The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics
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Other documents of the author: Gil Solsona, Ruben; Boix Sales, Clara; Ibáñez, Maria; Sancho, Juan V
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comunitat-uji-handle2:10234/33596
comunitat-uji-handle3:10234/33597
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https://doi.org/10.1080/19440049.2017.1416679 |
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Title
The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomicsDate
2018-01-17Publisher
Taylor & FrancisBibliographic citation
GIL SOLSONA, Rubén; BOIX SALES, Clara; IBÁÑEZ MARTÍNEZ, María; SANCHO LLOPIS, Juan Vicente (2018). The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics. Food Additives & Contaminants: Part A, v. 35, issue 3, p. 395-403Type
info:eu-repo/semantics/articlePublisher version
https://www.tandfonline.com/doi/full/10.1080/19440049.2017.1416679?scroll=top&ne ...Version
info:eu-repo/semantics/publishedVersionSubject
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) ... [+]
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. [-]
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Food Additives & Contaminants: Part A (2018), v. 35, issue 3Investigation project
Generalitat Valenciana [Group of Excellence Prometeo II/2017/023]; Universitat Jaume I [UJI-B2016-10].Rights
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
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