  We demonstrate a smart laser-diffraction analysis technique for particle
mixture identification. We retrieve information about the size, geometry, and
ratio concentration of two-component heterogeneous particle mixtures with an
efficiency above 92%. In contrast to commonly-used laser diffraction schemes --
in which a large number of detectors is needed -- our machine-learning-assisted
protocol makes use of a single far-field diffraction pattern, contained within
a small angle ($\sim 0.26^{\circ}$) around the light propagation axis. Because
of its reliability and ease of implementation, our work may pave the way
towards the development of novel smart identification technologies for sample
classification and particle contamination monitoring in industrial
manufacturing processes.
