(X)over-bar charts with variable parameters

Varying the parameters of the (X) over bar chart has been explored extensively in recent years. In this paper, we extend the study of the (X) over bar chart with variable parameters to include variable action limits. The action limits establish whether the control should be relaxed or not. When the (X) over bar falls near the target, the control is relaxed so that there will be more time before the next sample and/or the next sample will be smaller than usual. When the (X) over bar falls far from the target but not in the action region, the control is tightened so that there is less time before the next sample and/or the next sample will be larger than usual. The goal is to draw the action limits wider than usual when the control is relaxed and narrower than usual when the control is tightened. This new feature then makes the (X) over bar chart more powerful than the CUSUM scheme in detecting shifts in the process mean.

Differential pulse polarographic determination of ceftazidime in urine samples with and without prior extraction

Ceftazidime shows two main polarographic reduction peaks at pH 4.0, that at -0.45 V owing to reduction of the C=N bond in the methylethoxyimino group and that at -1.00 V owing to the reductive elimination of pyridine: the first peak is particularly suitable for the determination of ceftazidime. Ceftazidime can also be determined indirectly using the tensammetric peak at -0.60 V (in Britton-Robinson buffer pH 9.5) of pyridine liberated on hydrolysis. Ceftazidime can be determined in urine using the direct method only after Cls solid phase extraction, but it can be determined directly in the urine by hydrolysing it and using the pyridine peak. (C) 1997 Elsevier B.V. B.V.

Joint conomic design of (x)over-bar and R charts under Weibull shock models

This paper deals with the joint economic design of (x) over bar and R charts when the occurrence times of assignable causes follow Weibull distributions with increasing failure rates. The variable quality characteristic is assumed to be normally distributed and the process is subject to two independent assignable causes (such as tool wear-out, overheating, or vibration). One cause changes the process mean and the other changes the process variance. However, the occurrence of one kind of assignable cause does not preclude the occurrence of the other. A cost model is developed and a non-uniform sampling interval scheme is adopted. A two-step search procedure is employed to determine the optimum design parameters. Finally, a sensitivity analysis of the model is conducted, and the cost savings associated with the use of non-uniform sampling intervals instead of constant sampling intervals are evaluated.

Pectinolytic enzyme production by Bacillus species and their potential application on juice extraction

Five Bacillus strains isolated from decaying vegetable material were cultivated on wheat bran and endo-polygalacturonases, exo-polygalacturonase and pectin lyase activities in the crude enzymatic solution obtained were determined. Highest activity was observed for all enzymes when fermentation was carried out at 28 degreesC, the highest activity values were obtained after 120 h of cultivation for exo-PG and after 48 h for endo-PG and PL. The use of the enzymatic solution for treatment of fruits and vegetable mash afforded a high juice extraction and a pulp with good pressing characteristics.

Effects of extraction methods on yam (Dioscorea alata) starch characteristics

Starch extraction from roots and tubers uses grating with water and sieves to separate the starch slurry from residual mass. The starch is recovered by decantation or centrifugation. The yam starch extraction is difficult due to high viscosity of the slurry caused by non-starch polysaccharides (NSP). The establishment of an efficient extraction process may turn yam into a competitive raw material. In this paper Dioscorea alata starch extracted by four methods was characterized in order to establish the impact of treatments. When the tubers were digested with an aqueous oxalic acid/ammonium oxalate (OA/AO) 1/1 solution, it was easier to separate the starch slurry from residual mass, because viscosity was reduced. For all the others methods tested, the viscosity remained almost the same. The nitrogen present in yam tubers was removed during the different extractions to a different extent. The largest nitrogen reduction was observed with ONAO followed by the control (water). The spectrum of starch granules sizes obtained also varied according to the treatment. Results proved that NSP carries small starch granules over to the waste water. The smaller starch granules diameter varied from 1.9 mu m (OA/AO extraction) to 13.5 mu m (water and pectinase extractions). The larger diameter varied from 41.0 mu m (NaOH treatment) to 67.7 mu m (ONAO). All starches extracted showed a RVA behavior in agreement with literature for yam starch, but with small differences due to the influence of methods. ONAO extraction showed the best recovery (18 g of starch/100 g tuber yam) and granular variation but it interfered with the rheological behavior of starch.

