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Sampling Columns

Risk: photo of a climber hanging from an overhang

Kim Esbensen and Claudia Paoletti hope that a risk assessment scope will provide the sampling community with an easier, and perhaps more powerful, way to reach out to business, commerce, trade as well as regulatory and law-enforcement authorities across many societal sectors. It may also speak a more business-oriented language beyond traditional “TOS technicalities”.

Issue 34/8 (2022)
Photo of confused person

“Error” and “uncertainty” are being used interchangeably and confusingly. This is “a scientific flaw of the first order”! However, Kim and Francis will put you right.

Issue 34/6 (2022)
Photo of the island where Viking remains were found

Sampling and Vikings seems to be the next unexpected connection within Kim Esbensen’s Sampling Column. Kim has been exploring an area of Southern Norway from where the founder of the Theory of Sampling, Pierre Gy, believed his ancestors originated. You will have to read the column to find the “smoking axe”! Oh, and there is an interesting report on the 10th World Conference on Sampling and Blending.

Issue 34/5 (2022)
Photo of entire crisps/chips and their crumbs

This column has invited two world-renowned experts in near infrared (NIR) spectroscopy to let the world benefit from decades of leading-edge experience, especially regarding sampling for quantitative NIR analysis.

Issue 34/4 (2022)

Sampling is nothing more than the practical application of statistics. If statistics were not available, then one would have to sample every portion of an entire population to determine one or more parameters of interest. There are many potential statistical tests that could be employed in sampling, but many statistical tests are useful only if certain assumptions about the population are valid. Prior to any sampling event, the operative Decision Unit (DU) must be established. The Decision Unit is the material object that an analytical result makes inference to. In many cases, there is more than one Decision Unit in a population. A lot is a collection (population) of individual Decision Units that will be treated as a whole (accepted or rejected), depending on the analytical results for individual Decision Units. The application of the Theory of Sampling (TOS) is critical for sampling the material within a Decision Unit. However, knowledge of the analytical concentration of interest within a Decision Unit may not provide information on unsampled Decision Units; especially for a hyper-heterogenous lot where a Decision Unit can be of a completely different characteristic than an adjacent Decision Unit. In cases where every Decision Unit cannot be sampled, application of non-parametric statistics can be used to make inference from sampled Decision Units to Decision Units that are not sampled. The combination of the TOS for sampling of individual Decision Units along with non-parametric statistics offers the best possible inference for situations where there are more Decision Units than can practically be sampled.

Issue 34/2 (2022)

Kim Esbensen, along with Dick Minnitt and Simon Dominy, tackle the ever-present dangers in sub-sampling; in this case in the assaying lab of mining companies.

Issue 34/1 (2022)

Getting your sampling right can hardly be more important than in the nuclear waste industry. This column describes how the Belgian nuclear waste processing has benefited from the Theory of Sampling, and how it has led to important insights leading to significant potential improvements in the field of radioactive waste characterisation.

Issue 33/8 (2021)

A Special Section dedicated to examing the “Economic arguments for representative sampling” with contributions from over 20 representative sampling experts.

Issue 33/7 (2021)

When previously industrialised or urbanised sites are redeveloped, the contamination of the soil is a vital consideration. It is essential that it is classified correctly as being fit for reuse or only for landfill, or even needing decontamination. When dealing with truck loads of soil, correct, representative sampling is essential to assure safety and to minimise unnecessary costs.

Issue 33/6 (2021)

Kim Esbensen has enlisted the support of another doyen of representative sampling, Pentti Minkkinen. In the commercial world, the reason for analysis comes down to money: whether ensuring you are getting what you paid for, but not providing more than necessary, or in regulatory compliance and the avoidance of fines. Kim’s Column has been pushing the importance of not overlooking the sampling step since its beginnings, and this edition provides clear examples where the consequences are costly; very costly.

Issue 33/5 (2021)

Latest Issue

Editor: Kim H. Esbensen

What is the meaning of analysing any sample if it cannot be documented to be representative? The answer is “none”, and that is the reason for this column. Starting with the Theory of Sampling, it builds into a valuable resource covering the theory and practice of representative sampling.
Read more about the Column Editor.