https://doi.org/10.1140/epjp/s13360-024-04900-1
Regular Article
Combining transmission speckle photography and convolutional neural network for determination of fat content in cow milk: an exercise in classification of parameters of a complex suspension
1
Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, 02-668, Warsaw, Poland
2
Laser and Fibre Optics Centre, Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
Received:
29
August
2023
Accepted:
15
January
2024
Published online:
2
February
2024
We have combined transmission speckle photography and machine learning for direct classification and recognition of off-the-shelf 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 recognize and classify laser speckle images from different fat content classes (0.5, 1.5, 2.0 and 3.2%). We allowed exposure time to vary between speckle images, to overcome possible limitations imposed by the camera's dynamic range. We investigated four exposure-time protocols and obtained the highest classification performance for shorter exposure times, in which the intensity histograms are kept similar (and close to that of a fully developed speckle) for all images. Our neural network was able to recognize the fat content classes of milk unambiguously, and we achieved classification accuracies of 100 and ~ 99% for the test and independent sets, respectively. It indicates that the parameters of other complex realistic suspensions could be classified with similar methods.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.