Fluoxetine and norfluoxetine analysis by direct injection of human plasma in a column switching liquid chromatographic system

A column switching LC method is presented for the analysis of fluoxetine (FLU) and norfluoxetine (NFLU) by direct injection of human plasma using a lab-made restricted access media (RAM) column. A RAM-BSA-octadecyl silica (C-18) column (40 min x 4.6 mm, 10 mu m) is evaluated in both backflush and foreflush elution modes and coupled with a C-18 lab-made (50 mm x 4.6 mm, 3 pm) analytical column in order to perform online sample preparation. Direct injection of 100 mu L, of plasma samples is possible with the developed approach. In addition, reduction of sample handling is obtained when compared with traditional liquid-liquid extraction (LLE) and SPE. The total analysis time is around 20 min. A LOQ of 15 ng/mL is achieved in a concentration range of 15-500 ng/mL, allowing the therapeutic drug monitoring of clinical samples. The precision values achieved are lower than 15% for all the evaluated points with adequate recovery and accuracy. Furthermore, no matrix interferences are found in the analysis and the proposed method shows to be an adequate alternative for analysis of FLU in plasma.

Determination of Minoxidil by Bleaching the Permanganate Carrier Solution in a Flow-Based Spectrophotometric System

The determination of minoxidil (MX) with potassium permanganate as a carrier in a flow injection method is described. The detection at 550nm was linear from 1.0x10-5 to 5.0x10-4mol L-1. The limit of detection (3 sigma/slope) was 8.92x10-6mol L-1, with an analytical frequency of 32h-1. The proposed method was applied to commercial samples, with recoveries from 104.7 to 106.4%. Comparison with the HPLC procedure reveled relative errors from 0.48 to 1.4%, and the results agreed within a 95% confidence level.

Development and Application of a Highly Selective Biomimetic Sensor for Detection of Captopril, an Important Ally in Hypertension Control

A biomimetic sensor is proposed as a promising new analytical method for determination of captopril in different classes of samples. The sensor was prepared by modifying a carbon paste electrode with iron (II) phthalocyanine bis(pyridine) [FePe(dipy)] complex. Amperometric measurements in a batch analytical mode were first carried out in order to optimize the sensor response. An applied potential lower than 0.2 V vs Ag vertical bar AgCl in 0.1 mol L(-1) of TRIS buffer at pH 8.0 provided the best response, with a linear range of 2.5 x 10(-5) to 1.7 x 10(-4) mol L(-1). A detailed investigation of the selectivity of the sensor, employing seventeen other drugs, was also performed. Recovery studies were carried out using biological and environment samples in order to evaluate the sensor`s potential for use with these sample classes. Finally, the performance of the biomimetic sensor was optimized in a flow injection (FIA) system using a wall jet electrochemical cell. Under optimized flow conditions, a broad linear response range, from 5.0 x 10(-4) to 2.5 x 10(-2) mol L(-1), was obtained for captopril, with a sensitivity of 210 +/- 1 mu A L mol(-1).

Analytical Assessment of a Home Made Capillary Electrophoresis Equipment with Linear Charge Coupled Device for Visible Light Absorption Detection in the Determination of Food Dyes

The performance of modular home made capillary electrophoresis equipment with spectrophotometric detection, at a visible region by means of a miniaturized linear charge coupled device, was evaluated for the determination of four food dyes. This system presents a simple but efficient home made cell detection scheme. A computer program that converts the spectral data after each run into the electropherograms was developed to evaluate the analytical parameters. The dyes selected for analytical evaluation of the system were Brilliant Blue FCF, Fast Green FCF, Sunset Yellow FCF, and Amaranth. Separation was carried out in a 29cm length and 75 mu m I.D fused silica capillary, using 10mmolL-1 borate buffer at pH 9, with separation voltage of 7.5kV. The detection limits for the dyes were between 0.3 and 1.5mgL-1 and the method presented adequate linearity over the ranges studied, with correlation coefficients greater than 0.99. The method was applied for determination and quantification of these dyes in fruit juices and candies.

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

Motion Detection for Video Surveillance

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.

In vitro culture, PCR , and nested PCR for the detection of Theileria equi in horses submitted to exercise

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

Surface-enhanced resonance Raman scattering: Single-molecule detection in a Langmuir-Blodgett monolayer

Surface-enhanced resonance Raman scattering (SERRS) is used for single-molecule detection from spatially resolved 1-mum(2) sections of a Langmuir-Blodgett (LB) monolayer deposited onto a Ag film. The target molecule, his (benzimidazo) thioperylene (BZP), is dispersed in an arachidic acid monomolecular layer containing one BZP molecule per mum(2) which is also the probing area of the Raman microscope. For concentrated samples (attomole quantities in the field of view), average SERRS, surface-enhanced fluorescence (SEF), and Raman imaging, including line mapping and global images at different temperatures, were recorded. Single-molecule SERRS spectra, obtained using an LB monolayer, present changes in bandwidth and relative intensities, highlighting the properties of single-molecule SERRS that are lost in average SERRS measurements of mixed LB monolayers obtained at the same temperatures. Also, the dilute system phenomenon of blinking is discussed with regard to results obtained from LB monolayers. The dilution process used in the single-molecule LB SERRS work is independently supported by fluorescence results obtained from very dilute solutions with monomer concentrations down to 10(-12) M.

An electrochemical sensor for (L)-dopa based on oxovanadium-salen thin film electrode applied flow injection system

An oxovanadium-salen complex (NAP-ethylene-bis(salicylidenciminato) oxovanadium) thin film deposited on a graphite-polyurethane electrode was investigated with regard to its potential use for detection of L-dopa in flow injection system. The oxovanadium(IV)/oxovanadium(V) redox couple of the modified electrode was found to mediate the L-dopa oxidation before its use in the FIA system. Experimental parameters, such as pH of the carrier solution, flow rate, sample volume injection and probable interferents were investigated. Under the optimized FIA conditions, the amperometric signal was linearly dependent on the L-dopa concentration over the range 1.0 x 10(-1) to 1.0 x 10(-4) mol L-1 (I-anodic, mu A) = 0.01 + 0.25 [L-dopa mu mol L-1]) with a detection limit (S/N = 3) of 8.0 x 10(-7) mol L-1 and a sampling frequency of 90 h(-1) was achieved. For a concentration of 1.0 x 10(-5) mol L-1 L-dopa, the R.S.D. of nine consecutive measurements was 3.7%. (c) 2006 Elsevier B.V. All rights reserved.

Spray layer-by-layer films based on phospholipid vesicles aiming sensing application via e-tongue system

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

Toward the Optimization of an e-Tongue System Using Information Visualization: A Case Study with Perylene Tetracarboxylic Derivative Films in the Sensing Units

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

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

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

Real-time multi-sensors measurement system with temperature effects compensation for impedance-based structural health monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.