Multivariate equivalence testing for food safety assessment. (December 2022)

• Record Type: Journal Article
• Authors: Leday, Gwenaël G.R.; Engel, Jasper; Vossen, Jack H.; de Vos, Ric C.H.; van der Voet, Hilko
• Is Part Of: Food and chemical toxicology. Volume 170(2022)
• Publication Date: 2022-12
• Subjects: Food safety -- Genetically modified crops -- Equivalence testing -- Desired power -- Multivariate equivalence test -- Untargeted metabolomics; Toxicology -- Periodicals; Food poisoning -- Periodicals; Food Poisoning -- Periodicals; Toxicology -- Periodicals; Toxicologie -- Périodiques; Intoxications alimentaires -- Périodiques; Food poisoning; Toxicology; Periodicals; Electronic journals; 615.9
• DOI: 10.1016/j.fct.2022.113446
• Languages: English
• ISSNs: 0278-6915

Abstract

Abstract: Products for food and feed derived from genetically modified (GM) crops are only allowed on the market when they are deemed to be safe for human health and the environment. The European Food Safety Authority (EFSA) performs safety assessment including a comparative approach: the compositional characteristics of a GM genotype are compared to those of reference genotypes that have a history of safe use. Statistical equivalence tests are used to carry out such a comparative assessment. These tests are univariate and therefore only consider one measured variable at a time. Phenotypic data, however, often comprise measurements on multiple variables that must be integrated to arrive at a single decision on acceptance in the regulatory process. The surge of modern molecular phenotyping platforms further challenges this integration, due to the large number of characteristics measured on the plants. This paper presents a new multivariate equivalence test that naturally extends a recently proposed univariate equivalence test and allows to assess equivalence across all variables simultaneously. The proposed test is illustrated on plant compositional data from a field study on maize grain and on untargeted metabolomic data of potato tubers, while its performance is assessed on simulated data. Highlights: A multivariate equivalence test for safety assessment of GM crops is proposed. The proposed test assesses equivalence globally over all analytes rather than locally for each analyte. The proposed test circumvents the multiplicity problem of univariate equivalence tests. The statistical power for showing equivalence for test genotypes is controlled at a desired value by construction. The proposed test is illustrated on maize compositional and potato metabolomics data to compare GM and reference genotypes.