|Labudde, Robert - Least Cost Formulations, Ltd|
|Harnly, James - Jim|
Submitted to: Official Methods of Analysis of AOAC International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/11/2011
Publication Date: 1/19/2012
Citation: Labudde, R., Harnly, J.M. 2012. Probability of identification (POI): a statistical model for the validation of qualitative botanical identification methods. Official Methods of Analysis of AOAC International. 95:273-285.
Interpretive Summary: Identification of botanical materials, especially botanical dietary supplements, has become a major concern in light of the tremendous growth of the market. Adulteration, both intentional and accidental, is common. The FDA has recently issued current good manufacturing procedures (cGMPs) that require identification of all botanical ingredients in a product. This has created an even greater demand for validated analytical methods for botanical identification. This paper presents an in-depth examination of the requirements for a validated method and is the basis for the document "Guidelines for Validation of Botanical Identification Methods" that is being issued by AOAC INTERNATIONAL. This paper discusses how the method requirements, the statistical processing, and the practical analytical chemistry interact. This will be of use to those who want to better understand the need to validate methods for identifying botanicals.
Technical Abstract: A qualitative botanical identification method (BIM) is an analytical procedure which returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material or whether it contains excessive non-target (undesirable) material. We describe the development and validation of studies for a BIM based on the idea of a proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection (POD), and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multi-collaborative study examples are given.