Recently, Li et al. introduced a novel similarity measure, the Shannon Entropy Correlation, originally developed in the field of signal processing, into compound identification. Note that various entropy measures have been extensively reviewed for time series analysis by Pessa et al. (2021). Their findings demonstrated superior performance over numerous binary and continuous similarity scores, including the Cosine Correlation. However, it is widely recognized that peak intensities of fragment ions with large mass-to-charge (m/z) ratios tend to be smaller, despite being the most informative for compound identification. Consequently, a weight factor transformation has been proposed to enhance compound identification performance by increasing the relative importance of large fragment ions through weighting their peak intensities more heavily. In the GC-MS community, the weighted Cosine Correlation, which employs the Cosine Correlation with transformed mass spectra, is extensively utilized. However, Li et al. (2021) compared the performance of compound identification across several similarity measures without considering the weight factor transformation. Therefore, it is crucial to explore the impact of weight factor transformation on compound identification performance for a more comprehensive understanding.