Errors in sampling

The data on the left were obtained under conditions in which random errors in sampling and the analytical method contribute to the overall variance. The data on the right were obtained in circumstances in which the sampling variance is known to be insignificant. Determine the overall variance and the contributions from sampling and the analytical method. 

Lis the possible sources of loss or error in sampling for particulate matter. 

As an alternative, sampling BW from a point just below the extra-low-level alarm point usually is also suitable. However, the BW is not always at its most concentrated point at the water column connection. A sample taken from this point may contain a mix of BW and steam, which leads to errors in sampling and hence, testing, reporting, interpretation, and subsequent actions. 

A major source of error in sampling may be incorporated from the actual process of taking increments from the bulk material,... 

Towards The Education of Analysts. Dr. Stalling expressed the view, think there are two critical factors which are the error in sampling estimates verses repeatability estimates. Those are two different universes as far as I can see. Without preserving that information in the final report that says the analytical uncertainty is such and such, a good deal of information is lost. ... 

This variation may be a result of (1) a real difference in children s intake of soil, (2) that the intake of the various tracer elements are not only from soil, but also from food or other objects which children put into their mouth, (3) errors in sampling excreta, primarily feces, from the diaper, (4) tracer elements that are transferred to feces via contact with the diaper, e.g., from certain skin lotions, (5) a difference in absorption of the various tracer elements from the gastrointestinal tract, and (6) the collected soil samples that are usually inhomogeneous and not representative of an average exposure. 

Errors in sampling (sample volume determination) are due to erroneous measurement of time or flow rate. Time can be measured so accurately that flow rate errors make up the majority of the 10% variation attributed to the average sampling pump. 

Avoidance of errors in sample preparation (extraction, derivatization) could be minimized by rigorous training of laboratory personnel, including appreciation of the patient behind each anonymous test tube. An environment free of noise and distractions is required to minimize the risk of serial solvent extractions being pooled in the wrong tube redundant labeling of glassware and step-by-step checklists are also critical elements of error prevention and detection. 

Table II. Summary of Manual Techniques and Sources of Error in Sampling Particulate Nitrate and Nitric Acid ...

New methods for non-destructive quantitative analysis of additives based on MIR spectra and multivariate calibration have been presented [67, 68], One of the limitations in the determination of additive levels by MIR spectroscopy is encountered in the detection limit of this technique, which is usually above the low concentration of additive present, due to their heavy dilution in the polymer matrix. The samples are thin polymer films with small variations in thickness (due to errors in sample preparation). The differences in thickness cause a shift in spectra and if not eliminated or reduced they may produce non-reliable results. Methods for spectral normalisation become necessary. These methods were reviewed and compared by Karstang et al. [68]. MIR is more specific than UV but the antioxidant content may be too low to give a suitable spectrum [69]. However, this difficulty can be overcome by using an additive-free polymer in the reference beam [67, 68, 69, 70]. On the other hand, UV and MIR have been successfully applied to quantify additives in polymer extracts [71, 72, 66]. 

The key factor in minimizing the overall sampling error is the elimination or reduction of these qualitative human errors in sampling design and field procedures. 

In the course of sample tracking, data evaluation, and interpretation, field sample IDs may be entered into several different field forms, spreadsheets, and data bases, and appear on maps and figures as identifiers for the sampling points. Because the field records and computer data entry during sample receiving at the laboratory are done for the most part manually, errors in sample ID recording are common. To reduce data management errors, sample numbers must be simple, short, and consecutive. 

In environmental analysis, especially in the determination of organic pollutants, sampling is considered to be the most critical step, the one that most often makes the greatest contribution to the total uncertainty of analysis. Therefore, to reduce the uncertainty of the analytical result, the closest attention should be paid to the sampling process. The main sources of error in sampling can be found in Madrid and Zayas.10... 

Internal standards are used to correct for errors in sample preparation or sample introduction and to help determine solute recoveries. They are added to the sample at the earliest possible introduction point in the analytical process. Standards solutions containing the solutes of interest are prepared, preferably at two or three different, known concentrations but with a constant concentration of internal standard. The same concentration of internal standard is also added to each sample. All the standards and samples receive the same treatment from sample preparation, through the sample introduction and separation processes, to detection. 

One important practical aspect of PLS is that it takes into account errors both in the concentration estimates and spectra. A method such as PCR will assume that the concentration estimates are error free. Much traditional statistics rest on this assumption, that all errors are in the dependent variables (spectra). If in medicine it is decided to determine the concentration of a compound in the urine of patients as a function of age, it is assumed that age can be estimated exactly, the statistical variation being in the concentration of a compound and the nature of the urine sample. Yet in chemistry there are often significant errors in sample preparation, for example, accuracy of weighings and dilutions and so the independent variable (c) in itself also contains errors. With modem spectrometers, these are sometimes larger than spectroscopic errors. One way of overcoming this difficulty is to try to minimise the covariance between both types of variables, namely the x (spectroscopic) and c (concentration) variables. 

A 1-in-diameter tube is used to collect a stack sample from a stack in which air is flowing at a velocity of 30 ft/s. The sampling pump available can pump only at a rate of 1 ft3/min. Estimate the error in sampling for (a) 10-pm-diameter spheres with p = 2 g/cm3 and (6) 0.1 p.m-diameter spheres with p = 10 g/cm3. 

The objective of statistical samphng is to establish likely values for the true error rate in the population of data being considered. If the tme error rate was known, the probabihties of given numbers of errors in samples could be obtained mathematically using standard statistical distributions. Statistical inference allows the reverse process — from an observed error rate in a sample likely and possible true error rates can be inferred. Likely data population error rates are defined by the 99% single upper confidence limit, and possible data population error rates by the 99.9% single upper confidence limit on the sample error rate. 

The first step of the particle size characterization process is acquiring a representative sample from a batch or lot of material. The issue of bulk sampling is complex the first chapter of this book is devoted to sampling theory. Any analysis of particle size is only as good as the sample used for the measurement and any errors in sampling will lead to erroneous results. 

Data validation is the final step before releasing results. This process starts with validating the samples and methods used. Then the data are reported with statistically valid limits of uncertainty after a thorough check has been made to eliminate errors in sampling and sample handling, in performing the analysis, in identifying samples, and in the calculations used. 

Although innumerable possibilities exist for errors in sampling of this sort, and it is very difficult, if not impossible, to determine in situ formation conditions, the data presented and discussed (Table I) represent 2in internally consistent data set. 

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