A recent study presents a novel end-to-end deep learning approach designed to transform hyperspectral images into chemical maps. This method employs a modified U-Net architecture (F4) combined with a custom loss function (F5) to enhance the accuracy and reduce noise in the resulting chemical maps. The input data for this approach consists of hyperspectral images (F2), and the output data are the corresponding chemical maps (F3). Compared to traditional models such as partial least squares regression (F6), this new technique demonstrates significant performance improvements (F7), as supported by the study's findings (A1). By integrating these innovations, the approach marks a notable advancement in the field of chemical imaging analysis. This development aligns with ongoing research efforts to leverage deep learning for improved interpretation of complex spectral data (F1).