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目的:应用近红外漫反射光谱结合偏最小二乘法,建立25个厂家85批一清颗粒中黄芩苷含量的近红外光谱定量分析模型。方法:在12000～4000cm-1光谱范围内扫描样品,以校正均方差(RMSEC)和相关系数(R2)为指标,通过筛选,确定了用于建模的最优近红外波段和光谱预处理方法。采用偏最小二乘法建立了近红外光谱与HPLC分析值之间的校正模型,并以此预测了10个未知样本。结果:近红外预测值与真实值的相关系数R2为0.9732,校正均方差(RMSEC)为0.0625,含量预测回收率为101.1%,RSD为3.0%,预测均方差(RMSEP)为1.5031。结论:该模型可直接对样品进行快速准确的检测,具有非破坏性、无污染、重现性好等优点,可以应用于一清颗粒制剂的定量测定。
OBJECTIVE: To establish a quantitative analysis model of baicalin in 85 batches of Chiyi Granules of 25 batches by near-infrared diffuse reflectance spectroscopy combined with partial least squares method. Methods: The samples were scanned in the spectral range of 12000 ~ 4000cm-1, and the optimal near-infrared band and spectral pretreatment methods for modeling were determined by screening with RMSEC and R2 as indexes . The partial least square method was used to establish a calibration model between near-infrared spectroscopy and HPLC analysis, and then 10 unknown samples were predicted. Results: The correlation coefficient R2 between predicted and real values of NIR was 0.9732, RMSEC was 0.0625, the predicted content recovery was 101.1%, RSD was 3.0%, and the root mean square error of prediction (RMSEP) was 1.5031. Conclusion: This model can directly and quickly detect samples with non-destructive, non-destructive, reproducible, and can be applied to the quantitative determination of a clear granule formulation.