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The detection of food fraud and undeclared food allergens is one of the major challenges for competent authorities. Because adulterations are continuously adapted to the methods used to uncover them, the accomplishment of this task has become increasingly difficult over time. In recent years, various new promising methods for the detection of multiple food adulterants and multiple food allergens have been developed. Some of them utilize LC-MS to identify specific marker peptides. However, these methods have yet to be validated and standardized. For this reason, the German officials have established a working group with the objective of validating methods through multilaboratory validation studies. The experts of the working group also aim for the first time to standardize validated methods and to develop general validation criteria. This manuscript will highlight the current work of the group. For this purpose, an overview is given on the principles and applications of the new mass spectrometric methods. Moreover, requirements and the present work of other institutions regarding method validation are described.

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes. [ABSTRACT FROM AUTHOR]