Cocos nucifera L. is a palm tree of paramount importance in the food and chemistry industries, although over 50% of its biomass is discarded as waste. The aim of the study is to investigate different pretreatments in to coconut husks (CH), based on acid, alkaline, ionic liquid (IL), and peroxidative, in order to produce fermentable sugars. Severity factors were calculated for pretreatments; values ranged from 0.3 to 1.7 for peroxide, from 0.01 to 1.4 for alkaline, from 1.4 to 2.8 for acid and from 2.0 to 3.0 for ionic liquid. Pretreatments were optimized (time and temperature) to maximize the sugar yield and to remove the total lignin after acid hydrolysis. Reducing sugar yield (70%) was higher when CH waste was alkaline-pretreated for 2 h at 76.21 °C. The highest lignin removal rate was recorded when alkaline (21.4%) and peroxide solutions (27.2%) were used. The IL did not increase sugar yield and was not effective in lignin removal. These outcomes were confirmed through infrared spectroscopy, whereas scanning electron microscopy showed increased biomass porosity during alkaline, acid and peroxide pretreatments. The IL showed little and non-significant changes. The crystallinity index notably increased after each pretreatment; besides, it was directly associated with sugar content.

In the present investigation, a collection of twenty two nerol derivatives, containing 1,2,3-triazolic appendages, was synthesized and screened in vitro for their cytotoxic activity against HL60, Nalm6, and Jurkat human leukemia cells as well as against B16F10 (melanoma cell line). In most cases, derivatives were able to reduce cell viability. The most potent compound (Z)-4-(((3,7-dimethylocta-2,6-dien-1-yl)oxy)methyl)-1-(4-(trifluoromethoxy)benzyl)-1H-1,2,3 triazole showed antiproliferative activity against Jurkat cells and reduced B16F10 cell migration. Physicochemical properties of the compounds were calculated in order to evaluate their potential for drug development. Most of the evaluated physicochemical parameters seemed to be favorable for drug development. In addition, for a better understanding of the biological activity results, 3D quantitative structure-activity relationship (QSAR) studies were carried out. 3D-QSAR studies indicate that the anticancer activities observed for the cell lines HL60 and Jurkat may occur by a similar mechanism of action and the same was found for the Nalm6 and B16F10 cell lines.

Synthetic herbicides are widely used for weed control in crops. Continuous application of chemicals induces the adaptation of weeds, leading to the development of resistance. Therefore, research for novel synthetic herbicides plays an important role in crop protection and food production. Within this context, it is important to understand the relationship between the structures of a set of compounds endowed with herbicide activity and their biological response. In this work, a quantitative structure-activity relationship (QSAR) study of 34 nostoclide analogues was carried out in order to analyze their ability to inhibit the photosystem-II. Compounds were optimized using the DFT/B3LYP/Def2-TZVPP method. 2D and 3D (Lennard-Jones and electrostatic potential energies) molecular descriptors were calculated at the same theory level. QSAR models showed major importance of electrostatic ligand-acceptor interactions and indicated the presence of aromatic interaction in the benzyl group. A photoelectron transfer via hydrogen-bond was proposed to occur between His215 residue and the carbonyl group.

<p>Crystallinity is an important property of lignocellulosic biomass due to its significant effect on acid/enzymatic hydrolysis. Normally, physicochemical analysis, such as powder X-ray diffraction and nuclear magnetic resonance, is used to reveal the crystallinity content. However, these analytical methods are expensive and laborious. In this context, methods that rapidly predict the crystallinity are important, even if used only for screening calibration. Thus, we intend to show the potential of near-infrared spectroscopy (NIRS) and chemometrics to replace reference methods in crystallinity determination. The results show that NIRS can be used to determine crystallinity in banana residues by the use of partial least squares regression, providing good coefficients of determination (R²_cal,pred > 0.82), low relative errors (< 14%) and good range error ratio (≥ 7.7). The interpretation of the regression coefficients, multivariate figures of merit and external validation results indicate a strong relationship between the NIR spectrum and crystallinity in banana samples.</p>

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

A obtenção de informações sobre uma rota biossintética é um procedimento complexo e trabalhoso. Neste sentido, o presente trabalho apresenta uma nova abordagem para a análise inicial da biossíntese de produtos naturais fluorescentes usando a biossíntese da violaceína como exemplo. Para tanto, uma cultura da Chromobacterium violaceum foi inoculada em um biorreator de onde alíquotas eram retiradas a cada 2 h para posterior análise por espectroscopia de fluorescência 2D. As matrizes de emissão-excitação obtidas demonstraram o comportamento dinâmico dos sinais de fluoróforos que são consumidos e produzidos pela bactéria. Estes sinais foram resolvidos pelo método PARAFAC (análise paralela de fatores) perfazendo um total de seis espécies químicas. O triptofano e a violaceína foram identificados por comparação espectral. A identificação dos outros fluoróforos mostrou-se a etapa crítica devido à falta de banco de dados de produtos naturais fluorescentes para comparação. Por fim, esta metodologia apresenta um grande potencial para fornecer informações da biossíntese de produtos naturais.

