Development and chemical and sensory characterization of pumpkin seed flour-based cereal bars

Pumpkin (Cucurbita maxima), popularly known as squash, is a widely grown vegetable in Brazil. In this study, pumpkin seed flours (PSF) with different granulometries were used: PSF 1 (medium granulometry) and PSF 2 (coarse granulometry) in the preparation of cereal bars (CB) with different combinations with brown oats. Five formulations were prepared: CB-1 (control - 25% brown oats and 0% PSF); CB-2 (12.5% PSF 1 and 12.5% brown oats); CB-3 (25% PSF 1 and 0% brown oats); CB-4 (12.5% PSF 2 and 12.5% brown oats); and CB-5 (25% PSF 2 and 0% brown oats). The acceptance test results were analyzed in a conventional preference mapping which indicated that the bars CB-2 and CB-5 received mostly the maximum hedonistic score. With the objective of developing a cereal bar replacing oats with PSF, the bars CB-2 and CB-5 were compared to the conventional bar CB-1. The cereal bars CB-2 and CB-5 showed an increase in crude protein (87.5% and 62.5%) and in dietary fiber (77% and 44%), respectively. These results allowed the classification of CB-2 and CB-5 as fiber sources; they can, therefore, be classified as light products according to the Brazilian legislation.

Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site-specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (ECa) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and ECa. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.

Evolution of micromorphological and chemical characters in the lichen-forming fungal family Pertusariaceae

Micromorphological characters of the fruiting bodies, such as ascus-type and hymenial amyloidity, and secondary chemistry have been widely employed as key characters in Ascomycota classification. However, the evolution of these characters has yet not been studied using molecular phylogenies. We have used a combined Bayesian and maximum likelihood based approach to trace character evolution on a tree inferred from a combined analysis of nuclear and mitochondrial ribosomal DNA sequences. The maximum likelihood aspect overcomes simplifications inherent in maximum parsimony methods, whereas the Markov chain Monte Carlo aspect renders results independent of any particular phylogenetic tree. The results indicate that the evolution of the two chemical characters is quite different, being stable once developed for the medullary lecanoric acid, whereas the cortical chlorinated xanthones appear to have been lost several times. The current ascus-types and the amyloidity of the hymenial gel in Pertusariaceae appear to have been developed within the family. The basal ascus-type of pertusarialean fungi remains unknown. (c) 2006 The Linnean Society of London, Biological Journal of the Linnean Society, 2006, 89, 615-626.

The monitoring of ecological quality and the classification of standing waters in temperate regions: a review and proposal based on a worked scheme for British waters

This paper reviews the ways that quality can be assessed in standing waters, a subject that has hitherto attracted little attention but which is now a legal requirement in Europe. It describes a scheme for the assessment and monitoring of water and ecological quality in standing waters greater than about I ha in area in England & Wales although it is generally relevant to North-west Europe. Thirteen hydrological, chemical and biological variables are used to characterise the standing water body in any current sampling. These are lake volume, maximum depth, onductivity, Secchi disc transparency, pH, total alkalinity, calcium ion concentration, total N concentration,winter total oxidised inorganic nitrogen (effectively nitrate) concentration, total P concentration, potential maximum chlorophyll a concentration, a score based on the nature of the submerged and emergent plant community, and the presence or absence of a fish community. Inter alia these variables are key indicators of the state of eutrophication, acidification, salinisation and infilling of a water body.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Use of Metals and Anion Species with Chemometrics Tools for Classification of Unprocessed and Processed Coconut Waters

Coconut water is a natural isotonic, nutritive, and low-caloric drink. Preservation process is necessary to increase its shelf life outside the fruit and to improve commercialization. However, the influence of the conservation processes, antioxidant addition, maturation time, and soil where coconut is cultivated on the chemical composition of coconut water has had few arguments and studies. For these reasons, an evaluation of coconut waters (unprocessed and processed) was carried out using Ca, Cu, Fe, K, Mg, Mn, Na, Zn, chloride, sulfate, phosphate, malate, and ascorbate concentrations and chemometric tools. The quantitative determinations were performed by electrothermal atomic absorption spectrometry, inductively coupled plasma optical emission spectrometry, and capillary electrophoresis. The results showed that Ca, K, and Zn concentrations did not present significant alterations between the samples. The ranges of Cu, Fe, Mg, Mn, PO (4) (3-) , and SO (4) (2-) concentrations were as follows: Cu (3.1-120 A mu g L(-1)), Fe (60-330 A mu g L(-1)), Mg (48-123 mg L(-1)), Mn (0.4-4.0 mg L(-1)), PO (4) (3-) (55-212 mg L(-1)), and SO (4) (2-) (19-136 mg L(-1)). The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to differentiate unprocessed and processed samples. Multivariated analysis (PCA and HCA) were compared through one-way analysis of variance with Tukey-Kramer multiple comparisons test, and p values less than 0.05 were considered to be significant.

