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Related Experiment Video

Updated: Jun 13, 2026

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Published on: September 2, 2020

[Sort similarity based on computational method for chemical fingerprinting].

Jinghua Zhong1, Xiaorong Hou, Xiaohui Fan

  • 1Pharmaceutical Informatics Institute, Zhejiang University, Hangzhou 310058, China.

Zhongguo Zhong Yao Za Zhi = Zhongguo Zhongyao Zazhi = China Journal of Chinese Materia Medica
|May 11, 2010
PubMed
Summary

This study introduces a new method for chemical fingerprinting analysis to improve sample classification. The novel approach enhances the accuracy of distinguishing between similar chromatograms, aiding in quality control.

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Pharmacognosy

Background:

  • Current chemical fingerprinting methods struggle with classifying similar chromatograms.
  • Inadequate performance in distinguishing between different sample classes with overlapping chemical profiles.

Purpose of the Study:

  • To develop a novel sort similarity measure for improved chemical fingerprinting.
  • To address limitations in current whole similarity determining methods.
  • To enhance sample classification accuracy for complex natural products.

Main Methods:

  • Feature extraction from chromatograms for sample classification.
  • Development of an inter-class similarity measure to assess chemical composition fluctuations within a class.
  • Application of the proposed method to High-Performance Liquid Chromatography (HPLC) fingerprints of Sarcandra glabra.

Main Results:

  • Successfully classified different parts of Sarcandra glabra (aerial vs. whole).
  • Effectively represented chemical fluctuations among samples of the same sort.
  • Demonstrated improved discrimination capabilities compared to traditional methods.

Conclusions:

  • The proposed sort similarity measure offers a more robust approach to chemical fingerprinting.
  • This method enhances the ability to classify samples and understand intra-class variability.
  • Applicable for quality evaluation and control of herbal medicines and natural products.