Lignin is a complex biopolymer extracted from plant cell walls, playing a crucial role in structural integrity. As the second most abundant biopolymer after cellulose, lignin has significant industrial value in bioenergy, the chemical industry, and agriculture, gaining attention as a sustainable alternative to fossil fuels. Its composition changes during degradation, affecting its applications, making accurate analysis essential. Common lignin analysis methods include Thermogravimetric Analysis (TGA), Fourier-transform Infrared Spectroscopy (FT-IR), and Gas Chromatography-Mass Spectrometry (GC-MS). While GC-MS enables precise chemical identification, its high cost and time requirements limit frequent use in budget-constrained studies. To address this challenge, this study explores the feasibility of an artificial intelligence model that predicts the GC-MS analysis results of depolymerized lignin using data obtained from TGA and FT-IR analyses. The proposed model demonstrates potential but requires further validation across various lignin substrates for generalizability. Additionally, collaboration with organic chemists is essential to assess its practical applicability in real-world lignin and biomass research.
Keywords: GC-MS analysis; biomass valorization; deep learning; depolymerized lignin prediction; multimodal spectroscopic analysis.