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This paper describes a new non-targeted method (NTM) for distinguishing spelt from wheat, which aids in food fraud detection and authenticity testing. A spectral fingerprint was obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Neural network (CNN) models are built using a nested cross validation (NCV) approach 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 CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. The method was validated using artificially mixed spectra from samples of processed spelt bread and flour, comprising of eleven untypical spelt, and six old wheat cultivars, which were not part of model building. The results showed that based on the same chemometric approach, the non-targeted method is reliable enough to be used on a wider range of cultivars and their mixes.

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 The popularity of spelt (Triticum spelta) is growing, and there is a need for a rapid method to authenticate it, and especially to detect adulteration by common wheat. In this study, an attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) method was developed, and combined with orthogonal partial least square discriminant analysis (OPLS-DA). A validation set of spelt and common wheat provided good discrimination between the two cereals. Also, adulterated samples were prepared as an external validation set, and the developed OPLS-DA model identified the spelt-common wheat mixtures as a separate class highlighting its strong predictive power. 

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