REIMS is a direct tissue metabolic profiling technique used to accurately classify tissues using available mass spectral databases. This study was made to evaluate the reproducibility of the analytical equipment, methodology and tissue classification algorithms using a single-source reference material across four sites with identical equipment in the UK, Hungary, The Netherlands, and Canada. This was followed by each site analysing four different types of locally-sourced food-grade animal tissue.  Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69–100% correct classification for food-grade meat samples. The latter was caused by differences in animal tissue from local sources leading to significant variability in the accuracy of an individual site’s model. The  results inform future multi-site REIMS studies applied to clinical and food samples, and emphasise the importance of carefully-annotated samples that encompass sufficient population diversity.  

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