Volume 11 Issue 1
Feb.  2021
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Zhenhao Li, Xiaohui Zhang, Jie Liao, Xiaohui Fan, Yiyu Cheng. An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry[J]. Journal of Pharmaceutical Analysis, 2021, 11(1): 88-95. doi: 10.1016/j.jpha.2020.01.003
Citation: Zhenhao Li, Xiaohui Zhang, Jie Liao, Xiaohui Fan, Yiyu Cheng. An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry[J]. Journal of Pharmaceutical Analysis, 2021, 11(1): 88-95. doi: 10.1016/j.jpha.2020.01.003

An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry

doi: 10.1016/j.jpha.2020.01.003
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This work is financially supported by the National Natural Science Foundation of China (Grant No. 81803714) and the Fundamental Research Funds for the Central Universities (Grant No. 2019QNA7041). We would like to thank Dr. Yi Wang and Dr. Xiaoping Zhao for their helpful comments and discussions.

  • Received Date: Aug. 11, 2019
  • Accepted Date: Jan. 13, 2020
  • Rev Recd Date: Dec. 28, 2019
  • Available Online: Jan. 24, 2022
  • Publish Date: Feb. 15, 2021
  • Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine. However, this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness. In this work, we proposed a multiple reaction monitoring (MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment. The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards, based on which the large-scale fingerprinting method was efficiently established. This approach was exemplified on QiShenYiQi Pill (QSYQ), a traditional Chinese medicine-derived drug product, and its robustness was systematically evaluated by four indices: clustering analysis by principal component analysis, similarity analysis by the congruence coefficient, the number of separated peaks, and the peak area proportion of separated peaks. Compared with conventional ultraviolet-based fingerprints, the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples, but also higher robustness under different chromatographic conditions (i.e., flow rate, apparent pH, column temperature, and column). The result also showed for such large-scale fingerprints including a large number of peaks, the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm. This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive, robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.
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