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A multitude of studies demonstrate that robust predictive models for identifying microorganisms can be successfully developed by combining Raman micro-spectroscopy with chemometric techniques, frequently yielding highly accurate outcomes. Progress in machine learning alongside increased software availability has simplified the process for researchers to construct predictive frameworks from intricate data arrays. Conversely, the underlying reasons for the high accuracy of these classifications remain under-investigated. This study analyzes Raman spectroscopic datasets from fungal spores and carotenoid-producing microorganisms to demonstrate that successful differentiation is frequently independent of peak positions or subtle variations in spectral band ratios caused by minor biochemical differences among the organisms. Instead, distinctive baseline variations in biochemically related microorganisms, which may be amplified by specific data preprocessing routines, play a critical role; furthermore, seemingly neutral spectral intervals can significantly influence a convolutional neural network. By applying a technique known as Gradient-weighted Class Activation Mapping, we investigate the internal mechanics of convolutional neural networks within microbiological domains to determine the precise Raman spectral bands driving successful classification.