Unlike the mass spectra generated by GC-MS, LC-MS spectra are high-resolution and require several preprocessing steps before computing similarity scores. These steps include centroiding of peaks to simplify the spectra, removing background noise to enhance signal clarity, and matching peaks across spectra for comparison. Consequently, we investigated the effect of preprocessing order, particularly in relation to the application of the weight factor transformation. As recommended by Li et al., we transformed low-entropy spectra before computing the Shannon Entropy Correlation and applied this low-entropy transformation prior to computing the Cosine and Tsallis Entropy Correlations as well. To our knowledge, this is the first study to explore the impact of the weight factor transformation on Shannon Entropy Correlation and to evaluate the performance of Shannon Entropy Correlation in GC-MS-based identification.