Preliminary authentication of apple juices using untargeted UHPLC-HRMS analysis combined to chemometrics. (September 2022)

• Record Type: Journal Article
• Authors: Dinis, Katy; Tsamba, Lucie; Thomas, Freddy; Jamin, Eric; Camel, Valérie
• Is Part Of: Food control. Volume 139(2022)
• Publication Date: 2022-09
• Subjects: Food authenticity -- High resolution mass spectrometry -- Liquid chromatography -- Metabolomics -- PCA -- PLS-DA -- OPLS-DA; 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.2022.109098
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: In this work, apple juice samples from different farming and production processes (direct and concentrated juices; organic and conventional juices) were analyzed by ultra-high performance liquid chromatography coupled to high resolution mass spectrometry (UHPLC-HRMS). A workflow was developed and implemented for data processing using the Workflow4Metabolomics (W4M) platform. First, features were detected using XCMS, and next data filtration steps were applied leading to the removal of nearly 50% of the detected features. Intra- and inter-batch correction was then performed, followed by chemometric tools (PCA, PLS-DA, OPLS-DA, ANOVA). The developed approach successfully discriminated apple juice samples in two distinct scenarios simultaneously (direct vs. concentrated juices and organic vs. conventional juices). PCA highlighted the reproducibility of the method and confirmed the efficiency of batch corrections. OPLS-DA models showed good quality metrics, particularly after feature selection for organic vs. conventional juices discrimination (almost 80% of predictive ability). Based on ANOVA and OPLS-DA results, 24 features were retained as significantly discriminant. Among them, some compounds were identified as amino-acids and derivatives, using additional MS/MS experiments and online databases. An independent data set was used to evaluate their potential as marker compounds, with promising results obtained. Further investigation is needed to validate such an untargeted method and its routine application to detect apple juice adulteration and confirm its authenticity. Highlights: Development of an UHPLC-HRMS metabolomics approach with great potential in juice authentication. Discrimination between sample groups in two distinct authentication applications. Relevant markers selected by OPLS-DA and ANOVA were tentatively identified. Main discriminant compounds were identified as amino-acids and derivatives.