Related Experiment Video
Updated: Aug 11, 2026

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays
Published on: April 23, 2012
Correction of analytical results for recovery: a comparison of the method performance characteristics from recent
C von Holst1, J Stroka, E Anklam
1European Commission, Joint Research Centre, Institute for Health and Consumer Protection, I-21020 Ispra, Italy. christoph.von-holst@jrc.it
Abstract:
Results from recently conducted collaborative trials on the determination of aflatoxin B(1) in various matrices have been evaluated to establish whether the use of recovery data would result in a distinct change of the relative between-laboratory standard deviation (RSD(R)) of the corrected data compared with the uncorrected data. In addition, we applied conventional and robust statistics to evaluate whether the impact of the use of recovery data on the estimation of RSD(R) depended on the statistical method applied for data analysis. This investigation was based on means before and after correction for recovery. The method performance characteristics were calculated using results from naturally contaminated test materials, while the results from test materials fortified with the target analytes were used to estimate the recovery. The study revealed that applying conventional and robust statistics in general led to comparable estimates for RSD(R). The comparison about the use of recovery data showed that in most cases, the RSD(R) obtained from the analysis of aflatoxin B(1) decreased after correction of the results for recovery. This tendency was similar when the comparison was done using robust or conventional statistics. However, in three cases, conventional statistics yielded a higher RSD(R) for the corrected data, whereas robust statistics showed the opposite. Looking carefully at the data, the treatment of conventional statistics indicated that the way outliers are detected and removed could result in an under- or overestimation of RSD(R). Applying the law of error propagation revealed that most likely the correlation between the uncorrected data and the recovery rate led to a reduced variability of the data corrected for recovery.
More Related Videos
09:44RNAi-mediated Control of Aflatoxins in Peanut: Method to Analyze Mycotoxin Production and Transgene Expression in the Peanut/Aspergillus Pathosystem
Published on: December 21, 2015
10:24Non-destructive SPE-UPLC-based Quantification of Aflatoxins and Stilbenoid Phytoalexins in Single Peanut (Arachis spp.) Seeds
Published on: April 19, 2024
Related Concept Videos
Data Validation
Key parameters for method validation include:
Atomic Absorption Spectroscopy: Lab
Solutions containing organic solvents, such as low-molecular-mass alcohols, esters, or ketones, enhance absorbances by increasing nebulizer...