Preprint / Version 1

Texture Feature Extraction from 1H NMR Spectra for the Geographical Origin Traceability of Chinese Yam

Authors

  • Zhongyi Hu College of Computer Science and Artifical Intelligence, Wenzhou University, Wenzhou 325035, China
  • Zhenzhen Luo Zhenhai District Finance Bureau, Ningbo 315202, China; [email protected]
  • Yanli Wang National Health Commission Key Laboratory of Birth Defect Prevention, Henan Institute of Reproductive Health Science and Technology, Zhengzhou 450002, China; [email protected]
  • Qiuju Zhou College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang 464000, China; [email protected]
  • Shuangyan Liu High & New Technology Research Center, Henan Academy of Sciences, Zhengzhou 450002, China; [email protected]
  • Qiang Wang High & New Technology Research Center, Henan Academy of Sciences, Zhengzhou 450002, China; [email protected]

Keywords:

local binary pattern, support vector machine, intelligent identification, 1H NMR, geographical origin traceability, Chinese yam

Abstract

Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, 1H NMR combined intelligent “rotation-invariant uniform local binary pattern” identification was implemented for the geographical origin confirmation of geo-authentic Chinese yam (grown in Jiaozuo, Henan province) from Chinese yams grown in other locations. Our results showed that the texture feature of 1H NMR image extracted with rotation-invariant uniform local binary pattern for identification is far superior compared to the original NMR data. Furthermore, data preprocessing is necessary. Moreover, the model combining a feature extraction algorithm and support vector machine (SVM) classifier demonstrated good robustness. This approach is advantageous, as it is accurate, rapid, simple, and inexpensive. It is also suitable for the geographical origin traceability of other geographical indication agricultural products. Keywords: local binary pattern, support vector machine, intelligent identification, 1H NMR, geographical origin traceability, Chinese yam

Author Biographies

Zhongyi Hu, College of Computer Science and Artifical Intelligence, Wenzhou University, Wenzhou 325035, China

Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Project administration

Zhenzhen Luo, Zhenhai District Finance Bureau, Ningbo 315202, China; [email protected]

Validation, Formal analysis, Investigation, Data curation, Writing – original draft

Yanli Wang, National Health Commission Key Laboratory of Birth Defect Prevention, Henan Institute of Reproductive Health Science and Technology, Zhengzhou 450002, China; [email protected]

Validation, Investigation, Data curation, Funding acquisition

Qiuju Zhou, College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang 464000, China; [email protected]

Validation, Investigation, Data curation

Shuangyan Liu, High & New Technology Research Center, Henan Academy of Sciences, Zhengzhou 450002, China; [email protected]

Investigation, Funding acquisition

Qiang Wang, High & New Technology Research Center, Henan Academy of Sciences, Zhengzhou 450002, China; [email protected]

Conceptualization, Methodology, Investigation, Writing – review & editing, Supervision, Project administration, Funding acquisition

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