Analysis of GC&#215;GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of <i>Curcuma wenyujin</i>

Xinyue Yang; Yingyu Sima; Xuhuai Luo; Yaping Li; Min He

doi:10.1016/j.jpha.2024.01.004

Volume 14 Issue 4

Apr. 2024

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Article Navigation > Journal of Pharmaceutical Analysis > 2024 > 14(4): 100936

Xinyue Yang, Yingyu Sima, Xuhuai Luo, Yaping Li, Min He. Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin[J]. Journal of Pharmaceutical Analysis, 2024, 14(4): 100936. doi: 10.1016/j.jpha.2024.01.004

Citation:

Xinyue Yang, Yingyu Sima, Xuhuai Luo, Yaping Li, Min He. Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin[J]. Journal of Pharmaceutical Analysis, 2024, 14(4): 100936. doi: 10.1016/j.jpha.2024.01.004

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Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin

doi: 10.1016/j.jpha.2024.01.004

a. Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China;
b. Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, 410082, China;
c. Department of Quality Control, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China

Funds:

This work is financially supported by Hunan 2011 Collaborative Innovation Center of Chemical Engineering &

Technology with Environmental Benignity and Effective Resource Utilization, Hunan Province Natural Science Fund, China (Grant Nos.: 2020JJ4569 and 2023JJ60378) and Hunan Province College Students' Innovation and Entrepreneurship Training Program, China (Grant Nos.: S202110530044 and S202210530048).

Received Date: Jul. 25, 2023
Accepted Date: Jan. 11, 2024
Rev Recd Date: Dec. 24, 2023
Publish Date: Jan. 14, 2024

Abstract

Abstract

This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC×GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC×GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by “burr” signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC×GC data.
- GC×,
- GC,
- Image fingerprints,
- Contour detection,
- Clustering of mass spectra,
- Curcuma products

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References(52)

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