Food adulteration identification framework via unsupervised Anomaly Detection algorithm: applied to camel milk (FIAD）

Yilin Li; Junyi Yan; Pengbo Wang; Lei Sun; Ziwei Yan; Enguang Zuo

doi:10.1117/12.3005944

10 October 2023 Food adulteration identification framework via unsupervised Anomaly Detection algorithm: applied to camel milk (FIAD）

Yilin Li, Junyi Yan, Pengbo Wang, Lei Sun, Ziwei Yan, Enguang Zuo

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127990S (2023) https://doi.org/10.1117/12.3005944
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Food adulteration driven by economic interests is an important cause of food safety. Camel milk is widely sought for its high nutritional and medicinal value; some businesses adulterate it for profiteering due to its low yield and high price. Traditional adulteration detection methods rely on supervised learning, which is limited by the data of unknown categories in practical application scenarios, and it is difficult to solve the adulteration problem of category imbalance. For the above scenario, this paper proposes a camel milk adulteration detection framework FIAD based on an unsupervised anomaly detection algorithm, which starts from the perspective of anomaly detection and automatically captures and isolates anomalous features in the data through a tree algorithm without manual labeling of data, directly targeting the adulteration identification problem of category imbalance. We tested the discrimination performance of FIAD in a batch of category-imbalanced camel milk adulteration datasets. At 10% and 20% category imbalance, FIAD achieved AUCs of 0.943 and 0.959 and Recall of 0.915 and 0.949, while occupying less memory, better than eight baseline models. The results show that FIAD has excellent comprehensive identification performance and provides a low-cost and high-efficiency identification method for camel milk adulteration identification.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yilin Li, Junyi Yan, Pengbo Wang, Lei Sun, Ziwei Yan, and Enguang Zuo "Food adulteration identification framework via unsupervised Anomaly Detection algorithm: applied to camel milk (FIAD）", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127990S (10 October 2023); https://doi.org/10.1117/12.3005944

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KEYWORDS

Data modeling

Machine learning

Detection and tracking algorithms

Performance modeling

Education and training

Statistical modeling

Safety

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