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In analytical chemistry, cross-validation is an approach by which the sets of scientific data generated using two or more methods are critically assessed. [1] The cross-validation can be categorized as either method validation [ 1 ] or analytical data validation.
Bioanalytical laboratories often deal with large numbers of samples, for example resulting from clinical trials. As such, automated sample preparation methods and liquid-handling robots are commonly employed to increase efficiency and reduce costs.
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
The EBF has also reached out to participate in international scientific meetings representing the bioanalytical voice of the European pharmaceutical industry. Selective examples are: American Association of Pharmaceutical Scientists meetings, The Boston Society Applied Pharmaceutical Analysis meetings, and Canadian Validation Group meeting.
A calibration curve plot showing limit of detection (LOD), limit of quantification (LOQ), dynamic range, and limit of linearity (LOL).. In analytical chemistry, a calibration curve, also known as a standard curve, is a general method for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration. [1]
This assay set-up can lack robustness and is not suitable for validation following the FDA's guidelines for bioanalytical method validation. This is demonstrated by an absence of published method that have been validated to the standards outlined by the FDA for bioanalytical methods.
Cross-validation includes resampling and sample splitting methods that use different portions of the data to test and train a model on different iterations. It is often used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.
Its application in a context of validation opened up to a new way to investigate results from a modelling process or an analytical method. In 2001, Bellocchi and co-workers firstly introduced the possibility to use fuzzy logic to evaluate model estimates at the Second International Symposium on Modelling Cropping Systems [11] ( Florence , Italy ...