Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification

Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical classification was demonstrated by extracting IR spectral images from the Spectral Database for Organic Compounds and converting them into 194300-dimensional vector data. Hierarchical clustering of 227 compounds revealed distinct main clusters (A–G), each with specific subclusters exhibiting higher intracluster similarities. Despite the challenges, including sensitivity to spectral deviations and difficulty of distinguishing delicate chemical structures in spectra with low transparency in the fingerprint area, the proposed image recognition approach exhibits good potential. The Tanimoto coefficient was used to evaluate the molecular similarity, providing valuable insights. However, some results deviated from chemists’ intuitions. The study also highlighted that the scaling composition formulas and molecular weights did not affect the classification results because high-dimensional features dominated the process. Overall, this study demonstrates the feasibility of using machine learning with IR spectral image data for chemical classification and offers a novel perspective that complements traditional methods, although the classifications may not always align with chemists’ intuitions.