An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Andrea Loddo; Davide Ghiani; Alessandra Perniciano; Luca Zedda; Barbara Pes; Cecilia Di Ruberto

doi:10.20944/preprints202404.1882.v1

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preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202404.1882.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Andrea Loddo

^* ,

Davide Ghiani

Alessandra Perniciano

Luca Zedda

Barbara Pes

Cecilia Di Ruberto

Version 1 : Received: 26 April 2024 / Approved: 29 April 2024 / Online: 29 April 2024 (04:59:23 CEST)

How to cite: Loddo, A.; Ghiani, D.; Perniciano, A.; Zedda, L.; Pes, B.; Di Ruberto, C. An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation. Preprints 2024, 2024041882. https://doi.org/10.20944/preprints202404.1882.v1 Loddo, A.; Ghiani, D.; Perniciano, A.; Zedda, L.; Pes, B.; Di Ruberto, C. An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation. Preprints 2024, 2024041882. https://doi.org/10.20944/preprints202404.1882.v1

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MDPI and ACS Style

Loddo, A.; Ghiani, D.; Perniciano, A.; Zedda, L.; Pes, B.; Di Ruberto, C. An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation. Preprints 2024, 2024041882. https://doi.org/10.20944/preprints202404.1882.v1

APA Style

Loddo, A., Ghiani, D., Perniciano, A., Zedda, L., Pes, B., & Di Ruberto, C. (2024). An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation. Preprints. https://doi.org/10.20944/preprints202404.1882.v1

Chicago/Turabian Style

Loddo, A., Barbara Pes and Cecilia Di Ruberto. 2024 "An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation" Preprints. https://doi.org/10.20944/preprints202404.1882.v1

Abstract

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

Keywords

image processing; computer vision; machine learning; food industry; curd-firming time detection

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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