Hidrocarbonetos policíclicos aromáticos (HPAS) em cachaça, rum, uísque e álcool combustível

The concentration of 15 polycyclic aromatic hydrocarbons (PAHs) in 57 samples of distillates (cachaça, rum, whiskey, and alcohol fuel) has been determined by HPLC-Fluorescence detection. The quantitative analytical profile of PAHs treated by Partial Least Square - Discriminant Analysis (PLS-DA) provided a good classification of the studied spirits based on their PAHs content. Additionally, the classification of the sugar cane derivatives according to the harvest practice was obtained treating the analytical data by Linear Discriminant Analysis (LDA), using naphthalene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, benz[b]fluoranthene, and benz[g,h,i]perylene, as a chemical descriptors.

Distinção entre cachaças destiladas em alambiques e em colunas usando quimiometria

One hundred fifteen cachaça samples derived from distillation in copper stills (73) or in stainless steels (42) were analyzed for thirty five itens by chromatography and inductively coupled plasma optical emission spectrometry. The analytical data were treated through Factor Analysis (FA), Partial Least Square Discriminant Analysis (PLS-DA) and Quadratic Discriminant Analysis (QDA). The FA explained 66.0% of the database variance. PLS-DA showed that it is possible to distinguish between the two groups of cachaças with 52.8% of the database variance. QDA was used to build up a classification model using acetaldehyde, ethyl carbamate, isobutyl alcohol, benzaldehyde, acetic acid and formaldehyde as chemical descriptors. The model presented 91.7% of accuracy on predicting the apparatus in which unknown samples were distilled.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

NEAR INFRARED SPECTROSCOPY FOR ESTIMATING SUGARCANE BAGASSE CONTENT IN MEDIUM DENSITY FIBERBOARD

Medium density fiberboard (MDF) is an engineered wood product formed by breaking down selected lignin-cellulosic material residuals into fibers, combining it with wax and a resin binder, and then forming panels by applying high temperature and pressure. Because the raw material in the industrial process is ever-changing, the panel industry requires methods for monitoring the composition of their products. The aim of this study was to estimate the ratio of sugarcane (SC) bagasse to Eucalyptus wood in MDF panels using near infrared (NIR) spectroscopy. Principal component analysis (PCA) and partial least square (PLS) regressions were performed. MDF panels having different bagasse contents were easily distinguished from each other by the PCA of their NIR spectra with clearly different patterns of response. The PLS-R models for SC content of these MDF samples presented a strong coefficient of determination (0.96) between the NIR-predicted and Lab-determined values and a low standard error of prediction (similar to 1.5%) in the cross-validations. A key role of resins (adhesives), cellulose, and lignin for such PLS-R calibrations was shown. PLS-DA model correctly classified ninety-four percent of MDF samples by cross-validations and ninety-eight percent of the panels by independent test set. These NIR-based models can be useful to quickly estimate sugarcane bagasse vs. Eucalyptus wood content ratio in unknown MDF samples and to verify the quality of these engineered wood products in an online process.

Characterizing chaotic melodies in automatic music composition

In this paper, we initially present an algorithm for automatic composition of melodies using chaotic dynamical systems. Afterward, we characterize chaotic music in a comprehensive way as comprising three perspectives: musical discrimination, dynamical influence on musical features, and musical perception. With respect to the first perspective, the coherence between generated chaotic melodies (continuous as well as discrete chaotic melodies) and a set of classical reference melodies is characterized by statistical descriptors and melodic measures. The significant differences among the three types of melodies are determined by discriminant analysis. Regarding the second perspective, the influence of dynamical features of chaotic attractors, e.g., Lyapunov exponent, Hurst coefficient, and correlation dimension, on melodic features is determined by canonical correlation analysis. The last perspective is related to perception of originality, complexity, and degree of melodiousness (Euler's gradus suavitatis) of chaotic and classical melodies by nonparametric statistical tests. (c) 2010 American Institute of Physics. [doi: 10.1063/1.3487516]

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Laser-induced breakdown spectroscopy and chemometrics for classification of toys relying on toxic elements

Quality control of toys for avoiding children exposure to potentially toxic elements is of utmost relevance and it is a common requirement in national and/or international norms for health and safety reasons. Laser-induced breakdown spectroscopy (LIBS) was recently evaluated at authors` laboratory for direct analysis of plastic toys and one of the main difficulties for the determination of Cd. Cr and Pb was the variety of mixtures and types of polymers. As most norms rely on migration (lixiviation) protocols, chemometric classification models from LIBS spectra were tested for sampling toys that present potential risk of Cd, Cr and Pb contamination. The classification models were generated from the emission spectra of 51 polymeric toys and by using Partial Least Squares - Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA) and K-Nearest Neighbor (KNN). The classification models and validations were carried out with 40 and 11 test samples, respectively. Best results were obtained when KNN was used, with corrected predictions varying from 95% for Cd to 100% for Cr and Pb. (C) 2011 Elsevier B.V. All rights reserved.

Inter-individual variability in temporal structure of the basketball shoot

This study analyzed inter-individual variability of the temporal structure applied in basketball throwing. Ten experienced male athletes in basketball throwing were filmed and a number of kinematic movement parameters analyzed. A biomechanical model provided the relative timing of the shoulder, elbow and wrist joint movements. Inter-individual variability was analyzed using sequencing and relative timing of tem phases of the throw. To compare the variability of the movement phases between subjects a discriminant analysis and an ANOVA were applied. The Tukey test was applied to determine where differences occurred. The significance level was p = 0.05. Inter-individual variability was explained by three concomitant factors: (a) a precision control strategy, (b) a velocity control strategy and (c) intrinsic characteristics of the subjects. Therefore, despite the fact that some actions are common to the basketball throwing pattern each performed demonstrated particular and individual characteristics.

