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  • Title: Predictive quality control for compound liquorice tablets by the intelligent mergence fingerprint method combined with the systematic quantitative fingerprint method.
    Author: Hou Z, Sun G.
    Journal: Phytochem Anal; 2021 Nov; 32(6):1118-1130. PubMed ID: 33955089.
    Abstract:
    INTRODUCTION: Compound liquorice tablet (CLT) is a herbal compound preparation and is used as a classic antitussive and expectorant in China. It is composed of liquorice extract powder, opioid powder, star anise oil, camphor, and sodium benzoate. The complexity of herbal materials brings a huge challenge in producing compound preparations with stable and uniform quality consistency. OBJECTIVE: To establish a new intelligent model for predicting the quality of CLT. METHODS: The HPLC fingerprints of raw materials including liquorice extract powder, powdered opium, star anise oil, and sodium benzoate were tested and merged to generate the intelligent mergence fingerprints, whose correlation with the raw materials and the CLT samples was studied. The consistency of the intelligently merged fingerprints with the standard fingerprints was observed by using the systematic quantitative fingerprint method in order to calculate quality evaluation results. RESULTS: The intelligent mergence fingerprints covered all the main fingerprint peaks of four raw materials and had a good correlation with the CLT sample fingerprint. There were no significant quality differences either among the six intelligent mergence models obtained by combining different batches of raw materials or between the reference fingerprint of the intelligent mergence connection fingerprints (RFPIMFC ) and the theoretical standard preparation (RFPS ). CONCLUSION: The computer-aided model of intelligent mergence fingerprints could be used to predict the quality of herbal compound preparations based on raw materials. In this way, preproduction quality prediction can be realised in order to avoid low-quality medicinal materials and improve the quality consistency among different batches.
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