Measurement uncertainty and the path to reliable results
Measurement uncertainty in correlation to LIMS software: how precise results fit into a scale of values and probabilities
Sharing data and values is the fundamental role of a laboratory. As a matter of fact, laboratories must provide this information to prove their credibility and reliability. Measurement processes are also crucial in the laboratory’s workflows. They belong to a big part of a lab routine, as well as the use of laboratory software to support data evaluation, sample track, and analyses management. That software is called LIMS: a laboratory information management system. In this article, we will analyze the importance of measurement uncertainty and its influence on the connection to measurement instruments through a LIMS software.
Results are what derives from measurement methods. Results must be, therefore, accurate since decision-making processes are most of the time based on them. However, we should start asking ourselves the question: how precise must those results be?
It is widely recognized that empiric results cannot be exact since they can be affected by factors such as temperature, measurement instruments, and even humans. It is also important to consider that measurement instruments do not deliver 100% exact values and consequently, expression of uncertainty should not be underestimated.
The result of a measurement process is nothing but a number. Let us think for just a moment about everyday life. Numbers are a significant part of it. The measurement is, therefore, just the process meant to provide a number. And numbers are made to provide information. For this reason, the result of a measurement process is just a way to provide information through numbers.
Considering that results are not 100% exact, the parameter of measurement uncertainty has a crucial role in determining results’ accuracy. However, let us clarify accuracy before, a vital concept in this topic.
Thus, the accuracy of results has always been concerning scientists and engineers since measurement results are values, on which experts can build their theories. The information provided by measurement results is incomplete, and uncertainty is the most suitable concept to define it.
Propagation of uncertainty derives from all the possible combinations of uncertainties of the reference values, to which the results are traceable. As a matter of fact, measurement uncertainty and traceability are related concepts that are essential to define the quality of analytical data.
Hence, according to the ISO 5725 definition, accuracy consists of two fundamental concepts: trueness and precision. However, accuracy always involves the parameter of acceptability limits and accuracy profiles. This last parameter is strongly related to the topic of measurement uncertainty. Moreover, method validation, combined with measurement uncertainty, can provide an accurate estimation, or even check if the analytical method correctly fits the legal requirement.
However, before getting deeper into the theme of measurement uncertainty and its correlation to LIMS software, let us analyze some key concepts, such as method validation, traceability, and calibration.
Let us start with method validation, which has crucial importance in lab workflows. In fact, method validation considers all the possible effects or factors of influence on the final result and makes them traceable. Moreover, method validation finds all uncertainties of measurement and all effects associated with it within the specifications.
Therefore, let us analyze the concept of acceptable standards of accuracy: a result cannot be interpreted without a statement of uncertainty, and therefore before calculating measurement uncertainty the result has to be traceable and fit a reference or standard which is assumed to be true.
Measurements are supposed to provide us the quantitative knowledge about things and phenomena. Therefore, validation confirms the fitness-for-purpose of a specific analytical or empiric method. Given this circumstance, ISO standards involve confirmation by examination and endorsement by objective evidence that the requirements and specifications have been respected and fulfilled.
The LIMS software must react quickly to fulfill the requirements as well as cover the customers’ demands. LIMS software, such as [FP]-LIMS, is designed to manage analyses and, therefore, fulfill the specifications to investigate analytics. On the evidence that the objective of validation has been taken into account, we can affirm that validation has the task to measure the different effects, that influence the result, and to make sure that there are no other effects that can be considered.
By extension, the concept of traceability plays an essential role and needs to be examined as well. Let us start affirming that a traceable method implies a statement of accuracy and truth, although errors can always occur. Traceability involves the “history” of the results and shows if legal and international requirements have been respected. A method can be called traceable when it produces results, with their corresponding uncertainties, and the exact path that leads to them.
Strongly correlated to that is the calibration concept, which concerns the relationship between obtained measurement results through a measurement instrument and the expected measurement standards. Thus, calibrating is the result made traceable to the standards. Laboratory software must fulfill these requirements as well to be able to trace the result and deliver them in the most correct way possible and to meet the criteria of trueness and reliability.
Let us make an elementary example. A lab technician is conducting an OES analysis, and the parameters of truth and credibility swing between 0,5 and 0,8. Many factors, such as the composition of the chemical elements, also impact the analysis results. His optical emission spectrometer delivers 0,6 as a result, which means this value fulfills the fitness-for-purpose of a specific analytical system and, therefore, the criteria of trueness and traceability.
However, how the concept of “measurement” is combined to “uncertainty” and how this combination causes uncertainty in the process of measuring is still an open question in the field of metrology. Measurement uncertainty is a parameter that characterizes the dispersion of measured values. Within this parameter’s width, the true value lies with a specified probability, considering all sources of error. Consequently, the true and accurate result lies within this interval. This is why the concept of measurement uncertainty cannot be used as a synonym of “wrong result”. Measurement uncertainty expresses a values range within a result is true, accurate, and reliable.
For this reason, the LIMS software can cover a wide range of customers’ requests. Measurement uncertainty enables us to set a range of values, since measurement instruments do not deliver exact values. The customers should have the possibility to evaluate whether their values fit into the measurement uncertainty range. This makes results more reliable and reduces the scale of errors. Thus, all measurements are imperfect and have many potentials of variation. Therefore, measurement uncertainty helps to identify any limitation of the method and the opportunities to improve it.
As a result, there are no precise norms to determine measurement uncertainty. Therefore, software developers must cooperate very accurately with lab technicians to understand their specific needs and workflows. Statistic evaluations also help software developers of Fink & Partner GmbH systematically improve LIMS use and interfaces concerning the varying range of measurement instruments and their results – and, therefore, the parameter of measurement uncertainty.
To sum up, we can consider that no measurement is perfect since they have sources of variation. Therefore, measurement uncertainty can provide a helpful interval on values within which the values are accurate, traceable, and true. LIMS and measurement uncertainty cooperate: the one integrates the other, since LIMS collects, analyzes, and evaluates the results. Finally, measurement uncertainty is the opposite of “error” since it leads to reliable values according to the improvement range scale.