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The presence of endocrine disruptors in food is creating concern, and consequently, efficient methods for their identification are needed. In vitro bioassays have been developed to study endocrine properties of pure chemicals as well as mixtures. Because of their relative ease of execution and low cost, application of such techniques to whole foods is tempting. This raises a number of technical challenges. To address them, a study was commissioned in a contract laboratory offering in vitro estrogenicity testing of food products. Four different commercial and regulatory compliant cereal products were sent in blinded triplicates. Some samples were reported to be active by the laboratory. However, the results were not reproducible and very close to the 0% measurable activity. Furthermore, a statistical analysis of the raw data indicated an absence of activity. This was surprising since these products were expected to naturally contain estrogenic compounds such as the phytoestrogens lignans and flavones. However, phytoestrogens in cereals are known to occur mainly in bound, inactive, forms, suggesting that the sample preparation used in the present study was not suitable to release active estrogenic substances. This hypothesis was supported by preliminary data using a high‐resolution mass spectrometry method which did not detect any significant levels of phytoestrogens in the tested cereal extracts. Using a yeast‐based method, no estrogenic activity was found in breakfast cereals. However, benchmarking the bioassay data against quality criteria raised the question of the qualification of the sample preparation and bioassay used to adequately address estrogenic activity of whole foods. The quality requirements for testing whole food samples were formally addressed. The application of validated and harmonized methods for both sample preparation and bioassay testing is strongly advocated.

Metabolome profiles of ca. 750 compounds obtained from blood samples from 28 day rat studies (OECD 407) were combined with toxicity profiles in one database over ten years to predict toxicity of new compounds. We provide detailed descriptions of procedures and recommendations for 'omics data-bases. Control of variability (biological, sampling/storage and technical measurement) is essential. At the start of large scale projects control variability should be extensively investigated. Reference (positive control) substances should be used to evaluate and obtain a good signal/noise ratio. Procedures should be documented in standard operating procedures and followed meticulously. Exact repeats of positive controls should be regularly performed, to assess variability of positive responses. Control data should be regularly checked for shifts and analysed to obtain information concerning normality. If possible, data should be analysed by multiple procedures and conclusions should be drawn based on a joint assessment, not unlike peer review processes in histopathology.