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The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics
dc.contributor.author | Gil Solsona, Ruben | |
dc.contributor.author | Boix Sales, Clara | |
dc.contributor.author | Ibáñez, Maria | |
dc.contributor.author | Sancho, Juan V | |
dc.date.accessioned | 2018-09-17T10:00:17Z | |
dc.date.available | 2018-09-17T10:00:17Z | |
dc.date.issued | 2018-01-17 | |
dc.identifier.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. 395-403 | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/176077 | |
dc.description.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. | ca_CA |
dc.format.extent | 9 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Taylor & Francis | ca_CA |
dc.relation.isPartOf | Food Additives & Contaminants: Part A (2018), v. 35, issue 3 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/CNE/1.0/ | * |
dc.subject | Almond | ca_CA |
dc.subject | Untargeted metabolomics | ca_CA |
dc.subject | UHPLC | ca_CA |
dc.subject | High-resolution mass spectrometry | ca_CA |
dc.subject | PLS-DA | ca_CA |
dc.title | The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1080/19440049.2017.1416679 | |
dc.relation.projectID | Generalitat Valenciana [Group of Excellence Prometeo II/2017/023]; Universitat Jaume I [UJI-B2016-10]. | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | https://www.tandfonline.com/doi/full/10.1080/19440049.2017.1416679?scroll=top&needAccess=true | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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