Two major residues are produced by the sugarcane industry, the fibrous fraction following juice extraction (bagasse), and the harvest residue (straw). The structures of the lignins from these residues were studied by pyrolysis coupled to gas chromatography-mass spectrometry (Py-GC/MS), nuclear magnetic resonance (NMR), and derivatization followed by reductive cleavage (DFRC). Whereas the lignin from bagasse has a syringyl-rich p-hydroxyphenyl:guaiacyl:syringyl (H:G:S) molar composition of 2:38:60, the lignin from straw is guaiacyl-rich (H:G:S of 4:68:28). The compositional differences were also reflected in the relative abundances of the different interunit linkages. Bagasse lignin was primarily β-O-4′ alkyl-aryl ether units (representing 83% of NMRmeasurable units), followed by minor amounts of β-5′ (phenylcoumarans, 6%) and other condensed units. The lignin from straw has lower levels of β-ethers (75%) but higher relative levels of phenylcoumarans (β-5′, 15%) and dibenzodioxocins (5-5/4-O-β, 3%), consistent with a lignin enriched in G-units. Both lignins are extensively acylated at the γ-hydroxyl of the lignin side-chain (42% and 36% acylation in bagasse and straw), predominantly with p-coumarates (preferentially on S-units) but also with acetates (preferentially on G-units) to a minor extent. Tetrahydrofuran structures diagnostically arising from β-β-coupling (dehydrodimerization) of sinapyl p-coumarate or its cross-coupling with sinapyl alcohol were found in both lignins, indicating that sinapyl pcoumarate acts as a monomer participating in lignification. The flavone tricin was also found in the lignins from sugarcane, as also occurs in other grasses.
The syringyl/guaiacyl ratio was determined for six different Eucalyptus spp. wood clones cultivated in four regions in Brazil. The determinants were made by pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and the results were compared with those obtained by alkaline nitrobenzene oxidation method. The S/G ratios were obtained considering all the identified lignin derivatives in the pyrograms and also using two groups of markers. The first group of markers consisted of guaiacol, 4-methylguaiacol, 4-vinylguaiacol, trans-isoeugenol, syringol, 4-methylsyringol, 4-vinylsyringol and trans-4-propenylsyringol compounds as markers. The second group included guaiacol, 4-methylguaiacol, 4-vinylguaiacol, vanillin, 4-ethylsyringol, 4-vinylsyringol, syringaldehyde, syringylacetone and trans-4-propenylsyringol. It was observed from the statistical analysis that the values of S/G obtained by Py-GC-MS using the two groups of markers did not differ significantly from those obtained by nitrobenzene oxidation method.
In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions—modularity, hierarchy, and overlap, facet-wise models are developed to dissect and inspect problem decomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure.
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