建立了同时测定利福平片、异烟肼片、吡嗪酰胺片、异福片和异福酰胺片5种抗结核药物中的利福平（RMP）、异烟肼（INH）和吡嗪酰胺（PZA）含量的新方法,应用径向基神经网络（RBFNN）建立5种抗结核片剂药物样品的近红外光谱（NIRS）与其中RMP、INH和PZA含量间相关模型。模型以交互验证均方根误差（RMSECV）为评价标准,选择最有效光谱区域、对网络结构参数和扩展常数进行优化,得到最优定量分析模型。最优模型的RMSECV分别为0.0127、0.0104、0.0078,应用最优模型对预测集样品中RMP、INH和PZA含量进行预测,预测均方根误差（RMSEP）分别为0.0125、0.0109、0.0103。内部交互验证和外部验证均表明,该方法具有较高的准确度,能够满足5 种抗结核药物生产中RMP、INH和PZA的同时检测精度要求。

A new approach is established to determine the contents of rifampicin (RMP), isoniazide (INH) and pyrazinamide (PZA) in five varieties of anti-tuberculosis tablets,i.e. rifampicin tablets, isoniazide tablets, pyrazinamide tablets, rifampicin and isoniazide tablets and rifampicin isoniazide and pyrazinamide tablets. Near infrared spectroscopy (NIRS) with radial basis function neural network (RBFNN) is applied to establishing the analytical protocol for the determination of the contents of RMP, INH and PZA in the five anti-tuberculosis tablets. The root mean square error of calibration set obtained by cross-validation (RMSECV) is used as the evaluation of the model. The optimal quantitative analysis models are resulted from selecting the most effective spectral region,suitable topological parameter and spread constants. RMSECV of the optimum models are 0.0127, 0.0104 and 0.0078. Using these optimum models for determination of the RMP, INH and PZA contents in prediction set, the root mean square error of prediction set (RMSEP) are 0.0125, 0.0109 and 0.0103. Internal cross-validation and external validation are verified and the method has a high accuracy and can meet the accuracy in simultaneous determination of RMP, INH and PZA contents in 5 varieties of anti-tuberculosis tablets.