuguese-Spanish-Brazilian Meeting on Medicinal Chemistry, Aveiro, 28-30 Novembro 2012.

Physical-chemical and mineralogical characterization of fine aggregates from construction and demolition waste recycling plants

Construction and demolition waste (CDW) represents around 31% of all waste produced in the European Union. It is today acknowledged that the consumption of raw materials in the construction industry is a non-sustainable activity. It is thus necessary to reduce this consumption, and the volume of CDW dumped, by using this waste as a source of raw materials for the production of recycled aggregates. One potential use of these aggregates is their incorporation in reinforced concrete as a replacement of natural aggregates. A concrete that incorporates these aggregates and still performs well requires them to be fully characterized so that their behaviour within the concrete can be predicted. Coarse recycled aggregates have been studied quite thoroughly, because they are simpler to reintroduce in the market as a by-product, and so has the performance of concrete made with them. This paper describes the main results of research designed to characterize the physical and chemical properties of fine recycled aggregates for concrete production and their relationship with mineralogical composition and preprocessing. The constraints of the incorporation of fine aggregates in reinforced concrete are discussed. It is shown that, unless a developed processing diagram is used, this application is not feasible. (C) 2013 Elsevier Ltd. All rights reserved.

Size-segregated chemical composition of aerosol emissions in an urban road tunnel in Portugal

An atmospheric aerosol study was performed in 2008 inside an urban road tunnel, in Lisbon, Portugal. Using a high volume impactor, the aerosol was collected into four size fractions (PM0.5, PM0.5-1, PM1-2.5 and PM2.5-10) and analysed for particle mass (PM), organic and elemental carbon (OC and EC), polycyclic aromatic hydrocarbons (PAH), soluble inorganic ions and elemental composition. Three main groups of compounds were discriminated in the tunnel aerosol: carbonaceous, soil component and vehicle mechanical wear. Measurements indicate that Cu can be a good tracer for wear emissions of road traffic. Cu levels correlate strongly with Fe, Mn, Sn and Cr, showing a highly linear constant ratio in all size ranges, suggesting a unique origin through sizes. Ratios of Cu with other elements can be used to source apportion the trace elements present in urban atmospheres, mainly on what concerns coarse aerosol particles. (C) 2013 Elsevier Ltd. All rights reserved.

Structural, physical, and chemical modifications induced by microwave heating on native agar-like galactans

Native agars from Gracilaria vermiculophylla produced in sustainable aquaculture systems (IMTA) were extracted under conventional (TWE) and microwave (MAE) heating. The optimal extracts from both processes were compared in terms of their properties. The agars’ structure was further investigated through Fourier transform infrared and NMR spectroscopy. Both samples showed a regular structure with an identical backbone, β-D-galactose (G) and 3,6-anhydro-α-L-galactose (LA) units; a considerable degree of methylation was found at C6 of the G units and, to a lesser extent, at C2 of the LA residues. The methylation degree in the G units was lower for MAEopt agar; the sulfate content was also reduced. MAE led to higher agar recoveries with drastic extraction time and solvent volume reductions. Two times lower values of [η] and Mv obtained for the MAEopt sample indicate substantial depolymerization of the polysaccharide backbone; this was reflected in its gelling properties; yet it was clearly appropriate for commercial application in soft-texture food products.

Validação da versão portuguesa do método 'Internacional Chemical Control Toolkit'

