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机器学习应用于食品中毒素的检测:系统性审查

Alan Inglis¹, Andrew C Parnell¹, Natarajan Subramani²

¹Hamilton Institute, Eolas Building, Maynooth University, W23 F2H6 Maynooth, Kildare, Ireland.

|June 26, 2024

概括

机器学习 (ML) 模型可以准确及时检测食品中的有害真菌毒素. 本次审查强调了ML在食品安全中的应用,强调了需要可重复的研究和开源数据的需要.

科学领域:

食品科学食品科学食品科学
计算生物学计算生物学
公共卫生公共卫生

背景情况:

菌毒素,有毒的真菌代谢物,污染粮食作物,对人类和动物健康构成风险.
传统的检测方法对于大规模的食品安全查是缓慢且不切实际的.
机器学习 (ML) 显示出在食品工业中快速准确检测真菌毒素的前景.

结论:

在改善食品安全和减轻真菌毒素风险方面,ML的进步至关重要.
标准化报告和开放数据实践对于在真菌毒素研究中推进ML至关重要.
未来的研究应该专注于解决可重复性问题,并探索各种ML架构.

关键词:

食品安全食品安全机器学习是机器学习.菌类毒素 (mycotoxin) 是一种有毒的物质.预测模型是一个预测模型.系统性审查系统性审查

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