Joint (X)over-bar and R charts with two-stage samplings

When joint (X) over bar and R charts are in use, samples of fixed size are regularly taken from the process, and their means and ranges are plotted on the (X) over bar and R charts, respectively. In this article, joint (X) over bar and R charts have been used for monitoring continuous production processes. The sampling is performed, in two stages. During the first stage, one item of the sample is inspected and, depending on the result, the sampling is interrupted if the process is found to be in control; otherwise, it goes on to the second stage, where the remaining sample items are inspected. The two-stage sampling procedure speeds up the detection of process disturbances. The proposed joint (X) over bar and R charts are easier to administer and are more efficient than the joint (X) over bar and R charts with variable sample size where the quality characteristic of interest can be evaluated either by attribute or variable. Copyright (C) 2004 John Wiley Sons, Ltd.

Economic design of two-stage (X)over-bar charts: the Markov chain approach

When the (X) over bar chart is in use, samples are regularly taken from the process, and their means are plotted on the chart. In some cases, it is too expensive to obtain the X values, but not the values of a correlated variable Y. This paper presents a model for the economic design of a two-stage control chart, that is. a control chart based on both performance (X) and surrogate (Y) variables. The process is monitored by the surrogate variable until it signals an out-of-control behavior, and then a switch is made to the (X) over bar chart. The (X) over bar chart is built with central, warning. and action regions. If an X sample mean falls in the central region, the process surveillance returns to the (Y) over bar chart. Otherwise. The process remains under the (X) over bar chart's surveillance until an (X) over bar sample mean falls outside the control limits. The search for an assignable cause is undertaken when the performance variable signals an out-of-control behavior. In this way, the two variables, are used in an alternating fashion. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A study is performed to examine the economic advantages of using performance and surrogate variables. (C) 2003 Elsevier B.V. All rights reserved.

(X)OVER-BAR CHARTS WITH VARIABLE SAMPLE-SIZE

The usual practice in using a control chart to monitor a process is to take samples of size n from the process every h hours This article considers the properties of the XBAR chart when the size of each sample depends on what is observed in the preceding sample. The idea is that the sample should be large if the sample point of the preceding sample is close to but not actually outside the control limits and small if the sample point is close to the target. The properties of the variable sample size (VSS) XBAR chart are obtained using Markov chains. The VSS XBAR chart is substantially quicker than the traditional XBAR chart in detecting moderate shifts in the process.

EFFECT OF THE HELIUM-NEON LASER ON THE HEALING OF EXTRACTION WOUNDS - A HISTOLOGICAL STUDY IN RATS

A histological study on the healing of extraction wounds following laser irradiation using a He-Ne laser, was carried out in rats. The results suggest that this mode of treatment has a beneficial effect on initial bony wound healing. Proliferation of fibroblasts and formation of trabecular osteoid tissue were found to be more prominent within the irradiated group. The deposition of led in the newly formed bone suggests swifter ossification within the irradiated group.

VSSI (X)over-bar charts with sampling at fixed times

A standard (X) over bar chart for controlling the process mean takes samples of size no at specified, equally-spaced, fixed-time points. This article proposes a modification of the standard (X) over bar chart that allows one to take additional samples, bigger than no, between these fixed times. The additional samples are taken from the process when there is evidence that the process mean moved from target. Following the notation proposed by Reynolds (1996a) and Costs (1997) we shortly call the proposed (X) over bar chart as VSSIFT (X) over bar chart: where VSSIFT means variable sample size and sampling intervals with fixed times. The (X) over bar chart with the VSSIFT feature is easier to be administered than a standard VSSI (X) over bar chart that is not constrained to sample at the specified fixed times. The performances of the charts in detecting process mean shifts are comparable.

Economic design of (X)over-bar charts with variable parameters: the Markov chain approach

This paper presents an economic design of (X) over bar control charts with variable sample sizes, variable sampling intervals, and variable control limits. The sample size n, the sampling interval h, and the control limit coefficient k vary between minimum and maximum values, tightening or relaxing the control. The control is relaxed when an (X) over bar value falls close to the target and is tightened when an (X) over bar value falls far from the target. A cost model is constructed that involves the cost of false alarms, the cost of finding and eliminating the assignable cause, the cost associated with production in an out-of-control state, and the cost of sampling and testing. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A comprehensive study is performed to examine the economic advantages of varying the (X) over bar chart parameters.