Cesar Mello

Simultaneous determination of phenol isomers in binary mixtures by differential pulse voltammetry using carbon fibre electrode and neural network with pruning as a multivariate calibration tool

Neural networks with pruning were applied to model overlapped peaks obtained in differential pulse voltammetry (DPV) with modified carbon fibre electrode with TiO 2 of binary mixtures of catechol and hydroquinone. The best condition for electrochemical response was obtained with 0.05 mol l 1 Tris‐HCl buffer at pH 6.0 and T-800 sized carbon fibre electrode. Initially the voltammograms were processed using Fourier transform filter and principal component analysis (PCA) to noise reduction and data compression, respectively. The scores of these principal components were the input into the neural network and the optimal brain surgeon (OBS) was the procedure employed for pruning the neural network. The results obtained with pruning procedure were slightly better in relation to hydroquinone in comparison to the PLS1 and PLS2. However, the similar errors were obtained to catechol when using PLS or neural networks models. Using neural networks with pruning was possible to determine catechol and hydroquinone by DPV using carbon fibre electrode, in concentration range of 1.0 10 4 up to 6.010 4 mol l 1 with root mean square errors of predictions (%RMSEP) of 7.42 and 8.02, respectively. The good results show that the proposed methodology is a good alternative to simultaneous determination of catechol and hydroquinone in binary mixtures.

Simultaneous determination of lead and sulfur by energy-dispersive x-ray spectrometry. Comparison between artificial neural networks and other multivariate calibration methods

The need for mathematical methods to model data in energy-dispersive x-ray fluorescence (EDXRF) spectrometry is common owing to the overlapping of intense spectral lines in complex samples. This overlapping generally produces a large amount of scatter in the analytical curve, preventing simultaneous direct determinations of some elements without data treatment. This work demonstrates the performance of artificial neural networks (ANN) and other methods of multivariate calibration (linear or not) for the simultaneous determination of sulfur and lead, when overlapping of the sulfur Kα spectral line (2.308 keV) and the lead Mα line (2.346 keV) is observed. The performance of neural networks was compared by the f-test with five other data treatment methods: PLS (partial least squares), POLYPLS (polynomial partial least squares), NNPLS (partial least square neural networks), LR (linear regression) and CI (corrected intensity). It was verified that the ANN produces better predictions than the other methods, for both sulfur and lead, allowing their simultaneous determination in solid samples with good accuracy. Copyright

Redes neurais e suas aplicações em calibração multivariada

Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.

Óxido misto de ítrio-alumínio dopado com Eu(III)

In this work, we report the synthesis and the photoluminescence features of Eu(III)-doped yttrium-aluminium oxide obtained by non-hydrolytic sol-gel routes. After heating the powders above 600 oC the XRD patterns show the presence of the Y4Al2O9 (YAM) and Y3Al5O12 (YAG) phases. At 800 and at 1500 oC the PL spectra display the Eu(III) lines characteristic of the YAM monoclinic phase. The 5D0®7F2 transition is favored relatively to the 5D0®7F1 lines. However, at 1100 oC the cubic YAG is the preferential phase and the 5D0®7F1 transition dominates the spectrum. The Eu(III) ions lie in a centrosymmetrical site. The different solvents used in the sol-gel synthesis also change the relative proportion between these two phases. This is monitored analyzing the modifications in the relative intensity between the 5D0®7F2 and the 5D0®7F1 transitions.

Neuro-genetic approach for optimisation of the spectrophotometric catalytic determination of cobalt

A neuro-genetic approach was developed to optimise an automated procedure for the spectrophotometric determination of cobalt in bovine liver, fish and mussel samples by using a monosegmented flow system. The method exploited the Co(II) catalysed oxidation of Tiron by hydrogen peroxide. The values related to reagent concentrations and mean residence time were optimised in terms of slope and correlation coefficient of the calibration curve. The neural networks were used to model the system, relating the parameters of the monosegmented flow system with those of the calibration curve. The genetic algorithm was applied to attain the optimum values from the model developed by the neural networks. The detection and quantification limits were 1.66 and 5.33 ng l 1 Co, respectively.

Microstructures formation in a seemingly ideal homogeneous mixture of ethanol and methanol: An experimental evidence and two-dimensional correlation spectroscopy approach

An anomalous solution behavior at the molecular scale was observed for macroscopically homogeneous mixtures of methanol and ethanol. Two-dimensional Raman correlation spectroscopy was used to elucidate the possible existence of microstructures formed in the mixture. The result suggests that separate methanol and ethanol clusters are formed without heterohydrogen bonding between different alcohol species. Supramolecular structures seem to be formed by the interaction of such clusters with each other through cohesion and dispersion forces, but not through direct hydrogen bonding connections.

Utilização de filtro de transformada de fourier para a minimização de ruídos em sinais analíticos

Instrumental data always present some noise. The analytical data information and instrumental noise generally has different frequencies. Thus is possible to remove the noise using a digital filter based on Fourier transform and inverse Fourier transform. This procedure enhance the signal/noise ratio and consecutively increase the detection limits on instrumental analysis. The basic principle of Fourier transform filter with modifications implemented to improve its performance is presented. A numerical example, as well as a real voltammetric example are showed to demonstrate the Fourier transform filter implementation. The programs to perform the Fourier transform filter, in Matlab and Visual Basic languages, are included as appendices

Experimental evidence of the chaotic regime in a salicylate biosensor

Abstract In a biosensor for salicylate, developed using a modified carbon paste electrode with silica gel coated with Meldolas Blue and salicylate hydroxylase, the current time series presented a complex oscillatory pattern, for salicylate and NADH concentrations close to 2.0 × 10−5 and 3.6 × 10−5 mol 1−1, respectively. These experimental results suggest that the biosensor response presents chaotic characteristics. The complex temporal behavior of these series was diagnosed as deterministic chaos by measurement of the Grassberger-Procaccia dimension, and the calculation of the first Lyapunov exponent. The value determined for the correlational dimension is lower than 5. These results suggest a coupling of five or six differential equations to describe the redox reactions of the biosensor system.

Fast differentiation of bacteria causing pharyngitis by low resolution Raman spectroscopy and PLS-discriminant analysis

The diagnosis of the bacteria that cause pharyngitis through classical microbiological methods is a difficult, but usually very efficient task. However, the high cost of reagents and the time required for such identifications, about four days, could generate serious consequences, mainly when the patients concerned are children, the elderly or adults with low resistance. Thus, the search for new methods allowing a fast and reagentless differentiation of these microorganisms is extremely relevant. In this work, the main microorganisms responsible for pharyngitis, S. aureus, S. pyogenes and N. gonorrhoeae were studied. For each microorganism, sixty different dispersions were prepared using physiological solution as solvent. The Raman Spectra of these dispersions were recorded using a diode laser operating in the near infrared region. The PLS-discriminant analysis method was applied to bacteria differentiation through their respective Raman Spectra. This approach enabled the correct classification of 100% of all evaluated bacteria and unknown samples coming from clinical environment, in a reduced time interval (ca. 10 h), by using a low-cost, portable Raman spectrometer, which can be easily used in Intensive Care Units (ICU) and clinical environments.

Thermoanalysis of soybean oil extracted by two methods

The thermal stability of vegetable oils is an important factor that affects their quality. In this study, we investigated the thermal stability of oil and lecithin extracted from soybeans by two distinct processes: mechanical extraction (pressing) and physical extraction (solvent). Thermal analysis was used to obtain information about different methodologies of extraction. The physically extracted products proved more stable than those extracted mechanically. Raman and UV-Vis techniques were applied to underpin the discussion of process differences.