Chemical profile of rums as a function of their origin. The use of chemometric techniques for their identification

To identify chemical descriptors to distinguish Cuban from non-Cuban rums, analyses of 44 samples of rum from 15 different countries are described. To provide the chemical descriptors, analyses of the the mineral fraction, phenolic compounds, caramel, alcohols, acetic acid, ethyl acetate, ketones, and aldehydes were carried out. The analytical data were treated through the following chemometric methods: principal component analysis (PCA), partial least square-discriminate analysis (PLS-DA), and linear discriminate analysis (LDA). These analyses indicated 23 analytes as relevant chemical descriptors for the separation of rums into two distinct groups. The possibility of clustering the rum samples investigated through PCA analysis led to an accumulative percentage of 70.4% in the first three principal components, and isoamyl alcohol, n-propyl alcohol, copper, iron, 2-furfuraldehyde (furfuraldehyde), phenylmethanal (benzaldehyde), epicatechin, and vanillin were used as chemical descriptors. By applying the PLS-DA technique to the whole set of analytical data, the following analytes have been selected as descriptors: acetone, sec-butyl alcohol, isobutyl alcohol, ethyl acetate, methanol, isoamyl alcohol, magnesium, sodium, lead, iron, manganese, copper, zinc, 4-hydroxy3,5-dimethoxybenzaldehyde (syringaldehyde), methaldehyde (formaldehyde), 5-hydroxymethyl-2furfuraldehyde (5-HMF), acetalclehyde, 2-furfuraldehyde, 2-butenal (crotonaldehyde), n-pentanal (valeraldehyde), iso-pentanal (isovaleraldehyde), benzaldehyde, 2,3-butanodione monoxime, acetylacetone, epicatechin, and vanillin. By applying the LIDA technique, a model was developed, and the following analytes were selected as descriptors: ethyl acetate, sec-butyl alcohol, n-propyl alcohol, n-butyl alcohol, isoamyl alcohol, isobutyl alcohol, caramel, catechin, vanillin, epicatechin, manganese, acetalclehyde, 4-hydroxy-3-methoxybenzoic acid, 2-butenal, 4-hydroxy-3,5-dimethoxybenzoic acid, cyclopentanone, acetone, lead, zinc, calcium, barium, strontium, and sodium. This model allowed the discrimination of Cuban rums from the others with 88.2% accuracy.

Application of chemometric methods in the evaluation of chemical and spectroscopic data on organic matter from Oxisols in sewage sludge applications

Chemometric methods can contribute to soil research by permitting the extraction of more information from the data. The aim of this work was to use Principal Component Analysis to evaluate data obtained through chemical and spectroscopic methods on the changes in the humification process of soil organic matter from two tropical soils after sewage sludge application. In this case, humic acids extracted from Typic Eutrorthox and Typic Haplorthox soils with and without sewage sludge application for 7 consecutive years were studied. The results obtained for all of the samples and methods showed two clusters: samples extracted from the two soil types. These expected results indicated the textural difference between the two soils was more significant than the differences between treatments (control and sewage sludge application) or between depths. In this case, an individual chemometric treatment was made for each type of soil. It was noted that the characterization of the humic acids extracted from soils with and without sewage sludge application after 7 consecutive years using several methods supplies important results about changes in the humification degree of soil organic matter, These important result obtained by Principal Component Analysis justify further research using these methods to characterize the changes in the humic acids extracted from sewage sludge-amended soils. (C) 2009 Elsevier B.V. All rights reserved.

