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From Nontargeted to Targeted Analysis: Feature Selection in the Differentiation of Truffle Species ( Tuber spp.) Using 1 H NMR Spectroscopy and Support Vector Machine.

Thorsten MixJasmin JanneschützRami LudwigJulia EichbaumMarkus FischerThomas Hackl
Published in: Journal of agricultural and food chemistry (2023)
The price of different truffle types varies according to their culinary value, sometimes by more than a factor of 10. Nonprofessionals can hardly distinguish visually the species within the white or black truffles, making the possibility of food fraud very easy. Therefore, the identification of different truffle species ( Tuber spp.) is an analytical task that could be solved in this study. The polar extract from a total of 80 truffle samples was analyzed by 1 H NMR spectroscopy in combination with chemometric methods covering five commercially relevant species. All classification models were validated applying a repeated nested cross-validation. In direct comparison, the two very similar looking and closely related black representatives Tuber melanosporum and Tuber indicum could be classified 100% correctly. The most expensive truffle Tuber magnatum could be distinguished 100% from the other relevant white truffle Tuber borchii . In addition, signals for a potential Tuber borchii and a potential Tuber melanosporum marker for targeted approaches could be detected, and the corresponding molecules were identified as betaine and ribonate. A model covering all five truffle species Tuber aestivum , Tuber borchii , Tuber indicum , Tuber magnatum , and Tuber melanosporum was able to correctly discriminate between each of the species.
Keyphrases
  • machine learning
  • deep learning
  • oxidative stress
  • mass spectrometry
  • climate change
  • data analysis