IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0282429.html
   My bibliography  Save this article

Origin identification of Cornus officinalis based on PCA-SVM combined model

Author

Listed:
  • Yueqiang Jin
  • Bing Liu
  • Chaoning Li
  • Shasha Shi

Abstract

Infrared spectroscopy can quickly and non-destructively extract analytical information from samples. It can be applied to the authenticity identification of various Chinese herbal medicines, the prediction of the mixing amount of defective products, and the analysis of the origin. In this paper, the spectral information of Cornus officinalis from 11 origins was used as the research object, and the origin identification model of Cornus officinalis based on mid-infrared spectroscopy was established. First, principal component analysis was used to extract the absorbance data of Cornus officinalis in the wavenumber range of 551~3998 cm–1. The extracted principal components contain more than 99.8% of the information of the original data. Second, the extracted principal component information was used as input, and the origin category was used as output, and the origin identification model was trained with the help of support vector machine. In this paper, this combined model is called PCA-SVM combined model. Finally, the generalization ability of the PCA-SVM model is evaluated through an external test set. The three indicators of Accuracy, F1-Score, and Kappa coefficient are used to compare this model with other commonly used classification models such as naive Bayes model, decision trees, linear discriminant analysis, radial basis function neural network and partial least square discriminant analysis. The results show that PCA-SVM model is superior to other commonly used models in accuracy, F1 score and Kappa coefficient. In addition, compared with the SVM model with full spectrum data, the PCA-SVM model not only reduces the redundant variables in the model, but also has higher accuracy. Using this model to identify the origin of Cornus officinalis, the accuracy rate is 84.8%.

Suggested Citation

  • Yueqiang Jin & Bing Liu & Chaoning Li & Shasha Shi, 2023. "Origin identification of Cornus officinalis based on PCA-SVM combined model," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-20, February.
  • Handle: RePEc:plo:pone00:0282429
    DOI: 10.1371/journal.pone.0282429
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282429
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282429&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0282429?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rui Gao & Cheng Chen & Hang Wang & Chen Chen & Ziwei Yan & Huijie Han & Fangfang Chen & Yan Wu & Zhiao Wang & Yuxiu Zhou & Rumeng Si & Xiaoyi Lv, 2020. "Classification of multicategory edible fungi based on the infrared spectra of caps and stalks," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-14, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0282429. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.