目的 研究光谱颜色复制中原稿图像的光谱信息，并对目标色的光谱反射率进行重构，探究影响重构光谱精度的因素。方法 通过选取Munsell Color Matt（1269色块）和Color Checker Classic（24色块）2种色卡作为光谱反射率数据样本，建立不同的主成分分析线性重构模型，选取不同的基向量个数分别重构光谱，并对其精度进行评价，取Classic色卡模拟多光谱图像中重建光谱反射率的目标色，研究比较光谱重构模型和基向量数目对重构精度的影响。结果 实验表明，降维模型1最终恢复的数据在RMSE, GFC和色差上均优于模型2，随着基向量数目的增加，2种降维模型差距在减小，当基向量数目达到13以后，2种模型基本没差异。结论 文中提到光谱重建模型1和7个基向量是重构光谱图像的最佳方案。

The work aims to study the spectral information of the original image during the spectrum color reproduction and reconstruct the spectral reflectance of the target color, so as to explore the factors affecting the accuracy of reconstructed spectra. With two kinds of color cards (Munsell Color Matt (1269 color lump) and Color Checker Classic (24 color lump)) selected as the spectral reflectance data samples, different linear reconstruction models of PCA were established and different numbers of base vectors were selected to separately reconstruct the spectra. Then, their accuracy was evaluated. Classic color card was taken to simulate the target color in the multispectral image that reconstructed the spectral reflectance, so as to study and compare the influence of the number of spectral reconstruction models and base vectors on the reconstruction accuracy. The experimental results showed that the finally restored data by dimension reduction model 1 were better than dimension reduction model 2 in RMSE, GFC and color difference. With the increase of the number of base vectors, the gap of two dimension reduction models was reducing. When the number of base vectors was more than 13, the two models were almost no difference. The proposed spectral reconstruction model 1 and 7 base vectors are the optimal scheme for reconstructing spectral images.