Square wave adsorptive cathodic stripping voltarnmetry automated by sequential injection analysis - Potentialities and limitations exemplified by the determination of methyl parathion in water samples

This paper describes the development and evaluation of a sequential injection method to automate the determination of methyl parathion by square wave adsorptive cathodic stripping voltammetry exploiting the concept of monosegmented flow analysis to perform in-line sample conditioning and standard addition. Accumulation and stripping steps are made in the sample medium conditioned with 40 mmol L-1 Britton-Robinson buffer (pH 10) in 0.25 mol L-1 NaNO3. The homogenized mixture is injected at a flow rate of 10 mu Ls(-1) toward the flow cell, which is adapted to the capillary of a hanging drop mercury electrode. After a suitable deposition time, the flow is stopped and the potential is scanned from -0.3 to -1.0 V versus Ag/AgCl at frequency of 250 Hz and pulse height of 25 mV The linear dynamic range is observed for methyl parathion concentrations between 0.010 and 0.50 mgL(-1), with detection and quantification limits of 2 and 7 mu gL(-1), respectively. The sampling throughput is 25 h(-1) if the in line standard addition and sample conditioning protocols are followed, but this frequency can be increased up to 61 h(-1) if the sample is conditioned off-line and quantified using an external calibration curve. The method was applied for determination of methyl parathion in spiked water samples and the accuracy was evaluated either by comparison to high performance liquid chromatography with UV detection, or by the recovery percentages. Although no evidences of statistically significant differences were observed between the expected and obtained concentrations, because of the susceptibility of the method to interference by other pesticides (e.g., parathion, dichlorvos) and natural organic matter (e.g., fulvic and humic acids), isolation of the analyte may be required when more complex sample matrices are encountered. (C) 2007 Elsevier B.V. All rights reserved.

Development of a spectrophotometric Sequential Injection Analysis (SIA) procedure for determination of ammonium: A Response Surface Methodology (RSM) approach

This paper describes the optimization and use of a Sequential Injection Analysis (SIA) procedure for ammonium determination in waters. Response Surface Methodology (RSM) was used as a tool for optimization of a procedure based on the modified Berthelot reaction. The SIA system was designed to (i) prepare the reaction media by injecting an air-segmented zone containing the reagents in a mixing chamber, (ii) to aspirate the mixture back to the holding coil after homogenization, (iii) drive it to a thermostated reaction coil, where the flow is stopped for a previously established time, and (iv) to pump the mixture toward the detector flow cell for the spectrophotometric measurements. Using a 100 mu mol L(-1) ammonium solution, the following factors were considered for optimization: reaction temperature (25 - 45 degrees C), reaction time (30 - 90 s), hypochlorite concentration (20 - 40 mmol L(-1)) nitroprusside concentration (10 - 40 mmol L(-1)) and salicylate concentration (0.1 - 0.3 mol L(-1)). The proposed system fed the statistical program with absorbance data for fast construction of response surface plots. After optimization of the method, figures of merit were evaluated, as well as the ammonium concentration in some water samples. No evidence of statistical difference was observed in the results obtained by the proposed method in comparison to those obtained by a reference method based on the phenol reaction. (C) 2010 Elsevier B.V. All rights reserved.

Sequential injection analysis system with spectrophotometric detection for determination of norfloxacin and ciprofloxacin in pharmaceutical formulations

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

A flow-injection-ICP system sequential multielemental analysis with simultaneously mercury(II) preconcentration step

A flow-injection system for multielemental analysis with a mercury(II) preconcentration step using a resin Chelite-S(R)(Serva Feinbiochemica Heidelberg, Part No. 41709) packed minicolumn by inductively coupled plasma atomic emission spectroscopy is described. A mercury reductive elution procedure with a mixture of SnCl2/HCl was used, which allows use of 6 mol/L HCl solution instead of concentrated hydrochoric acid. The main parameters related to ICP operation, such as radio frequency power (950-1750 W), auxiliary argon flow (0.0-1.5 L/min) and spray chamber nebulizer pressure (15-35 psi), were studied. Optimization of the FIA system was reached by defining the best eluent carrier stream (1.4-2.8 mL/min), Hgdegrees carrier stream (10-40 mL min(-1)), loading time (0.5-4.0 min), sample flow rate (1.25-10.0 mL/min), temperature of reactor gas liquid separator (GLS) (25-75 degreesC) and eluent volume (50-350 muL). Throughput is around 30 samples per hour for analytical solutions within the range 50-2500 ng Hg(II)/L. Results from certified material showed good precision (RSD < 3%, n = 12) and no statistical difference was observed for real samples analyzed by AAS and by the proposed system.

