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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Detalles Bibliográficos
Autores principales: Yan, Junyi, Li, Hongyi, Zuo, Enguang, Li, Tianle, Chen, Chen, Chen, Cheng, Lv, Xiaoyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001316/
https://www.ncbi.nlm.nih.gov/pubmed/36900566
http://dx.doi.org/10.3390/foods12051048

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001316/
https://www.ncbi.nlm.nih.gov/pubmed/36900566
http://dx.doi.org/10.3390/foods12051048

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