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

Bernadette Richter; Stephanie Gurk; Deniz Wagner; Michael Bockmayr; Markus Fischer

doi:10.1016/j.foodchem.2019.01.105

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

Standard

Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination. / Richter, Bernadette; Gurk, Stephanie; Wagner, Deniz; Bockmayr, Michael; Fischer, Markus.

In: FOOD CHEM, Vol. 286, 15.07.2019, p. 475-482.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Richter, B, Gurk, S, Wagner, D, Bockmayr, M & Fischer, M 2019, 'Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination', FOOD CHEM, vol. 286, pp. 475-482. https://doi.org/10.1016/j.foodchem.2019.01.105

APA

Richter, B., Gurk, S., Wagner, D., Bockmayr, M., & Fischer, M. (2019). Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination. FOOD CHEM, 286, 475-482. https://doi.org/10.1016/j.foodchem.2019.01.105

Vancouver

Richter B, Gurk S, Wagner D, Bockmayr M, Fischer M. Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination. FOOD CHEM. 2019 Jul 15;286:475-482. https://doi.org/10.1016/j.foodchem.2019.01.105

Bibtex

@article{2ba2783e99c841aa8828d8db79627848,

title = "Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination",

abstract = "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.",

keywords = "Journal Article",

author = "Bernadette Richter and Stephanie Gurk and Deniz Wagner and Michael Bockmayr and Markus Fischer",

year = "2019",

month = jul,

day = "15",

doi = "10.1016/j.foodchem.2019.01.105",

language = "English",

volume = "286",

pages = "475--482",

journal = "FOOD CHEM",

issn = "0308-8146",

publisher = "Elsevier Limited",

}

RIS

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

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 -