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. Information2024, 15, 360.
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. Information 2024, 15, 360.
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. Information2024, 15, 360.
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. Information 2024, 15, 360.
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.
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.