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.
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.
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.
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.
A Special Section dedicated to examing the “Economic arguments for representative sampling” with contributions from over 20 representative sampling experts.
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.
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.
Many of you will be working in the cancer field, whether in diagnosis or research, in which case you will be very interested in this Sampling Column. Even if this is not your field of work, you will be interested from a personal point of view! It presents a fascinating idea and solution to improving the identification of different parts of a tumour and, thus, to provide better personalised treatment. “…in solid tumour oncology, representative sampling is truly a matter of life or death…”
This cannot be true—surely sampling and weighing are different activities. Well yes—and no! Sampling and weighing of traded metal, mineral and agro commodities are different activities—but at one or several stages in the supply chain they will come together in a single focus point, which is value ($, EUR).
The Sampling Column provides some easy-to-understand examples of what sampling errors are, what are the consequences of them and what can be done about them. Particular examples from pharma, PAT and NIR spectroscopy are provided.