The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics.
R Gil SolsonaC BoixM IbáñezJuan V SanchoPublished in: Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment (2018)
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.
Keyphrases
- high resolution mass spectrometry
- ms ms
- liquid chromatography
- ultra high performance liquid chromatography
- mass spectrometry
- gas chromatography
- tandem mass spectrometry
- machine learning
- deep learning
- simultaneous determination
- liquid chromatography tandem mass spectrometry
- amino acid
- healthcare
- social media
- human health
- high resolution