Spectral mixture modelling is one of the most important techniques for classifying hyperspectral data at sub-pixel resolution. The identification of spectrally pure endmembers for estimating their corresponding abundances is an important step in spectral unmixing. The application of spectral reduction prior to endmember extraction would optimize the process by increasing the sensitivity of the algorithms to the most distinctive and informative features of the dataset. The objective of this study is to compare different spectral reduction techniques prior to endmember extraction on six real hyperspectral datasets, including an Airborne Visible InfraRed Imaging Spectrometer- Next Generation (AVIRIS-NG) image over Indian sub-continent. The endmembers identified from different combinations of spectral reduction and endmember extraction techniques are used for linear spectral unmixing on the original datasets. The performance of such combinations after unmixing were compared in terms of pixel reconstruction error and also the computation time for each dataset. Spectral reduction by both feature extraction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), Minimum Noise Fraction (MNF), and a feature selection technique based on Partial Informational Correlation (PIC) measure were analysed. The PIC based spectral reduction was found to perform well in terms of reconstruction error and computation time when combined with N-FINDR endmember algorithm. This approach could be adopted for spectral reduction in unmixing of datasets with similar endmember classes.