Process control sampling errors

Comprehension of eight sampling errors and the simple set of seven SUOs will allow all process sampling situations to be properly analysed and appropriate solutions (principles, design, equipment, implementation, operation, quality control) to be derived. 

As mentioned earlier, the complete analytical process involves sampling, sample preservation, sample preparation, and finally, analysis. The purpose of quality assurance (QA) and quality control (QC) is to monitor, measure, and keep the systematic and random errors under control. QA/QC measures are necessary during sampling, sample preparation, and analysis. It has been stated that sample preparation is usually the major source of variability in a measurement process. Consequently, the QA/QC during this step is of utmost importance. The discussion here centers on QC during sample preparation. 

Analytical measurements should be made with properly tested and documented procedures. These procedures should utilise controls and calibration steps to minimise random and systematic errors. There are basically two types of controls (a) those used to determine whether or not an analytical procedure is in statistical control, and (b) those used to determine whether or not an analyte of interest is present in a studied population but not in a similar control population. The purpose of calibration is to minimise bias in the measurement process. Calibration or standardisation critically depends upon the quality of the chemicals in the standard solutions and the care exercised in their preparation. Another important factor is the stability of these standards once they are prepared. Calibration check standards should be freshly prepared frequently, depending on their stability (Keith, 1991). No data should be reported beyond the range of calibration of the methodology. Appropriate quality control samples and experiments must be included to verify that interferences are not present with the analytes of interest, or, if they are, that they be removed or accommodated. 

The G-BASE project collects samples in random number order (Plant, 1973), as this helps identify any correctable systematic errors introduced during sample preparation and analysis, processes in which the samples are handled in numeric order. For every block of one hundred numbers, five numbers are reserved for control samples so when they are submitted within a batch of samples they are blind to the analyst. The control samples inserted are one duplicate sample, two replicate samples, two blanks, and two secondary reference materials (SRM) used to monitor accuracy and precision as well as to level data between different field campaigns (see Johnson et al, 2008). Along with the original sample ofthe duplicate pair, this means 8% of samples submitted are control samples, a point not to be overlooked in setting the budget for analyses. 

Once the preliminary error checking of the raw data has been done, the control samples should be separated from the normal samples for more detailed examination. This process of separation is greatly aided by the inclusion of the STD SAMP field in the field database (see earlier) and a comprehensive sample list that identifies control samples and their relationships (Fig. 5.3). Control sample results can then be subjected to a number of statistical and plotting procedures that determine the accuracy and precision of results. These processes give an indication of the levels of uncertainty that are associated with the results, information that is essential to interpret the data and present it in a meaningful manner. 

Sometimes, as a series is sampled, the level of noise in each sample depends on diat of the previous sample. This is common in process control. For example, there may be problems in one aspect of the manufacturing procedure, an example being die proportion of an ingredient. If the proportion is in error by 0.5 % at 2 pm, does this provide an indication of die error at 2.30 pm ... 

When the process is under statistical control, the day-to-day results are normally distributed about the center line. A result outside the warning line indicates that something is wrong. Such a result need not be rejected, but documented procedures should be in place for suitable action. Instruments and sampling procedures should be checked for errors. Two successive values of the Quality Control sample falling outside the action line indicate that the process is no longer under statistical control. In this case, the results should be rejected and the process investigated for its unusual behavior. Further analyses should be suspended until the problem is resolved. 

We have seen that two sources of sampling error are the variation of the material as a short-range or localized phenomenon the FE, and the GSE. Variations, such as cycles, long-range trends, and nonrandom changes, result from differences in the material over time. Changes in the process, either intentional or incidental, result in variation, and samples taken sufficiently far apart in time may differ from each other substantially in the properties of interest. If we do not characterize the process variation relative to the material variation, our ability to understand and control the process or to reduce its variation will be limited and, in some cases, futile. 

While these techniques are widely used, they do not provide sufficient purity. Liquid phase purification is not an environmentally friendly process and requires corrosion-resistant equipment, as well as costly waste disposal processes. Alternative dry chemistry approaches, such as catalyst-assisted oxidation or ozone-eiuiched air oxidation, also require the use of aggressive substances or supplementary catalysts, which result in an additional contamination. Moreover, in many previous studies trial and error rather than insight and theory approaches have been applied. As a result, a lack of understanding and limited process control often lead to extensive sample losses of up to 90%. Because oxidation in air would be a controllable and enviromnentaUy friendly process, selective purification of carbon nanomaterials, such as CNT and ND, in air is very attractive. In contrast to current purification techniques, air oxidation does not require the use of toxic or aggressive chemicals, catalysts, or inhibitors and opens avenues for numerous new applications of carbon nanomaterials. 

An estimate of the combined variation from the first five sampling errors and the analytical error can be obtained by taking several samples very close together in time just seconds or minutes apart. Controlling process variation below this amount will be impossible unless the variation due to these errors is reduced. The long-term periodic and nonperiodic variation will presumably not be present in these samples because the process will not have changed much during this short span. Since analytical variation for specific methods is typically known it can be subtracted out to get an estimate of the contribution from the first five errors. 

Sample sets are provided for one-, five-, and ten-year cycles of continuous exposure, with an extra set for posterity. Another set is shipped, handled, dried, and weighed with the test samples but is never exposed outdoors, as a test of possible errors caused by the manipulations. Three briquettes of each stone type comprise each set. Control samples are also retained at the laboratories they are processed and measured with the sets that have been exposed. 

The mean value and the error bars should fall within the given limits for the assay for the various control samples both for actual OD values and for the processed PI% data. 

In the determination of metals from biological and medical matrixes, thermal ionization mass spectrometry is seldom used. Disadvantages of thermal ionization MS are the great fluctuations in the results, caused by different instrumental requirements. Isotope fractionation resulting from vaporization of the sample and the dependence of this process on temperature are the main sources of error. However, the development of computer-controlled sample preparation and measurements have minimized these errors ... 

Errors in Sampling Leading to Errors in Process Control... 

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