在近红外光谱分析模型传递中， 常用Kennard/Stone算法(K/S算法)来选择转换集样品。 K/S算法是根据样品间光谱的欧氏距离来计算样品间差异的。 为了寻求样品间差异的最佳表达方式， 用K/S算法选出更有代表性的样品。 文章分别用欧氏距离和马氏距离计算混胺样品的光谱差异， 用PDS算法传递混胺中二甲苯胺近红外光谱分析模型， 通过传递后的预测标准偏差(SEP)评价两种距离的优劣。 用性质差异和光谱差异结合的方式计算样品间的差异， 并与单独用光谱差异和性质差异的方式进行比较。 结果表明， 在PDS算法中， 马氏距离选出的样品更具有代表性， 性质差异和光谱差异的结合更能代表样品间的差异。

The Kennard/Stone(K/S) algorithm is adopted to select the transfer set samples in the near infrared analysis calibration transfer by calculating the Euclidean distance of the spectrum of the samples. In order to get a best expression of the comparability of the samples and select the best samples for calibration transfer, piecewise direct standardization (PDS) algorithm was investigated for resolving calibration transfer of near-infrared spectra of mixed amine, then the Euclidean distance and the Mahalanobis distance were applied to calculate the comparability of the spectra of the samples and the standard error of prediction (SEP) was used to evaluate them. The combination of spectral difference and property difference was introduced to estimate the effect of individual spectral difference and property difference. The experimental results showed that the Mahalanobis distance was better than the Euclidean distance in PDS and the combination of spectral difference and property difference was more effective in expressing the comparability of the samples.