Machine Learning for Characterizing Biofuels Based on Femtosecond Laser Ionization Mass Spectrometry

A sample mixture of fatty acid methyl esters (FAMEs) was measured by femtosecond laser ionization mass spectrometry (fsLIMS) using the fifth (206 nm) and fourth (257 nm) harmonic emissions of an ytterbium (Yb) laser (1030 nm). Molecular ions were observed as the major signals in this technique, providing valuable information concerning the molecular weight and the number of double bonds in the molecule. The mass spectral data were then used as explanatory variables in machine learning based on artificial intelligence (AI) to correlate with objective variables such as the cetane number, kinematic viscosity, specific gravity, a higher heating value, an iodine value, flash point, oxidative stability index, and a cloud point measured for reference biofuel samples containing various FAMEs. The properties of biofuels, i.e., the objective variables, were evaluated from the mass spectral data obtained for unknown samples. The errors in the evaluation were a few percent when the distribution of the FAMEs in the unknown biofuel sample was similar to those of the biofuels used for machine learning. As demonstrated herein, the present approach, involving a combination of fsLIMS and AI, has the potential for use in evaluating the properties of a biofuel and then in solving of environmental issues associated with global warming.