The spectral data are defined as a ‘fingerprint’ of an object and is very critical in many application. In addition the color specifications of surfaces under multiple viewing conditions can be determined by access to this information. The spectral reflectance can be directly measured using Spectrophotometers and are usually represented with a high dimension according to the accuracy of instrument. During the last decades, applying mathematical methods such as Principal Component Analysis (PCA) has shown that spectral data has limited number of principal vectors. In the present study, it is attempted to perform a spectrum analysis and to reconstruction the reflectance spectrum of brilliant samples. To this end, three categories of dyes namely the cationic dyes, which are inherently, categorized as brilliant dyes, disperse and reactive dyes, which include the brilliant term in their brand, and dyes without this term in their brand are utilized. The prepared samples of these dyes were with different densities. Throughout the study, various attempts including compacting spectral data (K/S and R), determining the maximum density required for exiting the scalable region, investigating the color data, and finally, reconstruction reflectance data using major components analysis technique were performed. Various educational environments were utilized for reconstruction reflectance data. After selecting the best educational environment, in which acceptable results could be achieved for reconstruction reflectance spectrum (GFC=0.99) in all dye categories, the mean amount of GFC and RMS parameters as well as color variation exposed to two light sources are taken as error evaluation criteria. Various investigation results showed negligible difference between spectrum analysis results of brilliant and non-brilliant samples. About reconstruction reflectance data, it seems that color variation, density variation, and reflectance curve total shape are more effective than the presence of this term in the dyes brands. Keywords: Brilliant dyes, Spectral reflectance, Principal Component Analysis, Principal vectors.