Em Portugal, como em outros países, podem ser encontrados milhares de trabalhadores com doenças e outros problemas resultantes da exposição aos compostos orgânicos voláteis (COV’s). No entanto são poucos os estudos aplicados à actividade industrial. Neste trabalho validou-se/estimou-se a exposição ocupacional a COV´s dos trabalhadores que executam a tarefa de ‘aplicação de revestimento do mobiliário’, mais conhecida por ‘acabamento de móveis’ na secção de acabamentos do sector de mobiliário de madeira, que se situa nos concelhos de Paços de Ferreira e Paredes, concelhos do Norte de Portugal. A amostra foi constituída por 17 empresas e foram avaliados 34 tarefas que corresponde ao mesmo número de trabalhadores, uma vez que eles executam essa tarefa durante as oito horas diárias de trabalho. Para avaliar a exposição dos trabalhadores foram utilizadas duas abordagens da higiene do trabalho: a pragmática, através da aplicação do método Toolkit e a tradicional com recurso a amostragens de ar e análises laboratoriais. Os resultados obtidos sugerem que o Toolkit é uma boa ferramenta para ser utilizada pelas Pequenas e Médias Empresas (PME’s), que trabalhem com substâncias em pó ou líquidas. É um método expedito e que não acarreta grandes esforços financeiros. Verificou-se ainda que a maioria dos trabalhadores estão em risco de exposição a COV’s. Sendo necessário tomar medidas de controlo.

Ecosapentanoic acid (EPA) does not affect cell kinetics in peripheral lymphocites from patients with Crohn's disease (CD) according to IL6 174G/C polymorphism

Rationale: Omega 3 fatty acids have been shown to be of potential benefit in patients with CD. The aim of the present study was to evaluate whether EPA can modulate the inflammatory response according to different genotypes of IL6G174G/C polymorphism. Methods: Peripheral blood cells were collected from CD patients with different genotypes for IL6 174G/C (GG, n = 16, GC, n = 8, CC, n = 7), and lymphocytes were established in culture media. Replicates with the addition of EPA (25 mM) were analysed in a period of 24h, 48h and 72h. Expression of IL6 e a PGE2 was assessed by ELISA. Apoptosis and cellular proliferation was determined by flow cytometry.

Copper, nickel and zinc removal by peanut hulls: batch and column studies in mono, tri-component systems and with real effluent

The main goal of this research study was the removal of Cu(II), Ni(II) and Zn(II) from aqueous solutions using peanut hulls. This work was mainly focused on the following aspects: chemical characterization of the biosorbent, kinetic studies, study of the pH influence in mono-component systems, equilibrium isotherms and column studies, both in mono and tri-component systems, and with a real industrial effluent from the electroplating industry. The chemical characterization of peanut hulls showed a high cellulose (44.8%) and lignin (36.1%) content, which favours biosorption of metal cations. The kinetic studies performed indicate that most of the sorption occurs in the first 30 min for all systems. In general, a pseudo-second order kinetics was followed, both in mono and tri-component systems. The equilibrium isotherms were better described by Freundlich model in all systems. Peanut hulls showed higher affinity for copper than for nickel and zinc when they are both present. The pH value between 5 and 6 was the most favourable for all systems. The sorbent capacity in column was 0.028 and 0.025 mmol g-1 for copper, respectively in mono and tri-component systems. A decrease of capacity for copper (50%) was observed when dealing with the real effluent. The Yoon-Nelson, Thomas and Yan’s models were fitted to the experimental data, being the latter the best fit.

Adsorption kinetics of removal of yellow lanasol dyestuff using gallinaceous feathers

Gallinaceous feathers are an abundant solid waste from the poultry processing industries, which poses disposal problems. A kinetic study dealing with the adsorption process of wool reactive dye, Yellow Lanasol 4G (CI Reactive Yellow 39), on gallinaceous (Gallus gallus, Cobb 500) feathers was carried out. The main research goals of this work were to evaluate the viability of using this waste as adsorbent and to study the kinetics of the adsorption process, using a synthetic effluent. The characterization of feathers was performed by scanning electron microscopy, mercury porosimetry and B.E.T. method. The study of several factors (stirring, particles size, initial dye concentration and temperature) showed their influence over the adsorption process. An adapted version of the Schumckler and Goldstein´s unreacted core model fitted the experimental data. The best fit was obtained when the rate-limiting step was the diffusion through the reacted layer, which was expected considering the size of the dyestuff molecules. The comparison with the granular activated carbon (GAC) Sutcliffe GAC 10-30 indicate that in spite of the high adsorption capacities shown by feathers the GAC presented higher values, the values obtained were respectively 150 and 219 mg g-1, for an initial concentration of 500 mg L-1. The results obtained might open future perspectives both to the valorization of feathers and to the economical treatment of textile wastewaters.

