Multivariate modelling techniques applied to metabolomic, elemental and isotopic fingerprints for the verification of regional geographical origin of Austrian carrots. (15th February 2021) - Jandric, Zora; Tchaikovsky, Anastassiya; Zitek, Andreas; Causon, Tim; Stursa, Vaclav; Prohaska, Thomas; Hann, Stephan
Highlights: 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. Abstract: 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 ( 86 Sr)/ n ( 87 Sr) 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.