CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and...

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Bibliographic Details
Main Authors: Junyi Yan, Hongyi Li, Enguang Zuo, Tianle Li, Chen Chen, Cheng Chen, Xiaoyi Lv
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Foods
Subjects:
food quality safety
contamination warning
contrastive learning
self-supervised learning
graph neural networks
Online Access:https://www.mdpi.com/2304-8158/12/5/1048

Internet

https://www.mdpi.com/2304-8158/12/5/1048

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