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We have combined transmission speckle photography and machine learning fordirect classification and recognition of milk fat content classes. Our aim washinged on the fact that parameters of scattering particles (and the dispersionmedium) can be linked to the intensity distribution (speckle) observed whencoherent light is transmitted through a scattering medium. For milk, it isprimarily the size distribution and concentration of fat globules, whichconstitutes the total fat content. Consequently, we trained convolutionalneural network to recognise and classify laser speckle from different fatcontent classes (0.5, 1.5, 2.0 and 3.2%). We investigated four exposure-timeprotocols and obtained the highest performance for shorter exposure times, inwhich the intensity histograms are kept similar for all images and the mostprobable intensity in the speckle pattern is close to zero. Our neural networkwas able to recognize the milk fat content classes unambiguously and weobtained the highest test and independent classification accuracies of 100 and~99% respectively. It indicates that the parameters of other complex realisticsuspensions could be classified with similar methods.

Combining transmission speckle photography and convolutional neural  network for determination of fat content in cow's milk -- an exercise in  classification of parameters of a complex suspension