Geographical Origin Traceability of Atractylodis Macrocephalae Rhizoma Based on Chemical Composition, Chromaticity, and Electronic Nose

被引:0
|
作者
Yang, Ruiqi [1 ]
Wang, Yushi [1 ]
Wang, Jiayu [1 ]
Guo, Xingyu [1 ]
Zhao, Yuanyu [1 ]
Zhu, Keyao [1 ]
Zhu, Xintian [1 ]
Zou, Huiqin [1 ]
Yan, Yonghong [1 ]
机构
[1] Beijing Univ Chinese Med, Sch Chinese Mat Med, Beijing 102488, Peoples R China
来源
MOLECULES | 2024年 / 29卷 / 21期
关键词
Atractylodis Macrocephalae Rhizoma; origin traceability; chromaticity; electronic nose; quality evaluation; COMPONENTS;
D O I
10.3390/molecules29214991
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Atractylodis Macrocephalae Rhizoma (AMR) is a traditional Chinese medicine used for gastrointestinal diseases. With increased demand, there are more and more places of cultivation for AMR. However, the quality of AMR varies from place to place, and there is no good way to distinguish AMR from different origins at present. In this paper, we determined the content of eight chemical components including 60% ethanol extracts, essential oil, polysaccharides, atractylenolides, and atractylone, obtained the color parameters of AMR powder by colorimetry, and odor information was captured by the electronic nose, all of which were combined with machine learning to establish a rapid origin traceability method. The results of the principal component analysis of the chemical components revealed that Zhejiang AMR has a high comprehensive score and overall better quality. The Kruskal-Wallis test demonstrated that there are varying degrees of differences in chemical composition and color parameters across the different origin. However, the accuracy of the classification model is low (less than 80%), making it difficult to distinguish between different origins of AMR. The electronic nose demonstrated excellent classification performance in the traceability of AMR from different origins, with accuracy reaching more than 90% (PLS-DA: 96.88%, BPNN: 96.88%, PSO-SVM: 100%). Overall, this study clarified the quality differences of AMR among different origins, and a rapid and precise method combining machine learning was developed to trace the origin of AMR.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] Classification of geographical origin of kimchi by volatile compounds analysis using an electronic nose
    Lee, Wang-Hee
    Oh, Il-Nam
    Choi, Seunghyun
    Park, Jong-Tae
    FOOD SCIENCE AND BIOTECHNOLOGY, 2021, 30 (10) : 1313 - 1319
  • [22] Artificial neural network model- and response surface methodology-based optimization of Atractylodis Macrocephalae Rhizoma polysaccharide extraction, kinetic modelling and structural characterization
    Qiu, Junjie
    Shi, Menglin
    Li, Siqi
    Ying, Qianyi
    Zhang, Xinxin
    Mao, Xinxin
    Shi, Senlin
    Wu, Suxiang
    ULTRASONICS SONOCHEMISTRY, 2023, 95
  • [23] Effect of cultivar and environment on chemical composition and geographical traceability of Spanish olive oils
    Rey-Gimenez, Raquel
    Sanchez-Gimeno, Ana Cristina
    JOURNAL OF THE AMERICAN OIL CHEMISTS SOCIETY, 2024, 101 (04) : 371 - 382
  • [24] Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics
    Fang, Shimao
    Huang, Wen-Jing
    Wei, Yuming
    Tao, Meng
    Hu, Xin
    Li, Tiehan
    Kalkhajeh, Yusef K.
    Deng, Wei-Wei
    Ning, Jingming
    JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 2019, 99 (15) : 6937 - 6943
  • [25] Geographical Origin Traceability and Varietal Classification of Zanthoxylum Based on Mineral Profile
    Wu, Zhen
    Li, Hong
    Yang, Yong
    Tan, Hongjun
    Zhan, Yong
    Jia, Fengxia
    Li, Xiaobin
    Wang, Fuqiang
    Shipin Kexue/Food Science, 2019, 40 (16): : 213 - 219
  • [26] Geographical Origin Traceability of Songjiang Rice Based on Mineral Elements Fingerprints
    Shi C.
    Cao M.
    Hu G.
    Shipin Kexue/Food Science, 2020, 41 (16): : 300 - 306
  • [27] Traceability Analysis of Geographical Origin of Fish and Shrimp Products Based on Proteomics
    Jiang, Bing-Xue
    Zhang, Xiao-Mei
    Wang, Zhi-Hong
    Li, Zhao-Jie
    Zhao, Xue
    Xu, Jie
    Hou, Hu
    Zhao, Sa
    Zhang, Hong-Wei
    Xue, Chang-Hu
    CHINESE JOURNAL OF ANALYTICAL CHEMISTRY, 2022, 50 (04) : 613 - 622
  • [28] Geographical origin traceability of Fuji apples based on fisher discriminant method
    Chen, Jing
    Wang, Keshan
    Liu, Xiaorong
    International Agricultural Engineering Journal, 2018, (04): : 330 - 336
  • [29] Peanut origin traceability: A hybrid neural network combining an electronic nose system and a hyperspectral system
    Wang, Zi
    Yu, Yang
    Liu, Junqi
    Zhang, Qinglun
    Guo, Xiaoqin
    Yang, Yixin
    Shi, Yan
    FOOD CHEMISTRY, 2024, 447
  • [30] Traceability of Green Tea Origin: An Adaptive Gas Features Classification Network Coupled With an Electronic Nose
    Yu, Xiaozhu
    Shen, Yiqing
    IEEE SENSORS JOURNAL, 2025, 25 (04) : 7708 - 7715