近红外光谱定量分析中, 采用合适的校正集选择方法是建立预测性能良好的近红外定量模型的关键技术之一。 校正集选择方法有RS法、 CS法、 KS法和SPXY法等, 但是对以上校正集选择方法缺乏系统地比较。 本文以积雪草总苷中积雪草苷NIR定量模型为载体, 对NIR定量模型的7个评价指标进行分类和筛选, 比较了CS法、 KS法和SPXY法三种校正集选择方法对NIR定量模型的准确性和稳健性两类评价指标的影响。 结果表明, SPXY法与CS法、 KS法选择校正集样本后所建近红外模型的RPD和RSEP两个准确性评价指标存在显著性差异, 模型的稳健性评价指标RMSECV和|RMSEP-RMSEC|不存在显著性差异。 因此, 建立积雪草总苷近红外光谱的积雪草苷偏最小二乘定量模型时, SPXY校正集选择方法能显著提高该定量模型的预测准确度, 但对模型稳健性的评价指标没有显著影响, 以上结论为中药固体体系建立近红外定量模型确定校正集选择方法提供参考。

The appropriate algorithm for calibration set selection was one of the key technologies for a good NIR quantitative model. There are different algorithms for calibration set selection, such as Random Sampling (RS) algorithm, Conventional Selection (CS) algorithm, Kennard-Stone(KS) algorithm and Sample set Portioning based on joint x-y distance (SPXY) algorithm, et al. However, there lack systematic comparisons between two algorithms of the above algorithms. The NIR quantitative models to determine the asiaticoside content in centella total glucosides were established in the present paper, of which 7 indexes were classified and selected, and the effects of CS algorithm, KS algorithm and SPXY algorithm for calibration set selection on the accuracy and robustness of NIR quantitative models were investigated. The accuracy indexes of NIR quantitative models with calibration set selected by SPXY algorithm were significantly different from that with calibration set selected by CS algorithm or KS algorithm, while the robustness indexes, such as RMSECV and |RMSEP-RMSEC|, were not significantly different. Therefore, SPXY algorithm for calibration set selection could improve the predicative accuracy of NIR quantitative models to determine asiaticoside content in centella total glucosides, and have no significant effect on the robustness of the models, which provides a reference to determine the appropriate algorithm for calibration set selection when NIR quantitative models are established for the solid system of traditional Chinese medcine.