Multiple sampling plan

One further point in dealing with the size of sample is the choice between single, double and multiple samples. Plans have been produced which allow comparison between these options at the same AQL, and using the same example as above (i.e. a batch of 20,000, AQL 4%) (Table 4.6). 

The obvious first reason for choosing a double or multiple sample plan (and possibly the only real reason) is the opportunity to take a smaller initial sample, i.e. the ability to make a status decision using a smaller sample than if a single plan were used. This reduction in the QC effort is only achieved when the status decision can be made on the first 1 or 2 samples when a further sample is necessary, the advantage is lost. 

Sampling plans of this kind generate OC curves of the same form as those described above, although in cases of sampling by variables the. v-axis is normally plotted as the mean determinant concentration (the fluorophore level in the example above). Other aspects of OC curves, of multiple sampling plans, and of sequential sampling plans, in which samples are taken one at a time, are dealt with in more advanced texts [8]-[10]. 

Multiple Sampling Plan. A multiple sampling plan is an extension of a double sampling plan. As long as the number of defectives falls between acceptance and rejections numbers, the inspection is continued. 

It is assumed for the remainder of this entry that the same number of units is tested from each of the sampled locations (i.e., it is a balanced sampling plan). Regardless of what sampling plan is used to determine testing, multiple units are normally collected at each of the sample locations during validation to serve as contingency samples for possible later testing. 

Prepare a 100-//1 reaction for double-stranded amplification of a single specimen for perpendicular DGGE. Use the same primer combination planned for subsequent comparisons of multiple samples. One of the primers should include a GC clamp. 

However, cause-and-effect relationships in these situations are obscured by rampant variability and multiple mysterious causes. The approach is passive. Classical observational tools for industry usually include sampling plans, control charts, and process capability studies. In addition, Branning has found two of the most useful observational tools for validation and PAT are process flow charts and fishbone diagrams, which help define the process and identify the potential sources of variability. These observational tools need to be used on a routine basis to collect background data for validation and PAT. 

Type y is characteristic of a relatively precise test measurement where several samples are required to give a good estimate of lot or population means needed to calculate defined sampling program is required. Usually only a few measurements (one. two) need be made on any sample. Type 4 is the most complex, since both components are important or significant. This is unfortunately frequently encountered in much testing. A specified sampling plan with multiple samples is required as well as multiple measurements on each... 

Also, the analysis plan should identify the statistical methods that will be used and how hypotheses will be tested (e.g., a p value cutoff or a confidence interval for the difference that excludes 0). And the plan should prespecify whether interim analyses are planned, indicate how issues of multiple testing will be addressed, and predefine any subgroup analyses that will be conducted. Finally, the analysis plan should include the results of power and sample size calculations. 

Prepare a simple schematic sketch of your multiple instrument situation, as in Figure 8.1, a sample which would be adequate for preliminary planning. The sketches will be helpful in applying the options presented in this chapter or in discussing options made available by various vendors. 

In this chapter, we presented our research efforts addressing the first two issues with constrained regularization and intrinsic Raman spectroscopy, respectively. These techniques will play a critical role in prospective studies involving multiple sites/subjects/days. We are currently planning for a multiple-subject and multiple-day in vivo study, first on dogs and then on humans. We believe these new developments together with a robust sample interface will enable us to demonstrate prospective applicability. 

Requiring low-sample volume micro-scale tests for its cost-effective application, the PEEP index has thus far employed bioassays with bacteria, algae and microinvertebrates. While well-standardized toxicity tests using freshwater fish existed at the time of the PEEP s conception in the early 1990 s (e.g., the Environment Canada fingerling rainbow trout 96-h lethality test to assess industrial wastewaters), they were excluded because of their large sample volume needs (e.g., close to 400 L of effluent sample required to undertake a multiple dilution 96-h LC50 bioassay in the case of the trout test). In addition to effluent sample volume, the cost of carrying out salmonid fish acute lethality bioassays for the 50 priority industrial effluents identified under SLAP I (the first 1988-93 Saint-Lawrence River Action Plan) was prohibitive. 

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