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Metabolic markers are considered as a promising choice for food authentication, but few metabolic markers were available to develop robust analytical methods for food authentication in routine control. Untargeted metabolomics by liquid chromatography-mass spectrometry (LC-MS) is increasingly used to discover new metabolic markers. This review summarises the general workflow, recent applications, advantages, limitations, and future needs of untargeted metabolomics by LC-MS for identifying metabolic markers. It concludes that untargeted metabolomics by LC-MS shows great efficiency to discover the metabolic markers for the authenticity assessment of biological identity, geographical origin, agricultural production, processing technology, freshness, and cause of animals’ death.

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There have been an increasing number of cases of substitution of organic produce by conventially grown produce driving the need for robust methods to authenticate organically grown produce. Metabolomics to distinguish organic produce from conventially grown produce has been used for some time. This review examines the latest applications, advantages, challenges and future perspectives of HRMS based targeted and untargeted metabolomics for discrimination between organic and conventional crops. It concludes that data from combined techniques, such as untargeted and targeted metabolomics or metabolomics and stable isotope analysis, can improve the robustness of discriminative models, but they require further validation.

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Not from concentrate (NFC) orange juice sells at a premium compared to orange juice from concentrate. Chinese researchers have used untargeted metabolomics followed by identification of potential markers from standards to distinguish the two types of orange juice. This produced 91 and 42 potential markers present in NFC orange juice using the mass spectrometer injection in positive and negative mode, including 7 tripeptides (reported for the first time in orange juice). A partial least squares discriminant analysis model, based on the potential markers in positive mode was constructed and validated with 97% and 95% accuracy for training and test. The model was successfully applied to commercial samples, and one NFC brand of orange juice was found to be possibly mislabelled.

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