为研究组分浓度分布范围对光谱法建模定量分析精度的影响, 根据朗伯-比尔定律构造三种组分理想吸收谱并叠加高斯噪声, 使用偏最小二乘回归对样本吸收谱及浓度进行建模和预测, 观测不同浓度分布范围下分析精度的变化。 研究表明, 在纯线性吸收的情况下, 组分浓度的分布范围对模型精度造成一定的影响。 无论是被测组分还是非测量组分, 校正集样本中覆盖足够大且较均匀的浓度分布范围是模型强普适性和良好预测精度的必要保证。 研究为合理选择具有良好浓度分布校正集样本, 从而提高模型质量、 减小预测误差提供了理论指导。

In order to discuss the effect of different distribution of components concentration on the accuracy of quantitative spectral analysis, according to the Lambert-Beer law, ideal absorption spectra of samples with three components were established. Gaussian noise was added to the spectra. Correction and prediction models were built by partial least squares regression to reflect the unequal modeling and prediction results between different distributions of components. Results show that, in the case of pure linear absorption, the accuracy of model is related to the distribution of components concentration. Not only to the component we focus on, but also to the non-tested components, the larger covered and more uniform distribution is a significant point of calibration set samples to establish a universal model and provide a satisfactory accuracy. This research supplies a theoretic guidance for reasonable choice of samples with suitable concentration distribution, which enhances the quality of model and reduces the prediction error of the predict set.