Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods
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 d...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2025.1544743/full |
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| author | Pan-Pan Zhang Xin-Jing Gui Xin-Jing Gui Xin-Jing Gui Xue-Hua Fan Han-Li Hai-Yang Li Xiao-Peng Li Feng-Yu Dong Yan-Li Wang Jing-Yao Jing-Yao Jun-Han Shi Jun-Han Shi Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu |
| author_facet | Pan-Pan Zhang Xin-Jing Gui Xin-Jing Gui Xin-Jing Gui Xue-Hua Fan Han-Li Hai-Yang Li Xiao-Peng Li Feng-Yu Dong Yan-Li Wang Jing-Yao Jing-Yao Jun-Han Shi Jun-Han Shi Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu |
| author_sort | Pan-Pan Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c688d23111df4d2f99c3101c6ba21953 |
| institution | Kabale University |
| issn | 2296-2646 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Chemistry |
| spelling | doaj-art-c688d23111df4d2f99c3101c6ba219532025-02-06T11:52:59ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462025-02-011310.3389/fchem.2025.15447431544743Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methodsPan-Pan Zhang0Xin-Jing Gui1Xin-Jing Gui2Xin-Jing Gui3Xue-Hua Fan4 Han-Li5Hai-Yang Li6Xiao-Peng Li7Feng-Yu Dong8Yan-Li Wang9 Jing-Yao10 Jing-Yao11Jun-Han Shi12Jun-Han Shi13Rui-Xin Liu14Rui-Xin Liu15Rui-Xin Liu16Rui-Xin Liu17Rui-Xin Liu18School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, ChinaDepartment of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaHenan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, ChinaHenan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, ChinaSchool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, ChinaSchool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, ChinaBeijing University of Chinese Medicine, Beijing, ChinaSchool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, ChinaSchool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, ChinaDepartment of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaDepartment of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaHenan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, ChinaDepartment of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaHenan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, ChinaSchool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, ChinaDepartment of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaHenan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, ChinaHenan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, ChinaCo-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, ChinaAmomi 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.https://www.frontiersin.org/articles/10.3389/fchem.2025.1544743/fullAmomi fructusENHS-GC-IMSintelligent data fusionquality identification |
| spellingShingle | Pan-Pan Zhang Xin-Jing Gui Xin-Jing Gui Xin-Jing Gui Xue-Hua Fan Han-Li Hai-Yang Li Xiao-Peng Li Feng-Yu Dong Yan-Li Wang Jing-Yao Jing-Yao Jun-Han Shi Jun-Han Shi Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu Rui-Xin Liu Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods Frontiers in Chemistry Amomi fructus EN HS-GC-IMS intelligent data fusion quality identification |
| title | Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods |
| title_full | Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods |
| title_fullStr | Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods |
| title_full_unstemmed | Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods |
| title_short | Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods |
| title_sort | quality identification of amomi fructus using e nose hs gc ims and intelligent data fusion methods |
| topic | Amomi fructus EN HS-GC-IMS intelligent data fusion quality identification |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2025.1544743/full |
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