Consistency evaluation and quantitative analysis of ginger formula granules based on portable near infrared spectrometer
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摘要 目的:建立生姜配方颗粒的近红外光谱(NIRS)一致性评价及定量分析方法。方法:利用便携式近红外光谱仪采集生姜配方颗粒样品的近红外光谱信息,通过对61批生姜配方颗粒样品的指纹图谱与对照指纹图谱进行一致性检验,采用相关系数进行分析评价。采用高效液相色谱法(HPLC)测定生姜配方颗粒样品中6-姜辣素的含量。考察不同预处理方法对定量模型参数的影响,建立偏最小二乘法(PLS)和支持向量回归(SVR)定量模型。结果:在一致性评价中61批生姜配方颗粒的指纹图谱与对照指纹图谱的相关系数均大于0.97,不同样品批次间一致性良好。在所建立的定量模型中,最优PLS模型的校正均方根误差(RMSEC)为0.071 1、校正相关系数(RC)为0.624 0、预测均方根误差(RMSEP)为0.065 4、预测相关系数(RP)为0.700 9,最优SVR模型的RMSEC为0.044 8、RC为0.919 8、RMSEP为0.066 3、RP为0.776 5。结论:所建立的SVR定量模型较PLS定量模型具有更好的预测性能,能够对6-姜辣素含量进行快速预测。本研究基于便携式近红外光谱仪对生姜配方颗粒进行了有效、快速、无损的质量分析,为生姜配方颗粒的质量控制提供新的方法参考。Abstract Objective: To establish a method for the consistency evaluation and quantitative analysis of near infrared spectroscopy(NIRS) of ginger formula granules. Methods: NIR spectral information of ginger formula granules samples were collected using a portable NIR spectrometer. The fingerprints of 61 batches of ginger formula granules samples were tested for consistency with the control fingerprints, and evaluated using correlation coefficients. The content of 6-gingerol in the ginger formula granules samples was determined by high performance liquid chromatograph(HPLC). The effects of different preprocessing methods on the parameters of the quantitative model were investigated, and the partial least squares(PLS) and support vector regression(SVR)quantitative models were established. Results: In the consistency evaluation, the correlation coefficients between the fingerprints of the 61 batches of ginger formula granules and the control fingerprints were all greater than 0.97 in the consistency test, the batch-to-batch consistency of the ginger formula granules samples was good.Among the established quantitative models, the optimal PLS model had a root mean square error of correction(RMSEC) of 0.0711, along with a correlation coefficient of correction(RC) of 0.6240. Additionally, the root mean square error of prediction(RMSEP) for this model was 0.0654, and the correlation coefficient of prediction (RP) was 0.7009. For the SVR model, the RMSEC was 0.0448 with an RC of 0.9198, while the RMSEP was 0.0663,and the RP stood at 0.7765. Conclusion: The SVR quantitative model has better predictive performance than the PLS quantitative model, and can rapidly predict the content of 6-gingerol. This research performs effective, rapid and nondestructive quality analysis of ginger formulation granules based on the portable near infrared spectrometer, which provides a new method reference for the quality control of ginger formula granules.
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