Corrigendum to 'Data in support of the detection of genetically modified organisms (GMOs) in food and feed samples' [Data Brief 7 (2016) pp. 243–252]

Abstract Background One of the parameters required for the assessment of food and feed safety is detection and identification of genetically modified organisms. Legislation in some countries necessitates detection and quantification of modification in food and feed samples. Scientists have raised concern about safety of antibiotic resistance marker (ARM) genes used for transformation of crops intended for human and animal consumption. In the present work two molecular approaches have been adopted: one exploratory; for detection and quantification of ARM genes in tested plant samples and the other confirmatory; to determine the specificity/reliability of the obtained results. Results Results revealed that primers for neomycin phosphotransferase (nptII) and aminoglycoside 3″ adenyl-transferase (aadA) were amplified in the majority of the 36 DNA screened samples. Melting curve analysis using hygromycin phosphotransferase (aphIV) gene as target sequence for the fluorescent-based detection approach was performed to ensure reliability and specificity of this procedure and to confirm results obtained by using conventional polymerase chain reaction (PCR). Quantitative RT-PCR results and validation analysis followed, revealed that all of the tested DNA samples were not violating the European legislation for GMOs labeling (0.9%). Conclusions The results fully demonstrated the reproducibility, sensitivity/specificity of the adopted approaches for detection and quantification of even traces of GMO contents. Applying measurement uncertainty (MU) procedures presented in this work will help decision makers to ensure compliance with International Legislation and Regulations. This in its turn will facilitate and enhance trading with countries having compelling labeling regulations.

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Calculation of Measurement Uncertainty in Quantitative Analysis of Genetically Modified Organisms Using Intermediate PrecisionA Practical Approach

Journal of AOAC International ◽

10.1093/jaoac/90.2.582 ◽

2007 ◽

Vol 90 (2) ◽

pp. 582-586 ◽

Cited By ~ 16

Author(s):

Jana Žel ◽

Kristina Gruden ◽

Katarina Cankar ◽

Dejan tebih ◽

Andrej Blejec

Keyword(s):

Nucleic Acids ◽

Measurement Uncertainty ◽

Genetically Modified Organisms ◽

Genetically Modified ◽

Laboratory Diagnostics ◽

Quantitative Characterization ◽

Intermediate Precision ◽

Food And Feed ◽

Repeatability And Reproducibility

Abstract Quantitative characterization of nucleic acids is becoming a frequently used method in routine analysis of biological samples, one use being the detection of genetically modified organisms (GMOs). Measurement uncertainty is an important factor to be considered in these analyses, especially where precise thresholds are set in regulations. Intermediate precision, defined as a measure between repeatability and reproducibility, is a parameter describing the real situation in laboratories dealing with quantitative aspects of molecular biology methods. In this paper, we describe the top-down approach to calculating measurement uncertainty, using intermediate precision, in routine GMO testing of food and feed samples. We illustrate its practicability in defining compliance of results with regulations. The method described is also applicable to other molecular methods for a variety of laboratory diagnostics where quantitative characterization of nucleic acids is needed.

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Log transformation of proficiency testing data on the content of genetically modified organisms in food and feed samples: is it justified?

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-019-02338-4 ◽

2019 ◽

Vol 412 (5) ◽

pp. 1129-1136 ◽

Cited By ~ 3

Author(s):

Wim Broothaerts ◽

Fernando Cordeiro ◽

Philippe Corbisier ◽

Piotr Robouch ◽

Hendrik Emons

Keyword(s):

Normal Distribution ◽

Proficiency Testing ◽

Genetically Modified Organisms ◽

Genetically Modified ◽

Data Transformation ◽

Evaluation Procedure ◽

Reference Laboratory ◽

Log Data ◽

Log Transformation ◽

Food And Feed

AbstractThe outcome of proficiency tests (PTs) is influenced, among others, by the evaluation procedure chosen by the PT provider. In particular for PTs on GMO testing a log-data transformation is often applied to fit skewed data distributions into a normal distribution. The study presented here has challenged this commonly applied approach. The 56 data populations from proficiency testing rounds organised since 2010 by the European Union Reference Laboratory for Genetically Modified Food and Feed (EURL GMFF) were used to investigate the assumption of a normal distribution of reported results within a PT. Statistical evaluation of the data distributions, composed of 3178 reported results, revealed that 41 of the 56 datasets showed indeed a normal distribution. For 10 datasets, the deviation from normality was not statistically significant at the raw or log scale, indicating that the normality assumption cannot be rejected. The normality of the five remaining datasets was statistically significant after log-data transformation. These datasets, however, appeared to be multimodal as a result of technical/experimental issues with the applied methods. On the basis of the real datasets analysed herein, it is concluded that the log transformation of reported data in proficiency testing rounds is often not necessary and should be cautiously applied. It is further shown that the log-data transformation, when applied to PT results, favours the positive performance scoring for overestimated results and strongly penalises underestimated results. The evaluation of the participants’ performance without prior transformation of their results may highlight rather than hide relevant underlying analytical problems and is recommended as an outcome of this study.

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