Wine and grape polyphenols — a chemical perspective

Phenolic compounds constitute a diverse group of secondary metabolites which are present in both grapes and wine. The phenolic content and composition of grape processed products (wine) are greatly influenced by the technological practice to which grapes are exposed. During the handling and maturation of the grapes several chemical changes may occur with the appearance of new compounds and/or disappearance of others, and consequent modification of the characteristic ratios of the total phenolic content as well as of their qualitative and quantitative profile. The non-volatile phenolic qualitative composition of grapes and wines, the biosynthetic relationships between these compounds, and the most relevant chemical changes occurring during processing and storage will be highlighted in this review.

β-Lactams: chemical structure, mode of action and mechanisms of resistance

This synopsis summarizes the key chemical and bacteriological characteristics of β-lactams, penicillins, cephalosporins, carbanpenems, monobactams and others. Particular notice is given to first-generation to fifth-generation cephalosporins. This reviewalso summarizes the main resistancemechanism to antibiotics, focusing particular attention to those conferring resistance to broad-spectrum cephalosporins by means of production of emerging cephalosporinases (extended-spectrum β-lactamases and AmpC β-lactamases), target alteration (penicillin-binding proteins from methicillin-resistant Staphylococcus aureus) and membrane transporters that pump β-lactams out of the bacterial cell.

Experimental design methodologies for the identification of Michaelis-Menten type kinetics

The main objective of this work was to investigate the application of experimental design techniques for the identification of Michaelis-Menten kinetic parameters. More specifically, this study attempts to elucidate the relative advantages/disadvantages of employing complex experimental design techniques in relation to equidistant sampling when applied to different reactor operation modes. All studies were supported by simulation data of a generic enzymatic process that obeys to the Michaelis-Menten kinetic equation. Different aspects were investigated, such as the influence of the reactor operation mode (batch, fed-batch with pulse wise feeding and fed-batch with continuous feeding) and the experimental design optimality criteria on the effectiveness of kinetic parameters identification. The following experimental design optimality criteria were investigated: 1) minimization of the sum of the diagonal of the Fisher information matrix (FIM) inverse (A-criterion), 2) maximization of the determinant of the FIM (D-criterion), 3) maximization of the smallest eigenvalue of the FIM (E-criterion) and 4) minimization of the quotient between the largest and the smallest eigenvalue (modified E-criterion). The comparison and assessment of the different methodologies was made on the basis of the Cramér-Rao lower bounds (CRLB) error in respect to the parameters vmax and Km of the Michaelis-Menten kinetic equation. In what concerns the reactor operation mode, it was concluded that fed-batch (pulses) is better than batch operation for parameter identification. When the former operation mode is adopted, the vmax CRLB error is lowered by 18.6 % while the Km CRLB error is lowered by 26.4 % when compared to the batch operation mode. Regarding the optimality criteria, the best method was the A-criterion, with an average vmax CRLB of 6.34 % and 5.27 %, for batch and fed-batch (pulses), respectively, while presenting a Km’s CRLB of 25.1 % and 18.1 %, for batch and fed-batch (pulses), respectively. As a general conclusion of the present study, it can be stated that experimental design is justified if the starting parameters CRLB errors are inferior to 19.5 % (vmax) and 45% (Km), for batch processes, and inferior to 42 % and to 50% for fed-batch (pulses) process. Otherwise equidistant sampling is a more rational decision. This conclusion clearly supports that, for fed-batch operation, the use of experimental design is likely to largely improve the identification of Michaelis-Menten kinetic parameters.

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.