Review of visual analytics methods for food safety risks

Yi Chen*, Caixia Wu, Qinghui Zhang, Di Wu

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)
45 Downloads (Pure)

Abstract

With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.

Original languageEnglish
Article number49
Number of pages14
Journalnpj Science of Food
Volume7
DOIs
Publication statusPublished - 12 Sept 2023

Fingerprint

Dive into the research topics of 'Review of visual analytics methods for food safety risks'. Together they form a unique fingerprint.

Cite this