METHODS article
Front. Nutr.
Sec. Food Chemistry
Untargeted Metabolomics analysis of Physalis pubescens L. with 1 respect to different varieties
Provisionally accepted- 1Institute of Food Processing, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- 2Harbin university of commerce, Harbin, China
- 3Harbin university of commerce, 哈尔滨, China
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Abstract 27 Physalis pubescens L. is a nutritious fruit with recognized pharmacological value, yet 28 its comprehensive metabolomic profile remains unexplored. This study aimed to 29 investigate the metabolomic differences among three distinct varieties of P. pubescens, 30 with a focus on the influence of fruit size. An untargeted metabolomics approach 31 employing UPLC-ESI-MS/MS was utilized. Multivariate statistical analyses, 32 including PCA and PLS-DA, revealed a clear separation in metabolic profiles, 33 primarily driven by fruit size. Comparative analysis between large-fruited variety B 34 and small-fruited varieties S and T identified 67 significant differential metabolites. 35 Notably, the flavonoid quercetin was not detected in large-fruited variety B under our 36 analytical conditions, and the relative content of most phenylpropanoid metabolites 37 was significantly lower in large-fruited variety compared to small-fruited varieties. 38 Conversely, 17 metabolites, including certain amino acids and riboflavin, were 39 up-regulated in large-fruited variety. Pathway analysis highlighted riboflavin 40 metabolism as a key distinguishing pathway. Our findings demonstrate that fruit size 41 may be a major factor influencing the phytochemical composition of P. pubescens. 42 The novelty of this work lies in establishing fruit size as a major factor shaping the 43 phytochemical composition of P. pubescens. These findings provide a metabolic 44 foundation for selecting varieties with desired nutraceutical properties and for guiding 45 future quality control and breeding programs.
Keywords: P. pubescens, LC-MS, untargeted metabolomics, differential metabolites, Metabolic pathways
Received: 16 May 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 Yan, Li, Chen, Shan, Zhang, Gao and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: 
Yang  Gao, gao20031026@163.com
Bin  Liu, 57277345@qq.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