Obtaining information about a biosynthetic pathway is a complex and laborious procedure. In this sense, this work presents a new approach for the initial analysis of the biosynthesis of fluorescent natural products using as example the violacein biosynthesis. For this, a culture of Chromobacterium violaceum was grown in a bioreactor from which aliquots were collected every 2 h for subsequent analysis by multi-wavelength fluorescence spectroscopy. The excitation-emission matrices demonstrated the dynamic behavior of the fluorophores signal that are consumed and produced by the bacterium. These signals were resolved by PARAFAC (parallel factor analysis) method totalizing six pure components. Tryptophan and violacein were identified by comparison to spectra available in the literature. The identification of other fluorophores was critical step due to the lack of a database of fluorescent natural products to compare spectra. Finally, this methodology has great potential to achieve a deeper insight into the biosynthesis of natural products.

In this work, the volatile chromatographic profiles of roasted Arabica coffees, previously analyzed for their sensorial attributes, were explored by principal component analysis. The volatile extraction technique used was the solid phase microextraction. The correlation optimized warping algorithm was used to align the gas chromatographic profiles. Fifty four compounds were found to be related to the sensorial attributes investigated. The volatiles pyrrole, 1-methyl-pyrrole, cyclopentanone, dihydro-2-methyl-3-furanone, furfural, 2-ethyl-5-methyl-pyrazine, 2-etenyl-n-methyl-pyrazine, 5-methyl-2-propionyl-furan compounds were important for the differentiation of coffee beverage according to the flavour, cleanliness and overall quality. Two figures of merit, sensitivity and specificity (or selectivity), were used to interpret the sensory attributes studied.

Quatro conjuntos de dados de QSAR e QSPR foram selecionados da literatura e os modelos de regressão foram construídos com 75, 56, 50 e 15 amostras no conjunto de treinamento. Estes modelos foram validados por meio de validação cruzada excluindo uma amostra de cada vez, validação cruzada excluindo Namostras de cada vez (LNO), validação externa, randomização do vetor ye validação bootstrap. Os resultados das validações mostraram que o tamanho do conjunto de treinamento é o fator principal para o bom desempenho de um modelo, uma vez que este piora para os conjuntos de dados pequenos. Modelos oriundos de conjuntos de dados muito pequenos não podem ser testados em toda a sua extensão. Além disto, eles podem falhar e apresentar comportamento atípico em alguns dos testes de validação (como, por exemplo, correlações espúrias, falta de robustez na reamostragem e na validação cruzada), mesmo tendo apresentado um bom desempenho na validação cruzada excluindo uma amostra, no ajuste e até na validação externa. Uma maneira simples de determinar o valor crítico de Nem LNO foi introduzida, usando o valor limite de 0,1 para oscilações em Q² (faixa de variações em único LNO e dois desvios padrões em LNO múltiplo). Foi mostrado que 10 - 25 ciclos de randomização de you de bootstrappingsão suficientes para uma validação típica. O uso do método bootstrapbaseado na análise de agrupamentos por métodos hierárquicos fornece resultados mais confiáveis e razoáveis do que aqueles baseados somente na randomização do conjunto de dados completo. A qualidade de dados em termos de significância estatística das relações descritor - yé o segundo fator mais importante para o desempenho do modelo. Uma seleção de variáveis em que as relações insignificantes não foram eliminadas pode conduzir a situações nas quais elas não serão detectadas durante o processo de validação do modelo, especialmente quando o conjunto de dados for grande.

