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Diverse methodologies exist to determine the chemical composition, proximate analysis, and calorific value of biomass. Researchers select and apply a specific methodology according to the lignocellulosic material they study and the budgetary resources available. In this project, we determined the primary chemical constitution and proximate analysis of <i>Prosopis laevigata</i> (Humb. & Bonpl.) Jonhst wood using a traditional chemical method and a novel procedure based on the deconvolution of the DTG signal produced by TGA. The highest calorific value was verified using a calorimetric pump based on mathematical models. We also conducted elemental analysis and a microanalysis of ash, and applied Fourier transform infrared spectroscopic analysis (FT-IR). The means of the results obtained by the chemical method and TGA-DTG, respectively, were: hemicelluloses 7.36%–(8.72%), cellulose 48.28%–(46.08%), lignin 30.57%–(32.44%), extractables 13.53%–(12.72%), moisture 2.03%–(4.96%), ash 1.77%–(1.90%), volatile matter 75.16%–(74.14%), and fixed carbon 23.05%–(18.93%). The procedure with the calorimetric pump generated a calorific value above 20.16 MJ/kg. The range generated by the various models was 18.23–21.07 MJ/kg. The results of the elemental analysis were: carbon 46.4%, hydrogen 6.79%, oxygen 46.43%, nitrogen 0.3%, and sulfur 0.5%. The microanalysis of ash identified 18 elements. The most abundant ones were potassium ˃ calcium ˃ sodium. Based on the infrared spectrum (FT-IR) of <i>Prosopis laevigata</i> wood, we detected the following functional groups: OH, C-H, C=O, CH_2, CH₃, C-O-C, C-OH, and C4-OH. Our conclusion is that the TGA-DTG method made it possible to obtain results in less time with no need for the numerous reagents that chemical procedures require. The calorific value of <i>P. laevigata</i> wood is higher than the standards. Finally, according to our results, proximate analysis provides the best model for calculating calorific value.