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
This work addresses the need for accurate fat content measurement in milk, which is important for dairy quality control, but it is incremental as it applies existing methods to a specific domain.
The researchers tackled the problem of determining fat content in cow's milk by combining transmission speckle photography with a convolutional neural network, achieving test and independent classification accuracies of 100% and ~99% respectively.
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.