UHPLC–HRMS-based tissue untargeted metabolomics study of naringin and hesperidin after dietary supplementation in chickens. (15th December 2018)

• Record Type: Journal Article
• Authors: Baira, Eirini; Dagla, Ioanna; Siapi, Eleni; Zoumpoulakis, Panagiotis; Simitzis, Panagiotis; Goliomytis, Michael; Deligeorgis, Stelios G.; Skaltsounis, Alexios-Leandros; Gikas, Evagelos
• Is Part Of: Food chemistry. Volume 269(2018)
• Page Start: 276
• Page End: 285
• Publication Date: 2018-12-15
• Subjects: UHPLC–HRMS ultra-high performance liquid chromatography–high resolution mass spectrometry; Metabolomics -- R statistical language -- Normalization -- MVA -- Software tools -- Online databases; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2018.06.146
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Metabolic changes in chickens tissues in response to naringin and hesperidin. A new comprehensive pipeline for untargeted metabolomics was proposed. Multiple internal standards, clustering validation and statistical scores were used. 3 significant variables discriminated the naringin group from control group. 13 significant variables discriminated the hesperidin group from control group. Abstract: To date numerous metabolomic studies have been performed in order to characterize nutritional intervention studies. The aim of the current study was to present a comprehensive pipeline for characterizing the metabolic changes that occur in chickens tissues in response to naringin and hesperidin dietary supplementation. Forty-nine chickens were randomly divided into 3 groups: the first one fed with diet supplemented with naringin, the second with hesperidin whereas the control group was fed by commercial basal diet. After 30 days of administration chicken muscle samples were analyzed by UHPLC–HRMS whereas data were analyzed by the proposed pipeline. Three significant variables were detected to discriminate the control from the group after naringin administration and thirteen variables after hesperidin supplementation. Furthermore, a more detailed pipeline (encompassing multiple internal standards, internal validation of the clustering, extended statistical significance scores and multiple identification procedures) has been proposed aiming towards a more accurate untargeted analysis.