  We have combined transmission speckle photography and machine learning for
direct classification and recognition of milk fat content classes. Our aim was
hinged on the fact that parameters of scattering particles (and the dispersion
medium) can be linked to the intensity distribution (speckle) observed when
coherent light is transmitted through a scattering medium. For milk, it is
primarily the size distribution and concentration of fat globules, which
constitutes the total fat content. Consequently, we trained convolutional
neural network to recognise and classify laser speckle from different fat
content classes (0.5, 1.5, 2.0 and 3.2%). We investigated four exposure-time
protocols and obtained the highest performance for shorter exposure times, in
which the intensity histograms are kept similar for all images and the most
probable intensity in the speckle pattern is close to zero. Our neural network
was able to recognize the milk fat content classes unambiguously and we
obtained the highest test and independent classification accuracies of 100 and
~99% respectively. It indicates that the parameters of other complex realistic
suspensions could be classified with similar methods.
