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

TL;DR

This study combines transmission speckle photography with convolutional neural networks to accurately classify milk fat content, demonstrating high recognition accuracy and potential application to other complex suspensions.

Contribution

It introduces a novel method integrating speckle photography and deep learning for parameter classification in complex suspensions.

Findings

Achieved 100% training accuracy in classifying milk fat content.

Shorter exposure times yield better speckle pattern recognition.

Method shows potential for broader applications in complex suspension analysis.

Abstract

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.