Application of Unsupervised Learning for the Evaluation of Burial Behavior of Geomaterials in Peatlands: Case of Lignin Moieties Yielded by Alkaline Oxidative Cleavage

Abstract

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.

Investigating the Physical and Operational Characteristics of Manufacturing Processes for MFI-Type Zeolite Membranes for Ethanol/Water Separation via Principal Component Analysis

“…The first two PCs accounted for only 38% of the total variance (21.4% and 16.6% for the first and second PC, respectively (Figure 1a)). This value is lower than those reported in previous PCA investigations concerning materials' characterization [20,21,23,28,60]. For the investigated variables, most properties had moderate to low contributions to PC 1 , with insignificant influence of pore size, flux, and synthesis and pervaporation temperature.…”

Towards Understanding Aerogels’ Efficiency for Oil Removal—A Principal Component Analysis Approach

“…PCs are actually orthogonal in relation to each other; this represents the geometrical interpretation of no correlation between PCs [ 14 ]. PCA is widely used in various fields, such as geology [ 15 , 16 ], biomass characterization, and valorization [ 17 ]. For aerogels, our previous studies applied PCA for the sake of revealing hidden patterns between the physical and chemical properties and adsorption parameters [ 12 , 13 ].…”