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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Ethnopharmacology
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1503508
This article is part of the Research Topic Advancing Herbal Quality Assurance: The Role of Artificial Intelligence in Enhancing Quality Control Practices View all articles

Efficient Generation of HPLC and FTIR Data for Quality Assessment Using Time Series Generation Model: A Case Study on Tibetan Medicine Shilajit

Provisionally accepted
Rong Ding Rong Ding 1Shiqi He Shiqi He 2Xuemei Wu Xuemei Wu 2Liwen Zhong Liwen Zhong 2Guopeng Chen Guopeng Chen 1Gu Rui Gu Rui 1*
  • 1 School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
  • 2 School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China

The final, formatted version of the article will be published soon.

    Background: The scarcity and preciousness of plateau characteristic medicinal plants pose a significant challenge in obtaining sufficient quantities of experimental samples for quality evaluation. Insufficient sample sizes often lead to ambiguous and questionable quality assessments and suboptimal performance in pattern recognition. Shilajit, a popular Tibetan medicine, is harvested from high altitudes above 2000 meters, making it difficult to obtain. Additionally, the complex geographical environment results in low uniformity of Shilajit quality. Methods: To address these challenges, this study employed a deep learning model, time-domain vector quantized variational autoencoder (TimeVQVAE), to generate data matrices based on chromatographic and spectral for different grades of Shilajit, thereby increasing in the amount of data. Partial least squares discriminant analysis (PLS-DA) was used to identify three grades of Shilajit samples based on original, generated, and combined data. Results: Compared with the originally generated high performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) data, the data generated by TimeVQVAE effectively preserved the chemical profile. In the test set, the average matrices for HPLC, FTIR, and combined data increased by 32.2%, 15.9%, and 23.0%, respectively. On the real test data, the PLS-DA model’s classification accuracy initially reached a maximum of 0.7905. However, after incorporating TimeVQVAE-generated data, the accuracy significantly improved, reaching 0.9442 in the test set. Additionally, the PLS-DA model trained with the fused data showed enhanced stability. Conclusion: This study offers a novel and effective approach for researching medicinal materials with small sample sizes, and addresses the limitations of improving model performance through data augmentation strategies.

    Keywords: Shilajit, FTIR, HPLC, Time Series Generation, Classification

    Received: 29 Sep 2024; Accepted: 05 Nov 2024.

    Copyright: © 2024 Ding, He, Wu, Zhong, Chen and Rui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Gu Rui, School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.