Chemiluminescence determination of antibacterial drug trimethoprim by automated sequential injection technique with permanganate and hexametaphosphate as reagents

Antibacterial drug trimethoprim [2,4-diamino-5-(3,4,5-trimethoxybenzyl)-pyrimidine] (I) was determined in pharmaceutical formulations by using a lab-made PC-controlled SIA analyser linked to conventional HPLC fluorimetric detector equipped with a chemiluminescence module. The chemical principle is the oxidation of I by KMnO(4) in acid medium; the reaction is accompanied by the emission of chemiluminescence, which is enhanced in the presence of hexametaphosphate (HMP). The optimum sequence and the flow parameters and concentrations and volumes of reagents aspirated optimised by a computer-aided simplex method were, 100 mul of 5 mM HMP, 40 mul of a test solution of I, 2 mul of 0.5 M H(2)SO(4) and 20 mul of 1 mM KMnO(4); the luminescing zone was pushed into the detector at a flow rate of 49 mul s(-1). The calibration graph relating the intensity of luminescence to concentration of I was parabolic (r=0.9994) in the range 0.5-100 mug ml(-1) of I with rectilinear part (r=0.9999) in the range 20-100 mug ml(-1) of I; the limit of detection was 0.1 mug ml(-1) of I. The method was used for the assay of Triprim(R) tablets (with nominal content 100 or 200 mg of I) for the active substance as well as for content uniformity tests; the R.S.D. values did not exceed 1% (n=5). The SIA results did not show statistical difference from those obtained by pharmacopoeial acidimetric titration in non-aqueous medium; the excipients such as microcrystalline cellulose, maze starch, povidone, talc, magnesium stearate and gelatin did not interfere.     

Neighboring extremals of dynamic optimization problems with path equality constraints

Neighboring extremals of dynamic optimization problems with path equality constraints and with an unknown parameter vector are considered in this paper. With some simplifications, the problem is reduced to solving a linear, time-varying two-point boundary-value problem with integral path equality constraints. A modified backward sweep method is used to solve this problem. Two example problems are solved to illustrate the validity and usefulness of the solution technique. This research was supported in part by the National Aeronautics and Space Administration under NASA Grant No. NCC-2-106. The author is indebted to Professor A. E. Bryson, Jr., Department of Aeronautics and Astronautics, Stanford University, for many stimulating discussions.     

Development of a Sequential Injection Analysis Device for the Determination of Total Polyphenol Index in Wine

A sequential injection analysis method is developed for the determination of the total polyphenol index in wines. The determination is based on the Folin-Ciocalteu reaction, which is monitored spectrophotometrically. Interactions between experimental variables and their optimal levels were investigated using factorial designs. The proposed system is fully computerised and is able to monitor polyphenol index in real samples of white, sweet and red wines. The calibration graph is linear from 5 to 200mg·L^–1 using Tannic acid as standard, with a detection limit of 3.2mg·L^–1. Interferences are studied. For validation purposes the proposed methodology was applied to the determination of the total polyphenol index in different types of wines and compared with earlier alternatives in order to assess their performance.     

Joint Monitoring of Several Types of Shocks in Normal Dynamic Linear Models: A Bayesian Decision Theoretic Approach

In this article we consider the sequential monitoring process in normal dynamic linear models as a Bayesian sequential decision problem. We use this approach to build a general procedure that jointly analyzes the existence of outliers, level changes, variance changes, and the development of local correlations. In addition, we study the frequentist performance of this procedure and compare it with the monitoring algorithm proposed in an earlier article.     

Sequential optimality conditions for composed convex optimization problems

Using a general approach which provides sequential optimality conditions for a general convex optimization problem, we derive necessary and sufficient optimality conditions for composed convex optimization problems. Further, we give sequential characterizations for a subgradient of the precomposition of a K-increasing lower semicontinuous convex function with a K-convex and K-epi-closed (continuous) function, where K is a nonempty convex cone. We prove that several results from the literature dealing with sequential characterizations of subgradients are obtained as particular cases of our results. We also improve the above mentioned statements.     

