Comparing Near-infrared Conventional Diffuse Reflectance Spectroscopy And Hyperspectral Imaging For Determination Of The Bulk Properties Of Solid Samples By Multivariate Regression: Determination Of Mooney Viscosity And Plasticity Indices Of Natural Rubber.

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Variabilidade espacial e sazonal da concentração de elementos-traço em sedimentos do sistema estuarino de Santos-Cubatão (SP)

Multi-element analyses of sediment samples from the Santos-Cubatão Estuarine System were carried out to investigate the spatial and seasonal variability of trace-element concentrations. The study area contains a rich mangrove ecosystem that is a habitat for tens of thousands of resident and migratory birds, some of them endangered globally. Enrichments of metals in fine-grained surface sediments are, in decreasing order, Hg, Mn, La, Ca, Sr, Cd, Zn, Pb, Ba, Cu, Cr, Fe, Nb, Y, Ni and Ga, relative to pre-industrial background levels. The maximum enrichment ranged from 49 (Hg) to 3.1 (Ga). Mercury concentrations were greater in the Cubatão river than in other sites, while the other elements showed greater concentrations in the Morrão river. Concentrations of Mn were significantly greater in winter and autumn than in summer and spring. However, other elements (e.g. Cd and Pb) showed the opposite, with greater concentrations in summer and spring. This study suggests that seasonal changes in physical and chemical conditions may affect the degree of sediment enrichment and therefore make the assessment of contamination difficult. Consequently, these processes need to be considered when assessing water quality and the potential contamination of biota.

Calibração da Reflectometria no Domínio do Tempo (TDR) para a estimativa da concentração da solução no solo

Time Domain Reflectometry (TDR) is a reliable method for in-situ measurements of the humidity and the solution concentration at the same soil volume. Accurate interpretation of electrical conductivity (and soil humidity) measurements may require a specific calibration curve. The primary goal of this work was to establish a calibration procedure for using TDR to estimate potassium nitrate concentrations (KNO3) in soil solution. An equation relating the electrical conductivity measured by TDR and KNO3 concentration was established enabling the use of TDR technique to estimate soil water content and nitrate concentration for efficient fertigation management.

Estimativa de bem-estar de frango de corte em função da concentração de amônia e grau de luminosidade no galpão de produção

Brazil is an important poultry meat export country, and large parts of its destination are countries with specific rearing restrictions related to broiler s welfare. One of the aerial pollutants mostly found in high concentrations in closed poultry housing environment is ammonia. There are evidences that broilers welfare may be compromised by the continuous exposition to this pollutant in rearing housing. This research aimed to estimate broilers welfare reared under specific thermal environmental attributes and bird s density, as function of the ammonia concentration and light intensity inside the housing environment using the Fuzzy Theory. Results showed that the best welfare value (0.89 in the scale: 0-1) approximately 90% of the ideal was found in the conditions that associated the ideal thermal environment, with bird s density between 13-15 birds m-2, with values of the ammonia concentration in the environment below 5 ppm, and light intensity near 1 lx. Using the predictive method it was possible to estimate broilers welfare with relation to the ammonia concentration and light intensity in the housing.

Análise tempo-intensidade dos estímulos doce e amargo de extrato de folhas de estévia [Stevia rebaudiana (Bert.) Bertoni] em doçura equivalente a sacarose

The extract of stevia leaves (Stevia rebaudiana Bertoni) is the only sweetener utilized in sucrose substitution which can be produced totally in Brazil. The objective of this study, was determine the temporal characteristic of sweet and bitter taste of stevia and compare with sucrose at 3 and 10% in the same equi-sweet. The time-intensity curves (T-I) for each substance were collected through the software Sistema de Coleta de Dados Tempo-Intensidade - SCDTI for Windows, where the judges recorded through of mouse the perception of each stimuli inside function of time, for each sample. The parameters of T-I curves collected were: time for intensity maxim (TImax), intensity maxim (Imax), time of decay (Td), time of plato (Platô), area under curve (Area) and total time of stimuli duration (Ttot). The parameters Td, Ttot, Area e Plato of T-I curves, for stimuli sweet in both sweetness level, were significativelly superior for stevia, while Timax e Imax were significativelly inferior (p£0,05), at differences between value for both substances were superior DESS at 10%. Sucrose didn?t present any record for simuli bitter as 3 as 10%, while stevia presented a characteristic T-I curve with intensity and total time of stimuli duration dependent of concentration.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Adaptações das curvas da coluna vertebral na marcha em função da altura dos saltos dos calçados

Universidade Estadual de Campinas. Faculdade de Educação Física

Metabolismo de lipoproteinas plasmaticas em fumantes cronicos sob o efeito agudo do fumo

Universidade Estadual de Campinas . Faculdade de Educação Física