Reflectance functions can be represented by low-dimensional linear models with weighted sum of principal components (or, often, referred to as basis functions). Such method to obtain a low-dimensional linear model is based on principal component analysis (PCA). The specific requirement for a low-dimensional model is to accumulate fraction of variance of the basis functions. The more basis functions included the more fraction of variance accumulates. The investigation of how many basis functions required so as to represent reflectance functions accurately has been extensively studied over the last two decades [1, 2] since Cohen fitted a linear model to spectral reflectance functions of Munsell color chips in 1964 [3]. In this paper, a comprehensive dataset of 97593 including six types of materials has been accumulated. These materials are paint, graphic, plastic, textile, skin and natural samples. Principal component analysis for each material has been studied. The effective dimension of reflectance functions representations for these materials has examined. It was found that a single set of basis functions can essentially be applied to represent all spectra in the world.