目的：建立羧甲司坦片快速定量分析模型，并探讨近红外定量分析模型中校正集样本数的选择，提高模型的预测能力。方法：收集全国19家生产企业共140批羧甲司坦片样品，采集近红外光谱图，以聚类分析法分别将总样本集分成120类、115类、111类、106类、102类、96类、91类、86类、81类、76类、71类、66类、61类、57类、52类、47类和42类，从每一类中选出一个样本作为训练集，逐一建立模型，利用OPUS光谱软件中定量2方法选择最优建模参数，建立羧甲司坦片通用型定量分析模型，并进行方法学验证。结果：选取61个样本建立羧甲司坦片定量模型，模型交叉验证均方根误差（RMSECV）为1.17%，检验集验证均方根误差（RMSEP）为1.08%，相关系数为0.995；模型能够快速准确预测片剂中羧甲司坦的质量分数，范围为22.28%~85.15%。结论：选取合适数量的样本作为模型的校正集，可有效提高模型预测能力；本文所建立的羧甲司坦片通用型快速定量分析模型准确度好，耐用性强。
Objective:To establish a rapid quantitative analysis model for carbocysteine tablets,and to study the number of samples in the training set so as to improve the predictive performance.Methods:A total of 140 batches of carbocysteine tablets samples from 19 national manufacturers were recruited for the study,and the scanning near-infrared spectra of all samples was collected.The total sample set was respectively divided as 120 types, 115 types,111 types,106 types,102 types,96 types,91 types,86 types,81 types,76 types,71 types,66 types, 61 types,57 types,52 types,47 types and 42 types using clustering analysis.One sample was then randomly selected from each cluster to construct the training set,and the number of samples in training set equaled the number of clusters.Therefore,the optimal calibration models obtained from the automatic optimization routine was implemented in the OPUS Quant 2 software using different numbers of calibration samples.Finally,the best model was chosen and verified.Results:61 samples were selected to establish the quantitative model.The root mean square errors of cross validation and prediction were 1.17% and 1.08% for the model for carbocysteine tablets,and correlation coefficient was 0.995;the quantitative model can quickly predict the content of carbocysteine tablets, with active pharmaceutical ingredient(API,)mass fraction ranging from 22.28% to 85.15%.Conclusion:In this study,the carbocysteine tablets universal quantitative model performances good accuracy and durability.The prediction ability of the established quantitative analysis model was improved by selecting appropriate number of samples.
