The qualitative and quantitative assessment of xiaochaihu granules based on e-eye, e-nose, e-tongue and chemometrics

电子舌 甘草甜素 黄芩苷 舌头 化学 化学计量学 电子鼻 人工智能 偏最小二乘回归 色谱法 品味 食品科学 数学 高效液相色谱法 统计 计算机科学 内科学 哲学 医学 语言学
作者
Xue Zhang,Hongwei Wu,Lina Lin,Xiao Du,Shihuan Tang,Huihui Liu,Hongjun Yang
出处
期刊:Journal of Pharmaceutical and Biomedical Analysis [Elsevier BV]
卷期号:205: 114298-114298 被引量:15
标识
DOI:10.1016/j.jpba.2021.114298
摘要

Xiaochaihu granules (XCHG), a famous Chinese patent medicine with high sales, have more than 100 approved number by China Food and Drug Administration (CFDA). Therefore, it is important to evaluate the quality of XCHG from different pharmaceutical companies. The data fusion of electronic eye (e-eye), electronic nose (e-nose) and electronic tongue (e-tongue) combined with chemometrics were applied for qualitative identification and quantitative prediction of XCHG quality. Firstly, main chemical constituents, such as saikosaponin b2, baicalin and glycyrrhizin were quantified with ultra-high-performance liquid chromatography (UHPLC). Secondly, the characteristic features of odor, color, and taste of XCHG were measured by e-nose, e-eye and e-tongue, and the Pearson correlation between constituents and e-signals was analyzed. Thirdly, partial least squares discrimination analysis (PLS-DA) of e-eye, e-nose and e-tongue were classified by the hierarchical clustering analysis (HCA) results of the main constituents of XCHG separately. Finally, partial least-squares regression (PLSR) was used to build the prediction model between components and data fusion of e-eye, e-nose and e-tongue. The results showed that saikosaponin b2, baicalin and glycyrrhizin were the three main components in XCHG samples. in which saikosaponin b2 ranged from 0.280 to 2.186 mg (relative standard deviation (RSD), 62.10 %), baicalin range from 25.883 mg to 49.108 mg (RSD, 16.64 %), and glycyrrhizin ranged from 0.897 mg to 6.052 mg (RSD, 40.32 %) of 31 batches of XCHG in each bag. Pearson correlation results showed that the main constituents were related to the core e-signals of XCHG, such as Eab, bitterness and R2 (odor sensitive to nitrogen oxide). Data fusion of e-eye, e-nose and e-tongue with main constitutes of XCHG using the PLSR model showed that the root mean square error (RMSE) values were 0.320 and 0.090 for saikosaponin b2 and licoricesaponin G2 (P < 0.000). The saikosaponin b2 and licoricesaponin G2 contents in XCHG could be predicted with integrated data of e-nose, e-eye, and e-tongue using the PLSR model.
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