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

In order to differentiate and characterize Madeira wines according to main grape varieties, the volatile composition (higher alcohols, fatty acids, ethyl esters and carbonyl compounds) was determined for 36 monovarietal Madeira wine samples elaborated from Boal, Malvazia, Sercial and Verdelho white grape varieties. The study was carried out by headspace solid-phase microextraction technique (HS-SPME), in dynamic mode, coupled with gas chromatography–mass spectrometry (GC–MS). Corrected peak area data for 42 analytes from the above mentioned chemical groups was used for statistical purposes. Principal component analysis (PCA) was applied in order to determine the main sources of variability present in the data sets and to establish the relation between samples (objects) and volatile compounds (variables). The data obtained by GC–MS shows that the most important contributions to the differentiation of Boal wines are benzyl alcohol and (E)-hex-3-en-1-ol. Ethyl octadecanoate, (Z)-hex-3-en-1-ol and benzoic acid are the major contributions in Malvazia wines and 2-methylpropan-1-ol is associated to Sercial wines. Verdelho wines are most correlated with 5-(ethoxymethyl)-furfural, nonanone and cis-9-ethyldecenoate. A 96.4% of prediction ability was obtained by the application of stepwise linear discriminant analysis (SLDA) using the 19 variables that maximise the variance of the initial data set.

Computational analysis and physico-chemical characterization of an inclusion compound between praziquantel and methyl-beta-cyclodextrin for use as an alternative in the treatment of schistosomiasis

Schistosomiasis is still an endemic disease in many regions, with 250 million people infected with Schistosoma and about 500,000 deaths per year. Praziquantel (PZQ) is the drug of choice for schistosomiasis treatment, however it is classified as Class II in the Biopharmaceutics Classification System, as its low solubility hinders its performance in biological systems. The use of cyclodextrins is a useful tool to increase the solubility and bioavailability of drugs. The aim of this work was to prepare an inclusion compound of PZQ and methyl-beta-cyclodextrin (MeCD), perform its physico-chemical characterization, and explore its in vitro cytotoxicity. SEM showed a change of the morphological characteristics of PZQ:MeCD crystals, and IR data supported this finding, with changes after interaction with MeCD including effects on the C-H of the aromatic ring, observed at 758 cm(-1). Differential scanning calorimetry measurements revealed that complexation occurred in a 1:1 molar ratio, as evidenced by the lack of a PZQ transition temperature after inclusion into the MeCD cavity. In solution, the PZQ UV spectrum profile in the presence of MeCD was comparable to the PZQ spectrum in a hydrophobic solvent. Phase solubility diagrams showed that there was a 5.5-fold increase in PZQ solubility, and were indicative of a type A(L) isotherm, that was used to determine an association constant (K(a)) of 140.8 M(-1). No cytotoxicity of the PZQ:MeCD inclusion compound was observed in tests using 3T3 cells. The results suggest that the association of PZQ with MeCD could be a good alternative for the treatment of schistosomiasis.

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Wine classification by taste sensors made from ultra-thin films and using neural networks

This paper reports on a sensor array able to distinguish tastes and used to classify red wines. The array comprises sensing units made from Langmuir-Blodgett (LB) films of conducting polymers and lipids and layer-by-layer (LBL) films from chitosan deposited onto gold interdigitated electrodes. Using impedance spectroscopy as the principle of detection, we show that distinct clusters can be identified in principal component analysis (PCA) plots for six types of red wine. Distinction can be made with regard to vintage, vineyard and brands of the red wine. Furthermore, if the data are treated with artificial neural networks (ANNs), this artificial tongue can identify wine samples stored under different conditions. This is illustrated by considering 900 wine samples, obtained with 30 measurements for each of the five bottles of the six wines, which could be recognised with 100% accuracy using the algorithms Standard Backpropagation and Backpropagation momentum in the ANNs. (C) 2003 Elsevier B.V. All rights reserved.

Self-Organizing Maps as a New Tool for Classification of Plants at Lower Hierarchical Levels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Classification of Aristolochia species based on GC-MS and chemometric analyses of essential oils

Essential oils were obtained from roots of 10 Aristolochia species by hydrodistillation and analysed by GC MS. A total of 75 compounds were identified in the analysed oils. Multivariate analyses of the chemical constituents of the roots enabled classification of the species into four morphological groups. These forms of analysis represent an aid in identification of further specimens belonging to these species. (C) 2007 Elsevier Ltd. All rights reserved.

Multi-method Study on Aquatic Humic Substances from the Rio Negro - Amazonas State/Brazil: Emphasis on Molecular-Size Classification of their Metal Contents

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)