AUTHOR=Zhang Pan-Pan , Gui Xin-Jing , Fan Xue-Hua , Han-Li  , Li Hai-Yang , Li Xiao-Peng , Dong Feng-Yu , Wang Yan-Li , Jing-Yao  , Shi Jun-Han , Liu Rui-Xin 

TITLE=Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1544743

DOI=10.3389/fchem.2025.1544743

ISSN=2296-2646

ABSTRACT=Amomi fructus (AF) has been used for both medicinal and food purposes for centuries. However, issues such as source mixing, substandard quality, and product adulteration often affect its efficacy. This study used E-nose (EN) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to determine and analyze the volatile organic compounds (VOCs) in AF and its counterfeit products. A total of 111 VOCs were detected by HS-GC-IMS, with 101 tentatively identified. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) identified 47 VOCs as differential markers for distinguishing authentic AF from counterfeits (VIP value >1 and P < 0.05). Based on the E-nose sensor response value and the peak volumes of the 111 VOCs, the unguided Principal Component Analysis (PCA), guided Principal Component Analysis-Discriminant Analysis (PCA-DA), and Partial Least Squares-Discriminant Analysis (PLS-DA) models were established to differentiate AF by authenticity, origin, and provenance. The authenticity identification model achieved 100.00% accuracy after PCA analysis, while the origin identification model and the provenance identification model were 95.65% (HS-GC-IMS: PLS-DA) and 98.18% (HS-GC-IMS: PCA-DA/PLS-DA), respectively. Further data-level fusion of E-nose and HS-GC-IMS significantly improved the accuracy of the origin identification model to 97.96% (PLS-DA), outperforming single-source data modeling. In conclusion, the intelligent data fusion algorithm based on E-nose and HS-GC-IMS data effectively identifies the authenticity, origin, and provenance of AF, providing a rapid and accurate method for quality evaluation.