Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination
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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.
in: FOOD CHEM, Jahrgang 286, 15.07.2019, S. 475-482.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination
AU - Richter, Bernadette
AU - Gurk, Stephanie
AU - Wagner, Deniz
AU - Bockmayr, Michael
AU - Fischer, Markus
N1 - Copyright © 2019 Elsevier Ltd. All rights reserved.
PY - 2019/7/15
Y1 - 2019/7/15
N2 - 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.
AB - 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.
KW - Journal Article
U2 - 10.1016/j.foodchem.2019.01.105
DO - 10.1016/j.foodchem.2019.01.105
M3 - SCORING: Journal article
C2 - 30827635
VL - 286
SP - 475
EP - 482
JO - FOOD CHEM
JF - FOOD CHEM
SN - 0308-8146
ER -