Veronika R. Meyer | Authors


Sampling: The Ghost in Front of the Laboratory Door

Sampling can be the most demanding part of an analysis. Anybody in charge of sampling needs a good understanding of the composition of the material to be investigated, its heterogeneity (or homogeneity, in simple cases), and the chemical properties of the analytes. Sampling procedures must be described in detail. Detecting the bias of a sampling procedure can be difficult; this fact is trivial, but it must not be forgotten.

Sampling: The Ghost in Front of the Laboratory Door

How does one meet the most demanding part of an analysis-sampling? A typical example is soil, which presents a twofold problem for the analyst: first, the selection of the sites where the samples are taken, and second, the reduction of a sample (for example, 1 kg) to the analysis aliquot size (for example, 10 μL). This paper describes the details of sampling issues.

Weighted Linear Least-Squares Fit — A Need? Monte Carlo Simulation Gives the Answer

Spreadsheet computer simulations can identify the influencing factors for the set-up of a calibration function such as the number of calibration points and their distribution or the position of the experimental points. By using a Monte Carlo approach, the quality of the experimental results (bias and standard deviation) can be studied under different conditions. This article presents a spreadsheet for the simulation of unweighted and weighted linear least-squares fit.

Can We Trust Experimental Standard Deviations?

The statistical nature of experiments leads to the fact that standard deviation (SD) of replicate analysis is not constant but a number with some variability: a standard deviation has its own standard deviation. As a consequence, a SD calculated from three experiments is not rugged and even SDs from ten experiments show a great variability. Analysts should be aware that with 100 or more the results have low scatter

Method Transfer in HPLC

Many HPLC analyses could be performed at lower expenditure. This could involve a combination of reducing the analysis time, reducing the resolution between critical peaks, and lowering the consumption of mobile phase. Successfully optimizing the method in such instances - as well as in situations where it is necessary to transfer the method to another laboratory that lacks the same selection of columns - can save the analyst time and money.