Automated Measurement of Air Bubbles Dispersion in Ice Cream Using Machine Learning Methods

Ice cream is a popular cold dessert. Its air phase consists of tiny bubbles with an average diameter of 15–60 µm. New ice cream formulations depend on the way the composition and production factors affect the air phase. As a result, ice cream producers need new time-saving and reliable methods to determine dispersion. The research objective was to create a computer program for marking the position of centers and diameter of air bubbles on microscopic images of a bounding circle type. The review part included 20 years of Russian and English publications on microscopic research methods in ice cream production indexed in Web of Science and Russian Research Citation Index. Microscopic images of ice cream air phase were obtained using an Olympus CX41RF microscope with a magnification of ×100. The automatic markup program employed the Python programming language, the Keras machine learning library, and the TensorFlow framework. The models were trained using the NVIDIA GTX video accelerator. The review showed that the dispersion of ice cream air phase depends on its composition and the freezing parameters whereas bubble formation is usually described in line with the existing foaming theories. A training data set was obtained by manual labeling of microscopic images. The optimal number channels in the convolutional layers of a neural network with LeNet-type architecture was determined, which made it possible to classify images as spheres or non-spheres with an accuracy of ≥ 0.995. The sliding window method helped to determine the limits of the neural network triggering for the sliding window method were determined, which reached 7.5% of the diameter with lateral displacement and 12.5% with scaling. The developed algorithm automatically marked bubbles on microscopic images. The error in determining the average diameter was below 1.8%. The new method for automated calculation of the number and diameter of air bubbles in ice cream proved to be user-friendly. It can be found in public domain, and researchers are free to adapt it to solve various computer vision issues.