The agronomic production systems may affect the levels of food metabolites. Metabolomics approaches have been applied as useful tool for the characterization of fruit metabolome. In this study, metabolomics techniques were used to assess the differences in phytochemical composition between goldenberry samples produced by organic and conventional systems. To verify that the organic samples were free of pesticides, individual pesticides were analyzed. Principal component analysis showed a clear separation of goldenberry samples from two different farming systems. Via targeted metabolomics assays, whereby carotenoids and ascorbic acid were analyzed, not statistical differences between both crops were found. Conversely, untargeted metabolomics allowed us to identify two withanolides and one fatty acyl glycoside as tentative metabolites to differentiate goldenberry fruits, recording organic fruits higher amounts of these compounds than conventional samples. Hence, untargeted metabolomics technology could be suitable to research differences on phytochemicals under different agricultural management practices and to authenticate organic products.
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http://dx.doi.org/10.1016/j.foodchem.2017.10.026 | DOI Listing |
J Fungi (Basel)
January 2025
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai Academy of Animal and Veterinary Science, Xining 810016, China.
() has garnered increasing attention as an ingredient in both the pharmaceutical and food industries. Depending on the drying method, the accumulation of metabolites can greatly affect the quality. This research employed an untargeted metabolomics (LC-MS/MS) strategy to elucidate the similarities and differences in the morphological characteristics, microstructure, antioxidant capacity, and metabolic profiles of subjected to three distinct drying methods: natural air-drying (YG), oven-drying (HG), and vacuum freeze-drying (DG).
View Article and Find Full Text PDFJ Fungi (Basel)
January 2025
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai Academy of Animal and Veterinary Science, Qinghai University, Xining 810016, China.
is a fungus that is cultured through fermentation from wild Chinese cordyceps. While studies have examined its metabolites, the evaluation of its antioxidant capacity remains to be conducted. The antioxidant results of indicate that the ferric ion-reducing antioxidant power (FRAP), antioxidant capacity (2.
View Article and Find Full Text PDFMetabolites
January 2025
Department of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Blood microsampling (BμS) has recently emerged as an interesting approach in the analysis of endogenous metabolites but also in metabolomics applications. Their non-invasive way of use and the simplified logistics that they offer renders these technologies highly attractive in large-scale studies, especially the novel quantitative microsampling approaches such as VAMs or qDBS. Herein, we investigate the potential of BµS devices compared to the conventional plasma samples used in global untargeted mass spectrometry-based metabolomics of blood.
View Article and Find Full Text PDFMetabolites
January 2025
Zhejiang Institute of Subtropical Crops, Zhejiang Academy of Agricultural Sciences, Wenzhou 325005, China.
Gardeniae Fructus (GF) has been widely used as both food and medicinal purposes for thousands of years, but their antioxidant properties and potential metabolite biomarkers remain unclear. : The purposes of this study were to examine antioxidant activities of 21 GF varieties from different geographical origins in China and identify potential biomarkers of antioxidant properties using an untargeted LC-MS-based metabolomics approach. : The results demonstrate that metabolomics had the ability to trace the geographical origins of GF.
View Article and Find Full Text PDFMetabolites
January 2025
Sport Coaching College, Beijing Sport University, Beijing 100084, China.
Objective: This study aimed to explore the molecular response mechanisms of differential blood metabolites before and after 8 weeks of threshold and polarized training models using metabolomics technology combined with changes in athletic performance.
Methods: Twenty-four male rowers aged 14-16 were randomly divided into a THR group and a POL group (12 participants each). The THR group followed a threshold training model (72%, 24%, and 4% of training time in low-, moderate-, and high-intensity zones, respectively), while the POL group followed a polarized training model (78%, 8%, and 14% training-intensity distribution).
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