Four quantitative structure-activity relationships (QSAR) and quantitative structure-property relationship (QSPR) data sets were selected from the literature and used to build regression models with 75, 56, 50 and 15 training samples. The models were validated by leave-one-out crossvalidation, leave-N-out crossvalidation (LNO), external validation, y-randomization and bootstrapping. Validations have shown that the size of the training sets is the crucial factor in determining model performance, which deteriorates as the data set becomes smaller. Models from very small data sets suffer from the impossibility of being thoroughly validated, failure and atypical behavior in certain validations (chance correlation, lack of robustness to resampling and LNO), regardless of their good performance in leave-one-out crossvalidation, fitting and even in external validation. A simple determination of the critical Nin LNO has been introduced by using the limit of 0.1 for oscillations in Q², quantified as the variation range in single LNO and two standard deviations in multiple LNO. It has been demonstrated that it is sufficient to perform 10 -25 y-randomization and bootstrap runs for a typical model validation. The bootstrap schemes based on hierarchical cluster analysis give more reliable and reasonable results than bootstraps relying only on randomization of the complete data set. Data quality in terms of statistical significance of descriptor -yrelationships is the second important factor for model performance.Variable selection that does not eliminate insignificant descriptor - yrelationships may lead to situations in which they are not detected during model validation, especially when dealing with large data sets.

An activity for introducing hierarchical cluster analysis (HCA) and principal component analysis (PCA) during the Instrumental Analytical Chemistry course is presented. The posed problem involves the discrimination of mineral water samples according to their geographical origin. Thirty-seven samples of 9 different brands were considered and the results from the determination of Na, K, Mg, Ca, Sr and Ba were taken into account. Non-supervised methods for pattern recognition were explored to construct a dendrogram, score and loading plots. The devised activity can be adopted for introducing Chemometrics devoted to data handling, stressing its importance in the context of modern Analytical Chemistry.

The purpose of this work is to study theoretically stereoelectronic aspects of the interaction between heme and artemisinin in the transitional heme-artemisinin complex. Through semi-empirical calculations using the PM3 method, the potential energy barrier of artemisinin rotation relative to heme in the heme-artemisinin complex was studied in vacuum and in the partially solvated state. The minimum heat of formation obtained for the complex with and without water molecules is -702.39 and -100.86 kcal mol-1, respectively, which corresponds to the dihedral angle C-Fe-O1-O2 of 43.93º and 51.90º around the iron-oxygen O1 bond, respectively. The water molecules bind to heme via 13 hydrogen bonds and O-H•••O and 6 C-H•••O interactions, which accounts for -67.23 kcal mol-1. It is observed that the inclusion of water molecules does not affect significantly the stability of the heme-artemisinin complex.

This work describes, through examples, a simple way to carry out experimental design calculations applying an spreadsheets. The aim of this tutorial is to introduce an alternative to sophisticated commercial programs that normally are too complex in data input and output. An overview of the principal methods is also briefly presented. The spreadsheets are suitable to handle different types of computations such as screening procedures applying factorial design and the optimization procedure based on response surface methodology. Furthermore, the spreadsheets are sufficiently versatile to be adapted to specific experimental designs.

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.

Os resultados obtidos com a análise química de 41 amostras de ceras de própolis e 9 amostras de ceras de favos de Apis mellifera, coletadas principalmente no Brasil, foram estudados usando Análise de Componentes Principais e Análise Hierárquica de Grupos. Na análise quimiométrica foram consideradas a distribuição de hidrocarbonetos e de resíduos alcoólicos e ácidos de mono-ésteres. O dendrograma obtido revelou afinidades químicas e diferenças que não puderam ser constatadas com a simples inspeção visual dos dados. Nenhuma diferença consistente foi detectada entre ceras de própolis e de favos. Os resultados deste e de outros trabalhos sugerem que hidrocarbonetos, ácidos carboxílicos, álcoois alifáticos e ésteres das ceras de própolis e de favos são produzidos pelas abelhas e, portanto, as diferenças detectadas entre uma e outra região são mais dependentes de fatores genéticos relacionados aos insetos do que da flora local. As amostras analisadas reuniram-se segundo dois grandes agrupamentos, um deles contendo exclusivamente amostras coletadas no Estado de São Paulo. Os resultados são discutidos levando em consideração o fato de que a africanização das abelhas teve início nesse estado, ocorrendo posteriormente a sua irradiação para outras partes do Brasil.

