Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae Fructus
<i>Forsythia suspensa</i> is a crucial plant resource due to its considerable edible and medicinal value. Its fruit, named Forsythiae Fructus (FF), has been widely used in Traditional Chinese Medicine (TCM). According to the fruit maturity stage, FF is categorized into GFF (green Forsyth...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/30/7/1404 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849738833722081280 |
|---|---|
| author | Qingling Xie Hanwen Yuan Shiqi Liu Ling Liang Jiangyi Luo Mengyun Wang Bin Li Wei Wang |
| author_facet | Qingling Xie Hanwen Yuan Shiqi Liu Ling Liang Jiangyi Luo Mengyun Wang Bin Li Wei Wang |
| author_sort | Qingling Xie |
| collection | DOAJ |
| description | <i>Forsythia suspensa</i> is a crucial plant resource due to its considerable edible and medicinal value. Its fruit, named Forsythiae Fructus (FF), has been widely used in Traditional Chinese Medicine (TCM). According to the fruit maturity stage, FF is categorized into GFF (green Forsythiae Fructus) and RFF (ripe Forsythiae Fructus). In this study, metabolomics based on UPLC-Q/Orbitrap MS and HS-GC-MS, combined with chemometric methods, was employed to differentiate GFF from RFF and identify potential differential metabolites. Additionally, the mid-level data fusion method was employed to integrate data from both techniques, and the performance of the OPLS-DA model (R<sup>2</sup>Y = 0.986, Q<sup>2</sup> = 0.974) surpassed that of the single HS-GC-MS technique (R<sup>2</sup>Y = 0.968, Q<sup>2</sup> = 0.930). Moreover, using the criteria of VIP > 1 and <i>p</i>-value < 0.05, 30 differential compounds were selected via mid-level data fusion, compared to the initial 61 differential compounds identified by single techniques, effectively reducing data noise and eliminating irrelevant variables. This study provides a comprehensive analysis of volatile and non-volatile compounds in FF, offering valuable insights into quality control and clinical differentiation between GFF and RFF. The findings highlight the potential use of multi-technology metabolomics in the quality control of TCM and offer new perspectives for future research on medicinal plants. |
| format | Article |
| id | doaj-art-9c33c9804b714a048c26a076641c5dfd |
| institution | DOAJ |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-9c33c9804b714a048c26a076641c5dfd2025-08-20T03:06:27ZengMDPI AGMolecules1420-30492025-03-01307140410.3390/molecules30071404Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae FructusQingling Xie0Hanwen Yuan1Shiqi Liu2Ling Liang3Jiangyi Luo4Mengyun Wang5Bin Li6Wei Wang7TCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, ChinaTCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, ChinaTCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, ChinaTCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, ChinaTCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, ChinaTCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, ChinaTCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, ChinaTCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China<i>Forsythia suspensa</i> is a crucial plant resource due to its considerable edible and medicinal value. Its fruit, named Forsythiae Fructus (FF), has been widely used in Traditional Chinese Medicine (TCM). According to the fruit maturity stage, FF is categorized into GFF (green Forsythiae Fructus) and RFF (ripe Forsythiae Fructus). In this study, metabolomics based on UPLC-Q/Orbitrap MS and HS-GC-MS, combined with chemometric methods, was employed to differentiate GFF from RFF and identify potential differential metabolites. Additionally, the mid-level data fusion method was employed to integrate data from both techniques, and the performance of the OPLS-DA model (R<sup>2</sup>Y = 0.986, Q<sup>2</sup> = 0.974) surpassed that of the single HS-GC-MS technique (R<sup>2</sup>Y = 0.968, Q<sup>2</sup> = 0.930). Moreover, using the criteria of VIP > 1 and <i>p</i>-value < 0.05, 30 differential compounds were selected via mid-level data fusion, compared to the initial 61 differential compounds identified by single techniques, effectively reducing data noise and eliminating irrelevant variables. This study provides a comprehensive analysis of volatile and non-volatile compounds in FF, offering valuable insights into quality control and clinical differentiation between GFF and RFF. The findings highlight the potential use of multi-technology metabolomics in the quality control of TCM and offer new perspectives for future research on medicinal plants.https://www.mdpi.com/1420-3049/30/7/1404Forsythiae FructusLC-MSHS-GC-MSmid-level data fusion |
| spellingShingle | Qingling Xie Hanwen Yuan Shiqi Liu Ling Liang Jiangyi Luo Mengyun Wang Bin Li Wei Wang Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae Fructus Molecules Forsythiae Fructus LC-MS HS-GC-MS mid-level data fusion |
| title | Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae Fructus |
| title_full | Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae Fructus |
| title_fullStr | Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae Fructus |
| title_full_unstemmed | Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae Fructus |
| title_short | Mid-Level Data Fusion Techniques of LC-MS and HS-GC-MS for Distinguishing Green and Ripe Forsythiae Fructus |
| title_sort | mid level data fusion techniques of lc ms and hs gc ms for distinguishing green and ripe forsythiae fructus |
| topic | Forsythiae Fructus LC-MS HS-GC-MS mid-level data fusion |
| url | https://www.mdpi.com/1420-3049/30/7/1404 |
| work_keys_str_mv | AT qinglingxie midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus AT hanwenyuan midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus AT shiqiliu midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus AT lingliang midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus AT jiangyiluo midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus AT mengyunwang midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus AT binli midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus AT weiwang midleveldatafusiontechniquesoflcmsandhsgcmsfordistinguishinggreenandripeforsythiaefructus |