Insight into the influence of various cultivation regions on the identification of metabolites from Capsella bursa-pastoris via a clustering algorithm

This study focused on the regional differentiation of metabolites of C. bursa-pastoris cultivated across Korea via UHPLC‒HRMS‒based untargeted metabolomics. Extensive screening was conducted on samples collected from five distinct sites in 20 cities, resulting in the identification of 311 primary an...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Scientific reports Ročník 15; číslo 1; s. 35974 - 14
Hlavní autori:	Lee, In Young, Lee, Doo-Hee, Lee, Min Ju, Park, Ju Hong, Joo, Nami
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 15.10.2025 Nature Publishing Group Nature Portfolio
Predmet:	631/449/1659 639/638/11/296 Algorithms Antioxidants Capsella bursa-pastoris Chromatography Chromatography, High Pressure Liquid Cluster Analysis Clustering Clustering Algorithms Cultivation Flavonoids Gas flow Geographical distribution Humanities and Social Sciences Identification Machine learning Mass spectrometry Metabolites Metabolome Metabolomics Metabolomics - methods multidisciplinary Phytochemicals Republic of Korea Science Science (multidisciplinary) Scientific imaging Secondary metabolites Seeds Software Temperature UHPLC‒HRMS Variance analysis Republic of Korea United States > US UHPLC‒HRMS Metabolites Clustering Machine learning Capsella bursa-pastoris
ISSN:	2045-2322, 2045-2322
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	This study focused on the regional differentiation of metabolites of C. bursa-pastoris cultivated across Korea via UHPLC‒HRMS‒based untargeted metabolomics. Extensive screening was conducted on samples collected from five distinct sites in 20 cities, resulting in the identification of 311 primary and secondary metabolites. The samples were classified via six clustering techniques (K-means, agglomerative clustering, spectral clustering, Birch, mini-batch K-means, and bisecting K-means) via a clustering algorithm that is based on metabolite concentrations. Twenty key features that significantly influenced the clustering were extracted and validated. The classification results demonstrated a strong correlation with the geographical location of the cultivation site. C. bursa-pastoris from inland regions presented relatively high concentrations of sulfureous compounds, such as glucosinolic acid and isothiocyanate. The findings of this study provide valuable insights into the integration of machine learning techniques with untargeted metabolomics, facilitating the development of targeted phytochemical profiles.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-19391-y