Sequential spectrophotometric determination of chromium(III) and chromium(VI) using flow injection analysis

A flow injection analysis (FIA) procedure for the speciation of Cr(III) and Cr(VI) using the 1,5-diphenylcarbazide (DPC) method is presented. As Cr(III) does not interfere in the Cr(VI) - DPC reaction, both Cr(VI) and total chromium [after the on-line oxidation of Cr(III) by Ce(IV)] are sequentially determined. Cr(III) is obtained by difference. Under the experimental conditions described, the calibration graphs are linear up to 2 μg mh1 of Cr(VI) and 4 μg ml-1 of Cr(III). The detection limits found were 18 ng ml -1 for Cr(VI) and 55 ng ml-1 for Cr(III), at a signal to noise ratio of 3. The common interfering elements in the Cr(VI) - DPC reaction were investigated under dynamic FIA conditions. The FIA method was also compared with the conventional spectrophotometric procedure.

Common architecture for discrete wavelet transform analysis and synthesis with sequential and constant processing elements

This paper adresses the problem on processing biological data such as cardiac beats, audio and ultrasonic range, calculating wavelet coefficients in real time, with processor clock running at frequency of present ASIC's and FPGA. The Paralell Filter Architecture for DWT has been improved, calculating wavelet coefficients in real time with hardware reduced to 60%. The new architecture, which also processes IDWT, is implemented with the Radix-2 or the Booth-Wallace Constant multipliers. Including series memory register banks, one integrated circuit Signal Analyzer, ultrasonic range, is presented.

Multi-scale structural and chemical analysis of sugarcane bagasse in the process of sequential acid-base pretreatment and ethanol production by Scheffersomyces shehatae and Saccharomyces cerevisiae

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

Deadlock analysis of asynchronous sequential processes

In this thesis we present ad study an object-oriented language, characterized by two different types of objects, passive and active objects, of which we define the operational syntax and semantics. For this language we also define the type system, that will be used for the type checking and for the extraction of behavioral types, which are an abstract description of the behavior of the methods, used in deadlock analysis. Programs can manifest deadlock due to the errors of the programmer. To statically identify possible unintended behaviors we studied and implemented a technique for the analysis of deadlock based on behavioral types.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Sequential and coordinated action of phytochromes A and B during Arabidopsis stem growth revealed by kinetic analysis

Photoreceptor proteins of the phytochrome family mediate light-induced inhibition of stem (hypocotyl) elongation during the development of photoautotrophy in seedlings. Analyses of overt mutant phenotypes have established the importance of phytochromes A and B (phyA and phyB) in this developmental process, but kinetic information that would augment emerging molecular models of phytochrome signal transduction is absent. We have addressed this deficiency by genetically dissecting phytochrome-response kinetics, after having solved the technical issues that previously limited growth studies of small Arabidopsis seedlings. We show here, with resolution on the order of minutes, that phyA initiated hypocotyl growth inhibition upon the onset of continuous red light. This primary contribution of phyA began to decrease after 3 hr of irradiation, the same time at which immunochemically detectable phyA disappeared and an exclusively phyB-dependent phase of inhibition began. The sequential and coordinated actions of phyA and phyB in red light were not observed in far-red light, which inhibited growth persistently through an exclusively phyA-mediated pathway.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Analyzing the n→π* Electronic Transition of Formaldehyde in Water. A Sequential Monte Carlo/Time-Dependent Density Functional Theory

The n→π* absorption transition of formaldehyde in water is analyzed using combined and sequential classical Monte Carlo (MC) simulations and quantum mechanics (QM) calculations. MC simulations generate the liquid solute-solvent structures for subsequent QM calculations. Using time-dependent density functional theory in a localized set of gaussian basis functions (TD-DFT/6-311++G(d,p)) calculations are made on statistically relevant configurations to obtain the average solvatochromic shift. All results presented here use the electrostatic embedding of the solvent. The statistically converged average result obtained of 2300 cm-1 is compared to previous theoretical results available. Analysis is made of the effective dipole moment of the hydrogen-bonded shell and how it could be held responsible for the polarization of the solvent molecules in the outer solvation shells.

Determination of picloram in waters by sequential injection chromatography with UV detection

This paper describes a sequential injection chromatography procedure for determination of picloram in waters exploring the low backpressure of a 2.5 cm long monolithic C18 column. Separation of the analyte from the matrix was achieved in less than 60 s using a mobile phase composed by 20:80 (v v-1) acetonitrile:5.0 mmol L-1 H3PO4 and flow rate of 30 μL s-1. Detection was made at 223 nm with a 40 mm optical path length cell. The limits of detection and quantification were 33 and 137 μg L-1, respectively. The proposed method is sensitive enough to monitor the maximum concentration level for picloram in drinking water (500 μg L-1). The sampling frequency is 60 analyses per hour, consuming only 300 μL of acetonitrile per analysis. The proposed methodology was applied to spiked river water samples and no statistically significant differences were observed in comparison to a conventional HPLC-UV method.