Despite the importance of stating the measurement uncertainty in chemical analysis, concepts are still not widely applied by the broader scientific community. The Guide to the expression of uncertainty in measurement approves the use of both the partial derivative approach and the Monte Carlo approach. There are two limitations to the partial derivative approach. Firstly, it involves the computation of first-order derivatives of each component of the output quantity. This requires some mathematical skills and can be tedious if the mathematical model is complex. Secondly, it is not able to predict the probability distribution of the output quantity accurately if the input quantities are not normally distributed. Knowledge of the probability distribution is essential to determine the coverage interval. The Monte Carlo approach performs random sampling from probability distributions of the input quantities; hence, there is no need to compute first-order derivatives. In addition, it gives the probability density function of the output quantity as the end result, from which the coverage interval can be determined. Here we demonstrate how the Monte Carlo approach can be easily implemented to estimate measurement uncertainty using a standard spreadsheet software program such as Microsoft Excel. It is our aim to provide the analytical community with a tool to estimate measurement uncertainty using software that is already widely available and that is so simple to apply that it can even be used by students with basic computer skills and minimal mathematical knowledge.

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Subtle variations in the isotopic composition of elements carry unique information about physical and chemical processes in nature and are now exploited widely in diverse areas of research. Reliable measurement of natural isotope abundance variations is among the biggest challenges in inorganic mass spectrometry as they are highly sensitive to methodological bias. For decades, double spiking of the sample with a mix of two stable isotopes has been considered the reference technique for measuring such variations both by multicollector-inductively coupled plasma mass spectrometry (MC-ICPMS) and multicollector-thermal ionization mass spectrometry (MC-TIMS). However, this technique can only be applied to elements having at least four stable isotopes. Here we present a novel approach that requires measurement of three isotope signals only and which is more robust than the conventional double spiking technique. This became possible by gravimetric mixing of the sample with an isotopic spike in different proportions and by applying principles of isotope dilution for data analysis (GS-IDA). The potential and principle use of the technique is demonstrated for Mg in human urine using MC-TIMS for isotopic analysis. Mg is an element inaccessible to double spiking methods as it consists of three stable isotopes only and shows great potential for metabolically induced isotope effects waiting to be explored.

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