Sample laser speckle images generated from commercial milk in thin cuvette

Published: 29 November 2023| Version 1 | DOI: 10.17632/pg9z22d9pz.1
Kwasi Nyandey


This is a sample of the whole data used in the study "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". Commercial milk samples of different fat content classes were purchased from supermarkets (where fresh milk was re-frigerated but ultra-pasteurized (UHT) milk was left on the open shelf). The samples were mainly from 3 dairy plants. We label the samples as Dairy 1 to Dairy 5 and a Collection of Dairies (CoD, see Table 1 in the paper). Dairies 4 and 5 were added to supplement the lack of 1.5% class in Dairies 1 and 2. The undiluted milk sample (10 μL) was loaded into a flat thin cuvette and covered with a 0.19 mm-thick coverslip. The cuvette was prepared by sticking a 50 μm-thick adhesive tape on a microscope slide and cutting out a 10 mm circular segment from the center of the tape. The sample in the cuvette was illuminated perpendicularly with a 5 mW, 1.7 mm collimated beam from a green frequency-doubled Nd:YAG laser at 532 nm. A 14-bit colour camera (Pike F-032C, AVT) was used to record laser speckle videos at 26.9o angle and 85 mm distance from the sample and at a constant temperature of 21±1oC. Two experimental runs were performed for each 1L-container of milk opened. In the first experimental run, the milk was loaded into the cuvette and 10 movies were recorded at 10 different locations on the sample (different background/static speckle) and labelled as “Version 1”. After recording 10 movies for Version 1 the cuvette was cleaned. It involved gently dipping it in a diluted cleaning solution, sonication for a minute and rinsing off with distilled water. Finally it was dried clean by blowing dry nitrogen gas (99.8%) over it. It was then loaded again with milk to record the movies for Version 2. At a single illumination point, ~1000 frames (13 s @ 75 fps) movie was recorded, the 10-locations on the same sample was exhaustive. Using milk form Dairy 1 only we experimented on 3 exposure time Protocols and confirmed our finding with milk from Dairies 2 and 3. The speckle images were extracted and normalized to grayscale level (0 - 1). Convolutional Neural Network (CNN) was trained on the version 1 of the data and tested on test set from version 1 and independent set (version 2). The study revealed that when "fully-developed laser speckles" are recorded from changing concentration and different particle sizes of suspensions CNN can be used to classify them unambiguously. In the folders there are sample images, videos, trained convolutional neural networks and classification algorithm



Polska Akademia Nauk Instytut Fizyki, University of Cape Coast


Machine Learning, Speckle Metrology, Milk Fat Globules, Convolutional Neural Network


Narodowe Centrum Nauki