Multivariate modelling techniques applied to metabolomic, elemental and isotopic fingerprints for the verification of regional geographical origin of Austrian carrots. (15th February 2021)

• Record Type: Journal Article
• Authors: Jandric, Zora; Tchaikovsky, Anastassiya; Zitek, Andreas; Causon, Tim; Stursa, Vaclav; Prohaska, Thomas; Hann, Stephan
• Is Part Of: Food chemistry. Volume 338(2021)
• Publication Date: 2021-02-15
• Subjects: Carrots -- Fingerprinting -- Metabolomics -- Multi-element -- Origin -- Strontium isotopes -- Chemometric classification; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.127924
• Languages: English
• ISSNs: 0308-8146

Abstract

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.