Potential and limitations of non-targeted fingerprinting for authentication of food in official control

Potential of mass spectrometry metabolomics for chemical food safety

This review aims to describe the most significant applications of mass spectrometry-based metabolomics in the field of chemical food safety. A particular discussion of all the different analytical steps involved in the metabolomics workflow (sample preparation, mass spectrometry analytical platform and data processing) will be addressed.

1H NMR profiling as an approach to differentiate conventionally and organically grown tomatoes

This study describes the approach of (1)H NMR profiling for the authentication of organically produced tomatoes (Solanum lycopersicum). Overall, 361 tomato samples of two different cultivars and four different producers were regularly analyzed during a 7 month period. The results of principal component analysis showed a significant trend for the separation between organically and conventionally produced tomatoes (p < 0.001 using the t test). Linear discriminant analysis demonstrated good discrimination between the growing regimens, and external validation showed 100% correctly classified tomato samples. Further validation studies, however, also disclosed unexpected differences between individual producers, which interfere with the aim of predicting the cultivation method, yet the results indicate significant differences between (1)H NMR spectra of organically and conventionally grown tomatoes.

Validated modeling for German white wine varietal authentication based on headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry fingerprinting

An untargeted analytical approach combined with chemometrics using the volatiles of German white wine was investigated regarding the usefulness for verifying botanical origin. A total of 198 wine samples of Riesling, Müller-Thurgau, Silvaner, Pinot Gris, and Pinot Blanc were examined applying headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry. The resultant three-dimensional raw data were processed by available metabolomics software. After data treatment, a partial least-squares discriminant analysis (PLS-DA) model was validated. External samples were correctly classified for 97% Silvaner, 93% Riesling, 91% Pinot Gris/Blanc, and 80% Müller-Thurgau. This model was related to monoterpenoids, C13-norisoprenoids, and esters. Further, 100% prediction for a two-class model of Riesling versus Pinot Gris/Blanc was confirmed by 74 additional samples measured independently. Hence, the strategy applied was, in particular, reliable and relevant for white wine varietal classification. In addition, the superior classification performance of the Riesling class was revealed.