Chemical composition data for 41 samples of propolis waxes and 9 samples of comb waxes of Apis mellifera collected mainly in Brazil were treated using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). For chemometrical analysis, the distribution of hydrocarbons and residues of alcohols and carboxylic acids of monoesters were considered. The clustering obtained revealed chemical affinities and differences not previously grasped by simple eye-inspection of the data. No consistent differences were detected between comb and propolis waxes. These and previous results suggest that hydrocarbons, carboxylic acids, aliphatic alcohols and esters from both comb and propolis waxes are bee-produced compounds and, hence, the differences detected between one and another region are dependent on genetic factors related to the insects rather than the local flora. The samples analyzed were split into two main clusters, one of them comprising exclusively material collected in the State of São Paulo. The results are discussed with respect to the africanization of honeybees that first took place in that State and therefrom irradiated to other parts of Brazil.

Neste trabalho, foi feito um estudo de relações quantitativas entre a estrutura e a atividade biológica de 21 derivados de progesteronas ministrados via oral, dentre os quais 19 são 17-alfa-acetoxiprogesteronas. O método de quadrados mínimos parciais foi usado para construir modelos de regressão de boa qualidade, com Q² = 0,707 e R² = 0,811 utilizando duas componentes principais e quatro descritores. A maioria dos descritores moleculares foi obtida a partir de gráficos moleculares das geometrias otimizadas por meio de cálculos ab initio com um conjunto de base DFT 6-31G** (descritores moleculares gráficos). Os outros descritores foram obtidos pela combinação dos descritores anteriores com parâmetros estruturais experimentais extraídos do complexo progesterona-receptor da progesterona (descritores gráfico-estruturais ou descritores gráficos e de modelagem). Os modelos de regressão empregando somente cinco descritores gráficos e três componentes principais foram satisfatórios, Q²=0,556, R²=0,718, demonstrando a utilidade dos mesmos em estudos QSAR. Neste trabalho, onde foram estudados derivados de progesterona, ficou evidente que os descritores moleculares gráficos descreveram com sucesso os efeitos conformacionais, estéreos e eletrônicos dos diferentes substituintes.

Quantitative Structure-Activity Relationship study on 21 oral progestogens, 19 of which are 17-alpha-acetoxyprogesterones, was performed by using Partial Least Squares. Fairly good regression models were achieved, the best being Q²=0.707, R²=0.811 with two Principal Components and four descriptors. Most of the molecular descriptors were generated from molecular graphics of DFT 6-31G** optimized geometries (molecular graphics descriptors) or were additionally combined with experimental structural parameters of progesterone receptor - progesterone complex (molecular graphics-structural, or molecular graphics and modeling descriptors). Regression models employing only these molecular graphics-based descriptors reached Q²=0.556, R²=0.718 with three Principal Components and five descriptors, demonstrating their usefulness in QSAR studies. In the case of progesterone derivatives, molecular graphics descriptors successfully included various conformational, steric and electronic substituent effects.

Neste trabalho, são apresentadas as técnicas usuais de quimiometria em estudos de relações quantitativas estrutura-atividade biológica (QSAR). Elas são introduzidas em ordem cronológica, iniciando pela análise de Hansch, e os métodos de análise exploratória de componentes principais e agrupamento hierárquico (PCA e HCA). Os métodos de regressão que usam a análise de componentes principais como fundamento (PCR e PLS) são apresentados a seguir. São introduzidos também os dois métodos de reconhecimento de padrões usados para classificação (KNN e SIMCA). Diferentes aplicações são usadas para ilustrar os diferentes métodos quimiométricos. É feito então um resumo da metodologia de regressão usada em QSAR-3D. Finalmente é apresentada uma nova metodologia, denominada PLS multilinear, já utilizada em química analítica, mas não explorada ainda pela comunidade de QSAR. Este método de regressão mantém a estrutura de ordem superior dos dados em lugar do desdobramento utilizado em QSAR-3D e apresenta vantagens computacionais, maior estabilidade e simplicidade do modelo, uma vez que o número de parâmetros calculados é bastante reduzido.

In this work, the chemometric techniques most frequently used in QSAR (quantitative structure-activity relationships) studies are reviewed. They are introduced in chronological order, beginning with Hansch analysis and the exploratory data analysis methods of principal components and hierarchical clustering (PCA and HCA). Principal component regression and partial least squares regression methods (PCR and PLS) are discussed, followed by the pattern recognition methods (KNN and SIMCA). Different applications are presented to illustrate these chemometric techniques. The methodology used for regression in 3D-QSAR is presented (unfolding PLS). Finally, the higher order method called Multilinear PLS, already used in analytical chemistry but not yet explored by the QSAR community, is introduced. This method maintains the multiway structure of the data and has several advantages over bilinear PLS including speed in calculation, simplicity and stability, since the number of parameters to be estimated can be greatly reduced.