Park Mingyu, Um Byung Hwan, Park Seung-Hyun, Kim Dae-Yeol
Department of Computer Engineering, Kyungnam University, Changwon 51767, Gyeongsangnam-do, Republic of Korea.
Carbon-Neutral Resources Research Center, Hankyong National University, 327, Jungang-ro, Anseong 17579, Gyeonggi-do, Republic of Korea.
Polymers (Basel). 2025 Mar 18;17(6):806. doi: 10.3390/polym17060806.
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.
木质素是一种从植物细胞壁中提取的复杂生物聚合物，在结构完整性方面发挥着关键作用。作为仅次于纤维素的第二丰富生物聚合物，木质素在生物能源、化学工业和农业中具有重要的工业价值，作为化石燃料的可持续替代品而受到关注。其组成在降解过程中会发生变化，影响其应用，因此准确分析至关重要。常见的木质素分析方法包括热重分析（TGA）、傅里叶变换红外光谱（FT-IR）和气相色谱-质谱联用（GC-MS）。虽然GC-MS能够进行精确的化学鉴定，但其高成本和时间要求限制了在预算有限的研究中的频繁使用。为应对这一挑战，本研究探索了一种人工智能模型的可行性，该模型利用从TGA和FT-IR分析中获得的数据预测解聚木质素的GC-MS分析结果。所提出的模型显示出潜力，但需要在各种木质素底物上进行进一步验证以确保通用性。此外，与有机化学家合作对于评估其在实际木质素和生物质研究中的实际适用性至关重要。
