Infrared (IR) spectroscopy, a cornerstone method for material characterization, visually represents the chemical bond-stretching modes. Additionally, the predictions of AI models trained on extensive IR spectral databases align with those of human chemists 12 . Unsupervised clustering with smaller datasets has also shown potential for organiccompound classification, with applications extending to hyperspectral identification 12c . By contrast, Enders et al. reported research on determining the presence or absence of functional groups in IR images using supervised machine learning 12l . Thus, image-recognition-based analysis can complement traditional methods.