Sampling and sample preparation

Transportation (by belt, rail, or truck) can initiate (due to movement of the coal) processes that result in size and density segregation. Thus, variations from one side of a conveyor belt to the other, from side-to-side, end-to-end, and top-to-bottom locations in individual cars or trucks, and between one location and another in a coal pile, must be anticipated (ASTM D-346 ASTM D-2234 ASTM D-4182 ASTM D-4702 ASTM D-4915 ASTM D-4916 ASTM D-6315 ASTM D-6518 ASTM D-6543 ISO 1988). Therefore, the challenge in sampling coal from a source or shipment is to collect a relatively small portion of the coal that accurately represents the composition of the coal. This requires that sample increments be collected such that no piece, regardless of position (or size) relative to the sampling position and implement, is collected or rejected selectively. Thus, the coal sample must be representative of the composition of the whole coal (i.e., coal in a pile or coal in a railcar or truck) as represented by the properties or quality of the sample. 

Optimization of coal sampling is a function of the many variable constituents of coal. The effect of fineness on the combustion of pulverized coal is dramatic, and the special problems associated with collection of an unbiased sample of pulverized coal need to be addressed (ASTM D-197). Operating samples are often collected from the coal streams to power plants on a regular basis not only for determination of heat balance but also to document compliance with air pollution emission regulations. 

Samples submitted for chemical and physical analyses are collected for a variety of reasons, but the collection of each sample should always conform to certain guidelines. The application of precise techniques in sample collection helps to ensure that data from each analysis performed on the samples will be useful. For interpretations and comparisons of elemental compositions of coal beds to be valid, the samples must be collected so that they are comparably representative of the coal bed. Such interpretations and comparisons should never be based on data from different types of samples (Swanson and Huffman, 1976 Golightly and Simon, 1989). 

when a property of coal (which exists as a large volume of material) is to be measured, there usually will be differences between the analytical data derived from application of the test methods to a gross lot or gross consignment and the data from the sample lot. This difference (the sampling error) has a frequency distribution with a mean value and a variance. Variance is a statistical term defined as the mean square of errors the square root of the variance is more generally known as the standard deviation or the standard error of sampling. 

Recognition of the issues involved in obtaining representative samples of coal and minimization of the sampling error has resulted in the designation of methods that dictate the correct manner for coal sampling (ASTM D-346 ASTM D-2234 ASTM D-4702 ASTM D-4915 ASTM D-4916 ASTM D-6315 ASTM D-6518 ASTM D-6543 ISO 1988 ISO 2309). 

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In order to obtain reliable results, three steps are involved in the analysis sampling and sample preparation carotenoid extraction, separation, identification. 

Zeisler R, Wise SA (1985) Quality assurance and protocols in sampling and sample preparation of biological samples. In Wolf WR, ed. Biological Reference Materials Availability, Uses and Need for Validation of Nutrient Measurement, pp. 257-279. John Wiley and Sons, New York. 

J. Pawliszyn (ed.), Sampling and Sample Preparation for Field and Laboratory, Elsevier, Amsterdam (2003). 

Analyte dilution sacrifices sensitivity. Matrix matching can only be applied for simple matrices, but is clearly not applicable for complex matrices of varying composition. Accurate correction for matrix effect is possible only if the IS is chosen with a mass number as close as possible to that of the analyte elements). Standard addition of a known amount of the element(s) of interest is a safe method for samples of unknown composition and thus unknown matrix effect. Chemical separations avoid spectral interference and allow preconcentration of the analyte elements. Sampling and sample preparation have recently been reviewed [4]. 

J. Begerow and L. Dunemann, in Sampling and Sample Preparation (M. Stoeppler, ed.), Springer-Verlag, Berlin (1997), pp. 155-66. 

Mass spectrometry combines exquisite sensitivity with a precision that often depends more on the uncertainties of sampling and sample preparation than on those of the method itself. Mass spectrometry is a supreme identification and recognition method in polymer/additive analysis through highly accurate masses and fragmentation patterns quantitation is its weakness. Direct mass spectrometry of complex polymeric matrices is feasible, yet not often pursued. Solid probe ToF-MS (DI-HRMS) is a breakthrough. Where used routinely, mass spectrometrists are usually still in charge. At the same time, however, costs need to be watched. 

In some cases the object under study has to be continuously monitored. Then on-line analytical methods are applied by which the system can be directly measured. The analytical process then runs without sampling and sample preparation, as can be seen in Fig. 2.3a. The analytical process is shortened even more in the case of in-line analysis where measurement and... 

A comprehensive review of compositional and failure analysis of polymers, which includes many further examples of analysis of contaminants, inclusions, chemical attack, degradation, etc., was published in 2000 [2], It includes details on methodologies, sampling, and sample preparation, and microscopy, infrared spectroscopy, and thermal analysis techniques. 

In municipalities where the water supplies are from deep-bored wells, samples of household water were collected for Rn-222 analyses. The water samples were analysed by liquid scintillation counting. The method for sampling and sample preparation were adopted from Partridge et al (1979). 

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