2025ApJS..281...51R

In the era of the James Webb Space Telescope (JWST), with its unprecedented sensitivity and spectral resolution, infrared spectral surveys have revealed a rich inventory of ices, including complex organic molecules (COMs), in young stellar objects. However, robust methods to decompose and quantify these absorption features, particularly across broad spectral ranges, are still under investigation. We present the Ice-fitting with NNLS-based Decomposition and Retrieval Algorithm (INDRA), a fully Python-based tool that performs continuum and silicate removal, global ice fitting using weighted nonnegative least-squares (NNLS), and estimates column densities and statistical significance. We apply INDRA to NGC 1333 IRAS 2A, a target from the JWST Observations of Young protoStars (JOYS+) program previously studied using local fitting. We derive optical depths via polynomial continuum subtraction and remove silicate absorption using a synthetic model, isolating ice features for global MIRI fitting. Our results are consistent with previous local fits, confirming simple species and COMs, and expand the inventory by identifying additional absorption features from CO₂ and NH${}_{4}^{+}$. We also propose the presence of organic refractories contributing up to 9.6% in the spectral region of ∼5-8 μm among the various ice components, whose inclusion significantly improves the global spectral fitting. These broad absorption features, extending across ∼5.5-11 μm, are likely produced by large, complex molecules containing carbonyl (C=O), hydroxyl (O-H), amine (N-H), and C-H bending modes. Our expanded inventory, now incorporating these organic residues, offers new insights into the chemical evolution of ices in star-forming regions and highlights the importance of global spectral fitting in constraining ice compositions.