中文摘要：

针对复杂样品近红外光谱分析中校正集的设计问题，探讨了标准样品参与复杂样品建模的可行性．通过标准样品和复杂基质样品共同构建的偏最小二乘（PLS）模型，考察了波段筛选和建模参数对预测结果的影响．结果表明，采用PLS方法建立定量模型时，校正集样品性质应该尽量与预测集样品相似，当样品的性质相差较大时，适当增加校正集样品的差异性可使模型具有更强的预测能力．同时，波段优选对提高预测结果的准确性具有重要的意义．

英文摘要：

Design of calibration samples is often used to improve the cost-effectiveness of near-infrared（NIR） spectral analysis. The feasibility of constructing a partial least squares （PLS） model with a parsimonious calibration set for NIR spectral analysis was discussed. Waveband selection was also investigated for improving the predictive ability of the model. The results indicate that, in construction of the quantitative analysis PLS model, the samples in a calibration set should be as similar as possible to the samples in the prediction set. For prediction of the samples with diverse properties, the diversity of the calibration samples is necessary to ensure the predictive ability of the model. On the other hand, waveband selection is proved to be very important for improving the predictive ability of models.