Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

Viabilidade do pólen e desenvolvimento do tubo polínico em macieira (Malus spp.)

No presente trabalho, estudaram-se características associadas à germinação in vitro e ao desenvolvimento in vivo do tubo polínico em seis variedades-copa e de porta-enxertos de macieira como subsídios para o estabelecimento de programas de melhoramento genético. Utilizou-se de pólen de seis cultivares de macieira inoculado em meio de cultura com ágar (10 g.L-1) em água destilada, combinados com concentrações de sacarose (0; 10; 20; 30; 40 e 50%) e ácido bórico (0 e 40 mg.L-1). Para o estudo do desenvolvimento do tubo polínico, realizou-se coleta das flores em quatro períodos (6; 12; 24 e 48 horas após as polinizações) em M9 x Marubakaido e a autofecundação em M9, sendo os tubos polínicos analisados em coloração de azul de anilina acidificada/carmim acético e em fluorescência. A sacarose, em concentrações entre 15% a 25%, pode ser empregada com sucesso para a germinação in vitro de grãos de pólen da macieira. O ácido bórico não teve efeito positivo para esta característica. Na ausência do ácido bórico e na presença de 15% de sacarose, observaram-se os maiores percentuais de germinação: Fuji (51,1%), Imperatriz (31,7%), M.9 (20,8%), Catarina (19,2%), Gala (13,7%) e Marubakaido (6,1%). Quanto ao desenvolvimento do tubo polínico, com 12 horas da polinização, iniciou-se a germinação no pólen, no estigma, no cruzamento M.9 x Marubakaido, e após 24 horas da polinização observou-se 83% de germinação. As técnicas de coloração com azul de anilina acidificada com carmim acético e de visualização em fluorescência foram eficientes na visualização e coloração dos grãos de pólen e do desenvolvimento dos tubos polínicos.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Redução de dose e aumento na vida útil do tubo de raios X em tomografia computadorizada

OBJETIVO: Reduzir a dose de radiação e aumentar a vida útil do tubo de raios X em exames de tomografia computadorizada. MATERIAIS E MÉTODOS: Foram avaliados exames de crânio, abdome superior e tórax. Foi verificado se a técnica utilizada poderia ser alterada, foram sugeridos novos protocolos, e feitas comparações de qualidade da imagem, dose de radiação e aquecimento do tubo de raios X. RESULTADOS: Uma redução no mAs pôde ser feita sem comprometer a qualidade do diagnóstico, proporcionando redução de até 20% na dose média dos exames de crânio em adultos e de até 45% em crianças com idade de 0 a 6 meses; pacientes com menos de 50 kg tiveram redução de aproximadamente 37% na dose média de radiação para os exames de abdome superior; para o exame de tórax de rotina a redução chegou a 54%. O aquecimento do tubo de raios X para os exames de crânio, abdome superior e tórax teve redução estimada em aproximadamente 13%, 23% e 41%, respectivamente. CONCLUSÃO: Uma alteração nos protocolos dos exames descritos acarretará diminuição significativa na dose de radiação e aumento na vida útil do tubo de raios X, sem comprometer o diagnóstico.

Uma célula eletroquímica de fluxo para modificação permanente de tubo de grafite empregado em absorção atômica

A flow cell assembled on the original geometry of a graphite tube to achieve permanent chemical modifier is proposed. The graphite tube operates as the working electrode. A stainless steel tube, positioned downstream from the working electrode, was used as the auxiliary electrode. The potential value applied on the graphite electrode was measured against a micro reference electrode (Ag/AgCl) inserted into the auxiliary electrode. Palladium solutions in acetate buffer (100 mmol L-1, pH = 4.8), flowing at 0.5 mL min-1 for 60 min was used to perform the electrochemical modification. A mercury solution (1 ng) was used to evaluate the performance of the permanent palladium modifier.