Effect of oxygen addition on the hydrogen production from ethanol steam reforming in a Pd–Ag membrane reactor

In this communication, a porous stainless steel (PSS) tube was electrolessly plated into Pd–Ag membrane reactor which was used for separating hydrogen produced in an ethanol steam reforming reaction with the addition of oxygen, which has not been reported before. Palladium and silver were deposited on porous stainless steel tube via the sequential electroless plating procedure with an overall film thickness of 20 μm and Pd/Ag weight ratio of 78/22. Ethanol–water mixture (n_water/n_ethanol = 1 or 3) and oxygen (n_oxygen/n_ethanol = 0.2 or 0.7) were fed concurrently into the membrane reactor packed with MDC-3. The reaction temperatures were set at 593–723 K and the pressures 3–10 atm. The effect of oxygen addition plays a vital role on the ethanol steam reforming reaction, especially for the Pd–Ag membrane reactor in which a higher flux of hydrogen is required. If oxygen in the feed is not sufficient, it would be possible that steam reforming reaction prevails. Inversely, high O₂ addition will shift the reaction scenario to be partial oxidation dominating, and selectivity of CO₂ increases with increasing oxygen feed. At high pressure, autothermal reaction of ethanol would be easily reached.     

Extreme value distribution of a recursive-type detector in a linear model

We study a CUSUM–type monitoring scheme designed to sequentially detect changes in the regression parameter of an underlying linear model. The test statistic used is based on recursive residuals. Main aim of this paper is to derive the limiting extreme value distribution under the null hypothesis of structural stability. The model assumptions are flexible enough to include rather general classes of error sequences such as augmented GARCH(1,1) processes. The result is underlined by an illustrative simulation study. Research partially supported by NSF grants DMS–0604670 and DMS–065242.     

Simulated annealing in the presence of noise

In many practical optimization problems, evaluation of a solution is subject to noise, e.g., due to stochastic simulations or measuring errors. Therefore, heuristics are needed that are capable of handling such noise. This paper first reviews the state-of-the-art in applying simulated annealing to noisy optimization problems. Then, two new algorithmic variants are proposed: an improved version of stochastic annealing that allows for arbitrary annealing schedules, and a new approach called simulated annealing in noisy environments (SANE). The latter integrates ideas from statistical sequential selection in order to reduce the number of samples required for making an acceptance decision with sufficient statistical confidence. Finally, SANE is shown to significantly outperform other state-of-the-art simulated annealing techniques on a stochastic travelling salesperson problem.     

“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study

We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models. In particular, we consider how different particle filtering methods affect the variance of the estimated likelihood. We review and compare particle Markov Chain Monte Carlo (MCMC), RMHMC, fixed-form variational Bayes, and integrated nested Laplace approximation to estimate the posterior distribution of the parameters. Additionally, we conduct the review from the point of view of whether these methods are (1) easily adaptable to different model specifications; (2) adaptable to higher dimensions of the model in a straightforward way; (3) feasible in the multivariate case. We show that when using the stochastic volatility model for methods comparison, various data-generating processes have to be considered to make a fair assessment of the methods. Finally, we present a challenging specification of the multivariate stochastic volatility model, which is rarely used to illustrate the methods but constitutes an important practical application.     

Analysis of Organophosphorous Pesticides Based on Housefly Acetylcholinesterase Using Sequential Injection Analysis

Organophosphorous pesticides were analyzed based on the inhibition of acetylcholinesterase (AChE) from housefly and the electric eel using a sequential injection analysis (SIA) system. This analytical system is fully computerized and is able to automatically monitor the pesticide residues in an assay cycle of 180 s including 30 s incubation time when the flow is stopped. The optimal operation conditions such as AChE, substrate, pH, flow rate, and stopping time were studied. The experimental setup gave good reproducibility with the linear ranges for paraoxon of 1–70 ppb and dichlorovos of 20–70 ppb. The detection limits of paraoxon and dichlorovos were 1 and 5 ppb, respectively. The AChE from housefly shows 10 times higher sensitivity than the enzyme from the eel for these pesticide determination in this system.