Proximal hyperspectral imaging is a powerful tool for non-destructive analysis in food quality assessment, precision agriculture, and material inspection. However, its high-dimensional nature makes it susceptible to noise, requiring effective preprocessing techniques. Prior research highlights the importance of radiometric correction, spectral calibration, normalization, and noise reduction. Yet, challenges remain in denoising methods, scatter correction, and dimensionality reduction, affecting spectral accuracy. This study systematically evaluates preprocessing techniques, including noise reduction, scatter correction, and dimensionality reduction, assessing their effectiveness in preserving spectral integrity. Additionally, the hybrid strategies are proposed to optimize the hyperspectral data for feature extraction. Among the fusion methods, the combination of Savitzky-Golay smoothing and principal component analysis demonstrates better performance in enhancing spectral features and data quality with a high PSNR of 30.83 dB and a low RMSE of 0.0334. In this paper, the findings contribute to the optimization of preprocessing strategies, advancing hyperspectral imaging applications across scientific and industrial domains.