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对食品安全风险的视觉分析方法的审查

Yi Chen¹, Caixia Wu², Qinghui Zhang²

¹Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China. chenyi@th.btbu.edu.cn.

NPJ science of food

|September 12, 2023

概括

视觉分析通过整合人类和机器智能来增强食品安全风险分析和预警 (RAPW). 本综述涵盖了十年来食品安全RAPW视觉分析方面的进展.

科学领域:

食品安全科学食品安全科学
数据科学数据科学数据科学
信息可视化信息可视化

背景情况:

大数据在食品安全方面越来越多地用于风险分析和预警 (RAPW).
视觉分析为大规模数据洞察提供交互式的人机集成方法.
越来越需要先进的方法来应对复杂的食品安全挑战.

研究的目的:

在过去的十年里,审查食品安全RAPW视觉分析的发展.
总结食品安全方面的数据来源,特征和分析任务.
确定该领域的未来机遇和挑战.

主要方法:

对用于关联分析,风险评估,风险预测和欺诈识别的数据分析方法的审查.
对多维,层次,关联和时空数据的可视化和交互技术的审查.
综合目前的研究趋势和食品安全视觉分析的未来方向.

主要成果:

总结了食品安全方面的数据来源,特征和分析任务.
对关键RAPW任务的数据分析方法进行了审查.
评估了各种数据类型的可视化和交互技术.

结论:

视觉分析为食品安全RAPW提供了新的解决方案.
未来的机会包括多式联网数据分析和AI集成.
应对挑战对于推动食品安全视觉分析至关重要.

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