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For researchers, writing a scientific research project proposal is only one step along the research journey. A key aspect of most research projects is the analytical method used, highlighting the importance of analytical method validation. High-quality analytical method validation allows researchers to gain reliable results, establish interlaboratory reliability, and tackle the crisis of irreproducible or erroneous results. Hence, analytical method validation also helps researchers protect their reputations.
A robust analytical method validation approach is essential to produce valid data, which can subsequently be demonstrated as both repeatable and reproducible.
What is analytical method validation?
Analytical method validation proves that a method is suitable for its intended use and fulfils the necessary quality requirements for the purpose of acquiring results. Unvalidated methods often yield irreproducible results—a common occurrence in science. And the reproducibility crisis is becoming quite widespread in academic research.
Regulating analytical method validation
Over the last decade, several analytical method validation guidelines have been developed by various industrial committees, regulatory agencies, and organisations for purposes of quality control and regulatory compliance. While specific aspects of method validation are likely to differ for pharmaceutical, clinical, regulatory, or research applications, one is responsible for ensuring that validation procedures fulfil the requirements of the desired application.
What parameters are analysed in analytical method validation?
Analytical method validation examines a wide range of parameters, including repeatability and reproducibility, specificity, linearity and range, detection limit, quantitation limit, precision, system suitability, and accuracy. In its most basic form, analytical method validation ensures that the method provides valid and reproducible test results using a process that can be implemented with ease.
Analytical method validation in research
Here are three key steps you can follow to maintain accuracy in analytical method validation:
- Ensuring repeatability/reproducibility: Your analytical data must include repeatability (‘n’), i.e., the number of all repeats for averages, and an indication of the closeness of the measurements. ‘n’ may vary depending on the study design and established cut-offs in a specified field. Reproducibility indicates the accuracy of measurement results, even if they are obtained at different laboratories. This will help add credibility to the study outcome.
- Ensuring robustness: It reflects the ability of your procedures to provide analytical results of acceptable accuracy and precision under a range of different conditions.
- Ensuring system suitability: This criterion refers to checking an analytical system, to ensure optimal system performance for its intended use. While not commonly used in academic research, it is a mandatory step in many industrial studies prior to testing real-world samples.
In selecting analytical methods, prioritise ease of implementation, stability, and precision. Emphasise computer control and rapid data acquisition for optimal progress. Consider specificity, detection, and quantitation limits for optimal data robustness.
Ultimately, whether you are writing for the top or the bottom of the evidence hierarchy pyramid, analytical method validation is essential. To ensure that your analytical methods are of the highest quality, check out Elsevier Author Services for a wide range of resources and support. Our experts are happy to help you navigate the analytical method validation process and resolve all your queries.