Entre a batuta e o tubo de ensaio: a carreira admirável de Alexandre Borodin

Alexander Borodin (1833-1887) is a singularity in the history of science. Whereas other scientists may have kept lifelong interests in some artistic field, he was unique in pursuing with great success two parallel careers in both chemistry and music, managing to excell in both to the end of his life. Although he considered himself primarily a chemist, present-day appreciation of his powerful music has greatly surpassed interest for his chemistry. This article treats the life and the unusual double career of the Russian chemist-musician.

Eletrodeposição de irídio em tubo de grafite como modificador químico permanente em espectrometria de absorção atômica

A tubular electrochemical flow-cell for iridium deposition on the inner surface of pyrolytic graphite tube for permanent chemical modification is proposed. A transversal heated graphite tube was used as working electrode, a cylindrical piece of graphite inserted into the graphite tube as auxiliary electrode, and a micro Ag/AgCl(sat) as reference electrode. Iridium solution in 1.0 mol L-1 HCl, flowing at 0.55 mL min-1 for 60 min was used to perform the electrochemical modification. The applied potential to the flow-cell was - 0.700 V vs Ag/AgCl. Scanning electron microscopy images were taken for thermal and electrochemical modified graphite surface in order to evaluate the iridium distribution. Selenium hydride trapping was used to verify the performance of the proposed permanent chemical modifier.

Espectrofotometría de absorción atómica con tubo en la llama: aplicación en la determinación total de cadmio, plomo y zinc en aguas frescas, agua de mar y sedimentos marinos

Thermospray flame furnace Atomic Absorption Spectrometry (TS-FF-AAS) was used for the total determination of Cd, Pb and Zn in fresh water and seawater samples at µg L-1 levels, and in marine sediment samples at µg g-1 levels. Using a sample loop of 50 µL and a peristaltic pump the samples were transported into the metallic tube placed over an air/acetylene flame, through a ceramic capillary (o.d. = 3.2 mm) containing two parallel internal orifices (i.d = 0.5 mm). The detection limits determined for Cd, Pb and Zn using a synthetic water matrix (2.5% m/v NaCl, 0.5% m/v MgCl2 and 0.8% m/v CaCl2) were 0.32 µg L-1; 2.6 µg L-1 and 0.21 µg L-1 respectively. The methodology by TS-FF-AAS was validated by determination of Cd, Pb and Zn in certified reference materials of water and marine sediment, and the t-test for differences between means was applied. No statistically significant differences were established in fresh water and seawater (p>0.05), whereas differences became apparent in marine sediment (p<0.03).

Análise de fármacos em material biológico: acoplamento microextração em fase sólida "no tubo" e cromatografia líquida de alta eficiência

A new solid phase microextraction (SPME) system, known as in-tube SPME, was recently developed using an open tubular fused-silica capilary column, instead of an SPME fiber, as the SPME device. On-line in-tube SPME is usually used in combination with high performance liquid chromatography. Drugs in biological samples are directly extracted and concentrated in the stationary phase of capillary columns by repeated draw/eject cycles of sample solution, and then directly transferred to the liquid chromatographic column. In-tube SPME is suitable for automation. Automated sample handling procedures not only shorten the total analysis time, but also usually provide better accuracy and precision relative to manual techniques. In-tube SPME has been demonstrated to be a very effective and highly sensitive technique to determine drugs in biological samples for various purposes such as therapeutic drug monitoring, clinical toxicology, bioavailability and pharmacokinetics.

Preconcentración y atomización con tubo en la llama para la determinación de Cadmio en agua de mar

A flow injection on-line pre-concentration system coupled to thermospray flame furnace atomic absorption spectrometry (TS-FF-AAS) for cadmium determination at sub μg L-1 levels in seawater samples was developed. The on-line system was evaluated by analysing cadmium containing in a synthetic seawater matrix (2.5% m/v NaCl, 0.5% m/v MgCl2 and 0.8% m/v CaCl2). A sample volume of 2 mL allows determining Cd with a detection limits of 30 ng L-1 (3* σblank/slope), pre-concentration factor of 34 and repeatability of 1,8% (calculated as RSD, N=8 and containing 200 ng L-1 of Cd ).

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.