Flow injection amperometric determination of procaine in pharmaceutical formulation using a screen-printed carbon electrode

A rapid and simple method for procaine determination was developed by flow injection analysis (FIA) using a screen-printed carbon electrode (SPCE) as amperometric detector. The present method is based on the amine/hydroxylamine oxidation from procaine monitored at 0.80 V on SPCE in sodium acetate solution pH 6.0. Using the best experimental conditions assigned as: pH 6.0, flow rate of 3.8 mL min(-1), sample volume of 100 mu L and analytical path of 30 cm it is possible to construct a linear calibration curve from 9.0 x 10(-6) to 1.0 x 10(-4) mol L-1. The relative standard deviation for 5.0 x 10(-5) mol L-1 procaine (15 repetitions using the same electrode) is 3.2% and detection limit calculated is 6.0 x 10(-6) mol L-1. Recoveries obtained for procaine gave a mean values from 94.8 to 102.3% and an analytical frequency of 36 injections per hour was achieved. The method was successfully applied for the determination of procaine in pharmaceutical formulation without any pre-treatment, which are in good accordance with the declared values of manufacturer and an official method based on spectrophotometric analysis. (c) 2006 Elsevier B.V. All rights reserved.

An adaptive chart for monitoring the process mean and variance

Traditionally, an (X) over bar chart is used to control the process mean and an R chart is used to control the process variance. However, these charts are not sensitive to small changes in the process parameters. The adaptive ($) over bar and R charts might be considered if the aim is to detect small disturbances. Due to the statistical character of the joint (X) over bar and R charts with fixed or adaptive parameters, they are not reliable in identifing the nature of the disturbance, whether it is one that shifts the process mean, increases the process variance, or leads to a combination of both effects. In practice, the speed with which the control charts detect process changes may be more important than their ability in identifying the nature of the change. Under these circumstances, it seems to be advantageous to consider a single chart, based on only one statistic, to simultaneously monitor the process mean and variance. In this paper, we propose the adaptive non-central chi-square statistic chart. This new chart is more effective than the adaptive (X) over bar and R charts in detecting disturbances that shift the process mean, increase the process variance, or lead to a combination of both effects. Copyright (c) 2006 John Wiley & Sons, Ltd.

Synthetic control chart for monitoring the pprocess mean and variance

In this article, we consider the synthetic control chart with two-stage sampling (SyTS chart) to control the process mean and variance. During the first stage, one item of the sample is inspected; if its value X, is close to the target value of the process mean, then the sampling is interrupted. Otherwise, the sampling goes on to the second stage, where the remaining items are inspected and the statistic T = Sigma [x(i) - mu(0) + xi sigma(0)](2) is computed taking into account all items of the sample. The design parameter is function of X-1. When the statistic T is larger than a specified value, the sample is classified as nonconforming. According to the synthetic procedure, the signal is based on Conforming Run Length (CRL). The CRL is the number of samples taken from the process since the previous nonconforming sample until the occurrence of the next nonconforming sample. If the CRL is sufficiently small, then a signal is generated. A comparative study shows that the SyTS chart and the joint X and S charts with double sampling are very similar in performance. However, from the practical viewpoint, the SyTS chart is more convenient to administer than the joint charts.

X charts with supplementary samples to control the mean and variance

A standard X chart for controlling a process takes regular individual observations, for instance every half hour. This article proposes a modification of the X chart that allows one to take supplementary samples. The supplementary sample is taken (and the (X) over bar and R values computed) when the current value of X falls outside the control limits. With the supplementary sample, the signal of out-of-control is given by an (X) over bar value outside the (X) over bar chart's control limits or an R value outside the R chart's control limit. The proposed chart is designed to hold the supplementary sample frequency, during the in-control period, as low as 5% or less. In this context, the practitioner might prefer to verify an out-of-control condition by simply comparing the (X) over bar and R values with the control limits. In other words, without plotting the (X) over bar and R points. The X chart with supplementary samples has two major advantages when compared with the standard (X) over bar and A charts: (a) the user will be plotting X values instead of (X) over bar and R values; (b) the shifts in the process mean and/or changes in the process variance are detected faster.

Effect of a pilot washing system on dicofol levels in orange matrix

An efficient analytical method is described for the analysis of dicofol residues in pulp and orange peel. Samples are mixed with Celite and transferred to chromatographic columns prepacked with silica gel. Dicofol is eluted with ethyl acetate, and the extracts are analyzed by gas chromatography with electron capture detection. Mean recoveries for dicofol at levels of 0.5, 2.0, 5.0, and 10 mg/kg ranged from 87 to 95% with relative standard deviation values between 2.6 and 9.0%. To investigate the effect of a pilot washing system on dicofol residues in oranges, the analytical procedure was applied to samples submitted to different treatments with commercial formulations under field and laboratory conditions. The orange samples with and without washing were analyzed in duplicate, and the results indicated that washing under the described conditions did not allow a complete removal of dicofol residues from orange peel.

Uroflow studies in children: Correlation with age, weight and body surface area.

Peak flow rate data (Q(max)) are important for diagnosis of lower urinary tract obstruction. However, the lack of uroflowmetry studies in children, makes their interpretation difficult. With this aim, we studied 167 boys classified in four groups according to their age (G1 : n = 48, 6-7 years; G2 : n = 43, 8-9 years; G3 : n = 37, 10-11 years; G4: n = 39, 12-14 years). We studied in all children, weight, height, body surface area, peak flow rate (Q(max) and the correspondant urinary volume (Vol). Means and standard deviations of Q(max) (ml/sec) were : 15 +/- 5 (G1), 15 +/- 5 (G2), 17 +/- 5 (G3) and 22 +/- 7 (G4) respectively. Corresponding urinary volumes (Vol) (mean standard deviation - in ml) were : G1 = 123 +/- 75; G2 = 122 +/- 79; G3 = 158 +/- 96 and G4 = 162 +/- 101. We found a significant correlation (p < 0,01) between Q(max) and Vol in groups G2, G3 and G4; and between Q(max) and height in groups G1 and G4.The authors demonstrated a positive correlation between maximum flow and voided volume, and an increase of Q(max) with age.