Details

Autor(en) / Beteiligte

• Jandric, Zora
• Tchaikovsky, Anastassiya
• Zitek, Andreas
• Causon, Tim
• Stursa, Vaclav
• Prohaska, Thomas
• Hann, Stephan

Titel

Multivariate modelling techniques applied to metabolomic, elemental and isotopic fingerprints for the verification of regional geographical origin of Austrian carrots

Ist Teil von

• Food chemistry, 2021-02, Vol.338, p.127924-127924, Article 127924

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• •Discrimination of Austrian carrots from five different regions was achieved.•Sr isotopic, multi-elemental and metabolomic data were used separately or in combination.•Mid-level data fusion allowed highly efficient and correct classification.•One-class classification was more suitable for authentication than discriminant approaches. An exploratory study for verifying regional geographical origin of carrots from specific production regions in Austria (“Genussregionen”) was performed by combining chemical fingerprinting methods, namely n(86Sr)/n(87Sr) isotope amount ratios, multi-elemental and metabolomic pattern. Chemometric classification models were built on individual and combined datasets using (data-driven) soft independent modelling of class analogies and (orthogonal) projections to latent structures-discriminant analysis to characterise and differentiate carrots grown in five regions in Austria. A predictive ability of 97% or better (depending on the classification technique) was obtained using combined Sr isotope amount ratios and multi-elemental data. The use of data fusion strategies, in particular the mid-level option (fusion of selected variables from the different analytical platforms), allowed highly efficient (99–100%, except soft independent modelling of class analogy with 97%) and correct classification of carrot samples.