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Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements

Bowler, Alexander; Ozturk, Samet; di Bari, Vincenzo; Glover, Zachary J.; Watson, Nicholas J.

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Authors

Alexander Bowler

Samet Ozturk

VINCENZO DI BARI Vincenzo.DiBari@nottingham.ac.uk
Assistant Professor in Food Structure

Zachary J. Glover

Nicholas J. Watson



Abstract

In manufacturing environments, real-time monitoring of yoghurt fermentation is required to maintain an optimal production schedule, ensure product quality, and prevent the growth of pathogenic bacteria. Ultrasonic sensors combined with machine learning models offer the potential for non-invasive process monitoring. However, methods are required to ensure the models are robust to changing ultrasonic measurement distributions as a result of changing process conditions. As it is unknown when these changes in distribution will occur, domain adaptation methods are needed that can be applied to newly acquired data in real-time. In this work, yoghurt fermentation processes are monitored using non-invasive ultrasonic sensors. Furthermore, a transmission based method is compared to an industrially-relevant non-transmission method which does not require the sound wave to travel through the fermenting yoghurt. Three machine learning algorithms were investigated including fully-connected neural networks, fully-connected neural networks with long short-term memory layers, and convolutional neural networks with long short-term memory layers. Three real-time domain adaptation strategies were also evaluated, namely; feature alignment, prediction alignment, and feature removal. The most accurate method (mean squared error of 0.008 to predict pH during fermentation) was non-transmission based and used convolutional neural networks with long short-term memory layers, and a combination of all three domain adaption methods.

Journal Article Type Article
Acceptance Date Jan 9, 2023
Online Publication Date Jan 10, 2023
Publication Date May 1, 2023
Deposit Date Jan 16, 2023
Publicly Available Date Jan 18, 2023
Journal Food Control
Print ISSN 0956-7135
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 147
Article Number 109622
DOI https://doi.org/10.1016/j.foodcont.2023.109622
Keywords Food Science; Biotechnology
Public URL https://nottingham-repository.worktribe.com/output/15940494
Publisher URL https://www.sciencedirect.com/science/article/pii/S0956713523000221?via%3Dihub

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