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Peer-reviewedCC BY 4.0Frontiers in Plant Science

Dissection of quality variation and hierarchical quality evaluation of Evodiae Fructus under multidimensional factors

  • Yuan-tong Yang1,2
  • Yi-jing Luo3
  • Yu-qin Luo2,4
  • Li-ye Pan2,4
  • Guo-wei Li2,4,5
  • Xiang-dong Chen2,4
  • Dong-mei Sun2,4
5 affiliations
  1. 1Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Formula Granule Enterprise, Guangzhou University of Chinese Medicine
  2. 2Traditional Chinese Medicine Industry and Transformation Discipline, Guangzhou University of Chinese Medicine
  3. 3Beijing University of Chinese Medicine
  4. 4Guangdong Yifang Pharmaceutical Co., Ltd., Guangdong Provincial Key Laboratory of TCM Formula Granule Enterprise
  5. 5China Pharmaceutical University
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Abstract

Introduction: Considerable quality fluctuation and heterogeneity are commonly observed in Evodiae Fructus due to multiple confounding factors. Accordingly, this study performed multi-factor analysis to explore potential key factors associated with its chemical quality, aiming to provide scientific evidence and theoretical support for the quality evaluation of this medicinal material.

Methods: First, for the HPLC-derived multi-component quantitative dataset, the Kruskal-Wallis H test followed by post-hoc Dunn’s tests with Benjamini-Hochberg FDR correction was performed, and generalized linear models (GLMs) specified with a Gamma distribution and log-link function together with random forest regression were further constructed to quantify the associations of botanical origins, geographical origins, commercial specifications, and maturity stages with the quality of Evodiae Fructus. Second, for the multi-dimensional UPLC-MS/MS metabolomic profiles, principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), Kruskal-Wallis H tests, and fold-change analysis were applied to screen differential metabolites across the predefined comparison groups, with metabolites satisfying VIP > 1, |log2FC| > log2(1.5) , and unadjusted p < 0.05 retained as differential metabolites. GNPS-based feature-based molecular networking (FBMN) was utilized to visualize the corresponding chemical space. Subsequently, Pearson correlation analysis (|r| > 0.6, P < 0.05) and random forest modeling were adopted to filter candidate core markers, which were further confirmed by receiver operating characteristic (ROC) curve analysis (AUC > 0.8). Finally, comparative back-propagation neural network (BPNN) models, with hyperparameters optimized by grid search using 10-fold cross-validation, were established to discriminate different commercial specifications of Evodiae Fructus using both pharmacopoeia-referenced indicators and the selected core biomarkers as input, and their predictive performance was evaluated on an independent test set.

Results: A significant correlation was found between commercial specifications and the differential accumulation of active constituents in Evodiae Fructus. ROC curve validation confirmed that 2-hydroxy-4-methoxy-3-(3’-methylbut-2’-enyl) quinolin-4-one and 1-methyl-[(3E,6Z,9Z,12E)-pentadeca-3,6,9,12-tetraen-1-yl]-4(1H)-quinolone, two alkyl quinolone alkaloids, could be regarded as specific potential biomarkers for quality assessment of Evodiae Fructus. The two candidate markers enable the discrimination of Evodiae Fructus with distinct specifications.

Conclusions: Collectively, this study disentangled the independent and interactive associations between commercial specification and chemical quality of Evodiae Fructus. The candidate markers we obtained enabled BPNN models to effectively discriminate different commercial specifications. These findings provide valuable references for the hierarchical quality assessment of this medicinal herb.

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Cite

BibTeXRIS
BibTeX
@article{yang2026dissection,
  title = {{Dissection of quality variation and hierarchical quality evaluation of Evodiae Fructus under multidimensional factors}},
  author = {Yuan-tong Yang and Yi-jing Luo and Yu-qin Luo and Li-ye Pan and Guo-wei Li and Xiang-dong Chen and Dong-mei Sun},
  journal = {Frontiers in Plant Science},
  year = {2026},
  month = oct,
  doi = {10.3389/fpls.2026.1942571},
  url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1942571/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Dissection of quality variation and hierarchical quality evaluation of Evodiae Fructus under multidimensional factors
AU  - Yuan-tong Yang
AU  - Yi-jing Luo
AU  - Yu-qin Luo
AU  - Li-ye Pan
AU  - Guo-wei Li
AU  - Xiang-dong Chen
AU  - Dong-mei Sun
PY  - 2026
DA  - 2026/10/28
JO  - Frontiers in Plant Science
DO  - 10.3389/fpls.2026.1942571
UR  - https://www.frontiersin.org/articles/10.3389/fpls.2026.1942571/full
AB  - Introduction: Considerable quality fluctuation and heterogeneity are commonly observed in Evodiae Fructus due to multiple confounding factors. Accordingly, this study performed multi-factor analysis to explore potential key factors associated with its chemical quality, aiming to provide scientific evidence and theoretical support for the quality evaluation of this medicinal material.  Methods: First, for the HPLC-derived multi-component quantitative dataset, the Kruskal-Wallis H test followed by post-hoc Dunn’s tests with Benjamini-Hochberg FDR correction was performed, and generalized linear models (GLMs) specified with a Gamma distribution and log-link function together with random forest regression were further constructed to quantify the associations of botanical origins, geographical origins, commercial specifications, and maturity stages with the quality of Evodiae Fructus. Second, for the multi-dimensional UPLC-MS/MS metabolomic profiles, principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), Kruskal-Wallis H tests, and fold-change analysis were applied to screen differential metabolites across the predefined comparison groups, with metabolites satisfying VIP > 1, |log2FC| > log2(1.5) , and unadjusted p < 0.05 retained as differential metabolites. GNPS-based feature-based molecular networking (FBMN) was utilized to visualize the corresponding chemical space. Subsequently, Pearson correlation analysis (|r| > 0.6, P < 0.05) and random forest modeling were adopted to filter candidate core markers, which were further confirmed by receiver operating characteristic (ROC) curve analysis (AUC > 0.8). Finally, comparative back-propagation neural network (BPNN) models, with hyperparameters optimized by grid search using 10-fold cross-validation, were established to discriminate different commercial specifications of Evodiae Fructus using both pharmacopoeia-referenced indicators and the selected core biomarkers as input, and their predictive performance was evaluated on an independent test set.  Results: A significant correlation was found between commercial specifications and the differential accumulation of active constituents in Evodiae Fructus. ROC curve validation confirmed that 2-hydroxy-4-methoxy-3-(3’-methylbut-2’-enyl) quinolin-4-one and 1-methyl-[(3E,6Z,9Z,12E)-pentadeca-3,6,9,12-tetraen-1-yl]-4(1H)-quinolone, two alkyl quinolone alkaloids, could be regarded as specific potential biomarkers for quality assessment of Evodiae Fructus. The two candidate markers enable the discrimination of Evodiae Fructus with distinct specifications.  Conclusions: Collectively, this study disentangled the independent and interactive associations between commercial specification and chemical quality of Evodiae Fructus. The candidate markers we obtained enabled BPNN models to effectively discriminate different commercial specifications. These findings provide valuable references for the hierarchical quality assessment of this medicinal herb.
SN  - 1664-462X
ER  -