In recent times spectral matching techniques play a crucial role for identification and classification of different materials based on their varying physical properties. In this research work novel frequency domain analysis techniques have been proposed for enhanced material discrimination. High resolution spectral data provides detailed information ranging from visible to infrared range, which helps to analyze different absorption and reflectance features which signifies the presence of different materials. This study employs Fast Fourier Transformation to shift the data in frequency domain for identifying detailed characteristics of the different objects. Cosine Similarity is a very well-known technique which measures the degree of similarity of spectral data, but not accurately identifies the spectral characteristics. To overcome this shortcoming Wavelet transformation has been applied in the spatial domain which decompose the spectral data in to approximation and detail coefficient extract the global and localized features. To identify frequency variation and phase characteristic of the data Hilbert Transformation methods has been applied in the frequency domain. Fractal dimension analysis is employed to assess the complexity of spectral patterns, revealing unique textural properties that differentiate materials. In this study with the use of this multiple method, reliable and accurate spectrum matching technique becomes achievable, exhibiting its effectiveness in high resolution spectral data analysis for a variety of applications, including identification of minerals, evaluation of oil quality, and agricultural monitoring etc. This research work also emphasizes the effectiveness of frequency domain analysis for high resolution spectral data interpretation.