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Food Authentication: Multi-elemental analysis of white asparagus for provenance discrimination

Richter, Bernadette, Gurk, Stephanie, Wagner, Deniz, Bockmayr, Michael, Fischer, Markus
Food chemistry 2019
asparagus spears, atomic absorption spectrometry, cobalt, lithium, prediction, product authenticity, provenance, rare earth elements, rubidium, strontium, support vector machines, uranium, China, Germany, Greece, Netherlands, Peru, Poland, Spain
Prediction of the geographic origin of white asparagus was realized using inductively coupled plasma mass spectrometry (ICP-MS) and machine learning techniques. The elemental profile of 319 asparagus samples originating from Germany, Poland, the Netherlands, Greece, Spain, China and Peru was determined. Using a support vector machine (SVM) combined with nested cross-validation, a prediction accuracy of 91.2% was achieved when classifying the country of origin. Accuracy can be increased up to 98% on subsets of samples with high SVM prediction scores. Most relevant elements for provenance discrimination were lithium, cobalt, rubidium, strontium, uranium and the rare earth elements. In addition, the multi-elemental method provided specific fingerprints of asparagus cultivation sites as German samples could be assigned correctly with an accuracy of 82.6%. Asparagus variety and harvest year had no significant influence on provenance distinction, which further underlines the robustness of this study.