The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics

ABSTRACT

The aim of this study was to use an untargeted UHPLC-HRMS-based metabolomics approach allowing discrimination between almonds based on their origin and variety. Samples were homogenised, extracted with ACN:H₂O (80:20) containing 0.1% HCOOH and injected in a UHPLC-QTOF instrument in both positive and negative ionisation modes. Principal component analysis (PCA) was performed to ensure the absence of outliers. Partial least squares – discriminant analysis (PLS-DA) was employed to create and validate the models for country (with five different compounds) and variety (with 20 features), showing more than 95% accuracy. Additional samples were injected and the model was evaluated with blind samples, with more than 95% of samples being correctly classified using both models. MS/MS experiments were carried out to tentatively elucidate the highlighted marker compounds (pyranosides, peptides or amino acids, among others). This study has shown the potential of high-resolution mass spectrometry to perform and validate classification models, also providing information concerning the identification of the unexpected biomarkers which showed the highest discriminant power.

GRAPHICAL ABSTRACT

KEYWORDS:

• Almond
• untargeted metabolomics
• UHPLC
• high-resolution mass spectrometry
• PLS-DA

Acknowledgments

The authors acknowledge the support from Generalitat Valenciana (Group of Excellence Prometeo II/2017/023). This work has also been developed with financial support from Universitat Jaume I (UJI-B2016-10).

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data can be accessed here.

Additional information

Funding

This work was supported by the Generalitat Valenciana [Group of Excellence Prometeo II/2017/023]; Universitat Jaume I [UJI-B2016-10].