Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.

ABSTRACT: Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpretation remains a challenge. Machine learning methods have gained prominence in spectral analyses, efficiently unveiling analyte compositions. However, these methods still face challenges in interpretability, particularly in generating clear feature importance maps that highlight the spectral features specific to each class of data. These limitations arise from feature noise, model complexity, and the lack of optimization for spectroscopy. In this work, we introduce a machine learning algorithm─logistic regression with peak-sensitive elastic-net

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12709630 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature