Instrument comparability of non-targeted UHPLC-HRMS for wine authentication. (February 2023)

• Record Type: Journal Article
• Authors: Ehlers, Mona; Uttl, Leos; Riedl, Janet; Raeke, Julia; Westkamp, Imke; Hajslova, Jana; Brockmeyer, Jens; Fauhl-Hassek, Carsten
• Is Part Of: Food control. Volume 144(2023)
• Publication Date: 2023-02
• Subjects: Food authenticity -- Grape variety -- Metabolomics -- Harmonization -- Inter-laboratory comparison -- Multivariate statistics; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2022.109360
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Instrument comparability is a major prerequisite for the harmonization of non-targeted analytical approaches for food authentication. While non-targeted liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful and widely used technique in authenticity studies, inter-laboratory or instrument comparison exercises are rare. For the first time, in the present study a comparison of two LC-MS platforms using identical setups and conditions except for the mass spectrometer (Orbitrap Q Exactive™ Focus vs. Plus) was carried out in order to assess the impact of instrumental differences on the classification of grape varieties in wine. A set of 201 red and white wine samples comprising 10 grape varieties was analyzed with both platforms and subjected to multivariate statistical analysis. Using unsupervised and supervised methods, the same correlation pattern between samples was observed with both platforms. Partial Least Squares – Discriminant Analysis (PLS-DA) classification models achieved comparable accuracies in the internal validation (90–95%). The percentage of common discriminating features was about 46–69% between both platforms. The common features were used to merge data from both instruments and joint PLS-DA models produced excellent results. The prediction of data from one platform with the model from the other platform, as well as a model using data from both platforms, resulted in correct classification rates of 92–98%. Putative identification of common features was performed to determine potential marker substances for grape varieties. With this study, a first step towards harmonization of non-targeted LC-MS approaches for wine authentication is taken, advancing their potential application in official food control. Highlights: Instrument comparison of non-targeted UHPLC-HRMS for wine authentication. variety classification of 201 commercial wines covering natural product variation. Comparable PLS-DA results for both instruments, joint models >90% accuracy. 46–69% of discriminating features common between both platforms. Tentative identification of potential marker substances.