Energy demand and the use of commodity consumer products, such as chemicals, plastics, and transportation fuels, are growing nowadays. These products, which are mainly derived from fossil resources and contribute to environmental pollution and CO2 emissions, will be used up eventually. Therefore, a renewable inexhaustible energy source is required. Plant biomass resources can be used as a suitable alternative source due to their green, clean attributes and low carbon emissions. Lignin is a class of complex aromatic polymers. It is highly abundant and a major constituent in the structural cell walls of all higher vascular land plants. Lignin can be used as an alternative source for fine chemicals and raw material for biofuel production. There are many chemical processes that can be potentially utilized to increase the degradation rate of lignin into biofuels or value-added chemicals. In this study, two lignin degradation methods, CuO–NaOH oxidation and tetramethyl ammonium hydroxide (TMAH) thermochemolysis, will be addressed. Both methods showed a high capacity to produce a large molecular dataset, resulting in tedious and time-consuming data analysis. To overcome this issue, an unsupervised machine learning technique called principal component analysis (PCA) is implemented.

Soil salinity is a growing threat to agricultural sustainability, particularly in arid and semi-arid regions. Understanding how salinity affects soil organic matter (OM) is critical for improving land management and maintaining soil health. This study addresses these challenges by exploring the molecular-level impact of salinity on OM dynamics. Salinity exerts a depth-dependent influence on lignin and microbial lipid biomarkers, which are used to trace plant inputs and microbial activity, respectively. For lignin biomarkers, in the surface layer (0–20 cm), higher salinity levels are associated with increased Syringyl/Vanillyl (S/V) and Cinnamyl/Vanillyl (C/V) ratios, suggesting enhanced preservation of syringyl (S) and cinnamyl (C) units. In the middle layer (−20 to −60 cm), higher salinity correlates with elevated SVC (total lignin phenols), Acid/aldehyde (Ad/Al) ratios, and other markers of selective lignin degradation. For lipid biomarkers, salinity modulates microbial adaptation and turnover, as seen in variations in i17 (iso-C17), a17 (anteiso-C17), and unsaturation indices such as C16:1/C16, reflecting Gram-positive and Gram-negative bacterial activity. These trends indicate that salinity stress alters microbial lipid profiles, leading to reduced turnover and enhanced preservation in deeper, more anoxic environments. Principal Component Analysis (PCA) revealed depth- and salinity-driven patterns that distinguish between surface microbial transformations and deep-layer molecular preservation. Correlation analysis of Principal Components (PCs) with salinity revealed that higher salinity favored molecular stability in deeper layers, while lower salinity was associated with microbial transformations in surface layers. These findings underscore salinity’s critical role in OM stabilization and turnover, and provide a molecular framework to guide sustainable management of saline soils.

Tropical Peatlands accumulate organic matter (OM) and a significant source of carbon dioxide (CO₂) and methane (CH₄) under anoxic conditions. However, it is still ambiguous where in the peat profile these OM and gases are produced. The composition of organic macromolecules that are present in peatland ecosystems are mainly lignin and polysaccharides. As greater concentrations of lignin are found to be strongly related to the high CO₂ and CH₄ concentrations under anoxic conditions in the surface peat, the need to study the degradation of lignin under anoxic and oxic conditions has emerged. In this study, we found that the “Wet Chemical Degradation” approach is the most preferable and qualified to evaluate the lignin degradation in soils accurately. Then, we applied PCA for the molecular fingerprint consisting of 11 major phenolic sub-units produced by alkaline oxidation using cupric oxide (II) along with alkaline hydrolysis of the lignin sample presented in the investigated peat column called “Sagnes”. The development of various characteristic indicators for lignin degradation state on the basis of the relative distribution of lignin phenols was measured by chromatography after CuO-NaOH oxidation. In order to achieve this aim, the so-called Principal Component Analysis (PCA) has been applied for the molecular fingerprint composed of the phenolic sub-units, yielded by CuO-NaOH oxidation. This approach aims to seek the efficiency of the already available proxies and potentially create new ones for the investigation of lignin burial along a peatland. Lignin phenol vegetation index (LPVI) is used for comparison. LPVI showed a higher correlation with PC1 rather than PC2. This confirms the potential of the application of LPVI to decipher vegetation change, even in a dynamic system as the peatland. The population is composed of the depth peat samples, and the variables are the proxies and relative contributions of the 11 yielded phenolic sub-units.