Greenhouse gases emission from soil contaminated with automobile industry residue in Brazil

Solid waste of the automobile industry containing large amounts of heavy metals might affect the emission of greenhouse gases (GHG) when applied to the soil. Accumulation of inorganic chemical elements in the environment generally occurs due to human activity (industry, agriculture, mining and waste landfills). Residues from human activities may release heavy metals to the soil solution, causing toxicity to plants and other soil organisms. Heavy metals may also be adsorbed to clay minerals and/or complexed by the soil organic matter, becoming a potential source of pollutants. Not much is known about the behavior of solid wastes in tropical soil as regarded as source of greenhouse gases (GHG). The emission of GHG (CO(2), CH(4) and N(2)O) was evaluated in incubated soil samples collected in an area contaminated with a solid residue from an automobile industry. Samples were randomly collected at 0 to 0.2 m (a mix of soil and residue), 0.2 to 0.4 m (only residue) and 0.4 to 0.6 m (only soil). A contiguous uncontaminated area, cultivated with sugarcane, was also sampled following the same protocol. Canonical Discriminant Analysis and Principal Component Analysis were applied to the data to evaluate the GHG emission rates. Emission rates of GHG were greater in the samples from the contaminated than the sugarcane area, particularly high during the first days of incubation. CO(2) emissions were greater in samples collected at the upper layer for both areas, while CH(4) and N(2)O emissions were similar in all samples. The emission rates of CH(4) were the most efficient variables to differentiate contaminated and uncontaminated areas.

Influence of magnesium status and magnesium intake on the blood glucose control in patients with type 2 diabetes

Background & aims: This study was undertaken to assess magnesium intake and magnesium status in patients with type 2 diabetes, and to identify the parameters that best predict alterations in fasting glucose and plasma magnesium. Methods: A cross-sectional study was carried out in patients with type 2 diabetes (n = 51; 53.6 +/- 10.5 y) selected within the inclusion factors, at the University Hospital Onofre Lopes. Magnesium intake was assessed by three 24-h recalls. Urine, plasma and erythrocytes magnesium, fasting and 2-h postprandial glucose, HbA1, microalbuminuria, proteinuria, and serum and urine creatinine were measured. Results: Mean magnesium intake (9.37 +/- 1.76 mmol/d), urine magnesium (2.80 +/- 1.51 mmol/d), plasma magnesium (0.71 +/- 0.08 mmol/L) and erythrocyte magnesium (1.92 +/- 0.23 mmol/L) levels were low. Seventy-seven percent of participants presented one or more magnesium status parameters below the cut-off points of 3.00 mmol/L for urine, 0.75 mmol/L for plasma and 1.65 mmol/L for erythrocytes. Subjects presented poor blood glucose control with fasting glucose of 8.1 +/- 3.7 mmol/L, 2-h postprandial glucose of 11.1 +/- 5.1 mmol/L, and HbA1 of 11.4 +/- 3.0%. The parameters that influenced fasting glucose were urine, plasma and dietary magnesium, while plasma magnesium was influenced by creatinine clearance. Conclusions: Magnesium status was influenced by kidney depuration and was altered in patients with type 2 diabetes, and magnesium showed to play an important role in blood glucose control. (C) 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Referral to post-acute care following traumatic brain injury (TBI) in the Australian context

The study aimed to describe the types of care allocated at the end of acute care to people diagnosed with TBI and to identify the factors associated with variations in referral to care. A retrospective analysis of medical records of 61 patients was conducted based on a sample from two hospitals. While 60.7% of the study sample were referred to formal rehabilitation care, this was primarily non-inpatient rehabilitation care (32.8%). Discriminant analysis was used to determine medical and non-medical predictors of referral. Results indicated that place of treatment and age contribute to group differences and were significant in separating the inpatient rehabilitation group from the non-inpatient and no rehabilitation groups. Review by a rehabilitation physician was associated with referral to inpatient rehabilitation but was not adequate to explain referral to non-inpatient rehabilitation. An in-depth exploration of post-acute referral is warranted to improve policy and practice in relation to continuity of care following TBI.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Altered endothelial function following the Fontan procedure

Objective: Thrombosis has been widely described after the Fontan procedure. The vascular endothelium plays a central role in the control of coagulation and fibrinolysis. The aim of this study was to investigate if patients undergoing a modified Fontan procedure have impaired endothelial function and fibrinolysis in the late postoperative course. Patients and methods: We compared 23 patients aged from 7 to 26 years with age-matched healthy volunteers, collecting blood samples prior to and following standardized venous occlusion testing. Plasma levels of von Willebrand factor antigen, tissue-type plasminogen activator antigen, plasminogen activator inhibitor-1, and D-dimer were measured with enzyme-linked immunosorbent assay. Results: We found increased plasma levels of von Willebrand factor antigen in patients when compared to controls (p = 0.003). At the basal condition, concentrations of tissue-type plasminogen activator antigen and plasminogen activator inhibitor-1 antigen in the plasma, as well as their activity, were not significantly different between patients and controls. Following venous occlusion, concentrations of tissue-type plasminogen activator antigen in the plasma were significantly increased both in patients and controls, compared to pre-occlusion values. D-dimer was within the reference range. Multivariate discriminant analysis differentiated patients and their controls on the basis of differences for plasminogen activator inhibitor-1 and von Willebrand factor antigen (p = 0.0016). Conclusions: Our data suggest that patients with the Fontan circulation may have endothelial dysfunction, as indicated by raised levels of von Willebrand factor. Fibrinolysis seems to be relatively preserved, as suggested by appropriate response to venous occlusion.