在消光法颗粒粒径测量中，被测颗粒系的消光光谱包含有颗粒粒径、折射率等信息。在可见及可见-红外波段内，对单峰R-R 分布的消光光谱，一阶微分以及二阶微分消光光谱进行主成分变换。通过分析比较，提出一种基于主成分分析的特征波长的选择算法。该算法首先对一阶微分消光光谱进行主成分变换，然后将每个波长下的一阶微分消光谱对主成分贡献率的大小作为特征波长选择的主要依据，同时将光谱范围的边界波长也作为特征波长，这样的波长选择方法保证了选出的光谱消光值具有较高的信息量。文中分别对单峰及双峰R-R 分布的颗粒系采用独立模式反演算法进行仿真实验验证，仿真实验证实了所提方法的有效性和实用性。

The extinction spectrums of particle system contain some information about the particle size and refractive index with total light scattering particle sizing method. In the visible and visible-infrared region, the principal component transform was performed for the extinction spectrums, the first-order differential extinction spectrums and the second-order differential extinction spectrums of monomodal R-R particle system. After analysis and comparison with the Principal Component Analysis (PCA) method, a selection algorithm for the characteristic wavelengths based on the PCA was proposed. This algorithm uses the principal component transform to dispose the first-order differential extinction spectrums firstly, and then regards the contribution rate of the principal component to the first-order differential extinction spectrums at each wavelength as the main criterion by which characteristic wavelengths can be selected. Meanwhile, the boundary wavelengths in the spectrum region are also selected as the characteristic wavelengths. This selection method ensures that the extinction values of selected characteristic wavelengths contain most information about the particle size distribution. The numerical simulation experiments on the monomodal and biomodal R-R distributions were performed in the independent model, which illustrated the validity and feasibility of the selection method based on the PCA.