Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning

Sabater, Carlos; Calvete, Inés; Vázquez, Xenia; Ruiz, Lorena; Margolles, Abelardo

doi:10.1016/j.ijfoodmicro.2024.110789

International Journal of Food Microbiology

2024

DOI: 10.1016/j.ijfoodmicro.2024.110789

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Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning

Carlos Sabater,

Inés Calvete,

Xenia Vázquez

et al.

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Recent advances of machine learning in the geographical origin traceability of food and agro‐products: A review

Li,

Qian,

Chen

et al. 2024

Comp Rev Food Sci Food Safe

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The geographical origin traceability of food and agro‐products has been attracted worldwide. Especially with the rise of machine learning (ML) technology, it provides cutting‐edge solutions to erstwhile intractable issues to identify the origin of food and agro‐products. By utilizing advanced algorithms, ML can extract feature information of food and agro‐products that is closely related to origin and, more accurately, identify and trace their origins, which is of great significance to the entire food and agriculture industry. This paper provides a comprehensive overview of the state‐of‐the‐art applications of ML in the geographical origin traceability of food and agro‐products. First, commonly used ML methods are summarized. The paper then outlines the whole process of preparation for modeling, model training as well as model evaluation for building traceability models–based ML. Finally, recent applications of ML combined with different traceability techniques in the field of food and agro‐products are revisited. Although ML has made many achievements in solving the geographical origin traceability problem of food and agro‐products, it still has great development potential. For example, the application of ML is yet insufficient in the geographical origin traceability using DNA or computer vision techniques. The ability of ML to predict the geographical origin of food and agro‐products can be further improved, for example, by increasing model interpretability, incorporating data fusion strategies, and others.

show abstract

Recent advances of machine learning in the geographical origin traceability of food and agro‐products: A review

Li,

Qian,

Chen

et al. 2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

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Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning

Cited by 1 publication

References 43 publications

Recent advances of machine learning in the geographical origin traceability of food and agro‐products: A review

Recent advances of machine learning in the geographical origin traceability of food and agro‐products: A review

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