使用精心调整的语言模型加速NOVA食品加工水平的分类:一项多国研究
Guanlan Hu1, Nadia Flexner1, María Victoria Tiscornia2
1Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada.
Nutrients
|October 14, 2023
概括
研究人员使用AI自动化了超加工食品 (UPF) 的分类. 这种方法可以准确地识别食品数据库中的UPF,提供一种具有成本效益的方法来监测全球食品供应.
科学领域:
- 营养科学 营养科学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 全球超加工食品 (UPF) 的消费量正在上升,与非传染性疾病风险的增加有关.
- 目前用于分类食品加工水平的方法 (例如,NOVA系统) 是手工,劳动密集型和耗时的.
- 需要有效,可扩展的方法来监测国家食品供应中的UPF含量.
研究的目的:
- 开发和验证一种自动化方法,使用基于变压器的语言模型对食品加工水平进行分类.
- 评估不同国家食品数据库 (加拿大,阿根廷,美国) 的自动化分类的准确性和通用性.
- 为决策者提供一个具有成本效益的工具来监测和监管UPF.
主要方法:
- 使用的基于变压器的语言模型 (例如,BERT) 对食品标签上的成分清单文本进行了微调.
- 将模型应用于加拿大,阿根廷和美国的国家食品数据库.
- 将语言模型的性能与使用营养数据和词包方法的传统机器学习模型进行了比较.
主要成果:
- 在加拿大食品分类方面取得了高整体准确性 (F1得分为0.979),优于传统模型.
- 对于大多数食品类别的加工食品和超加工食品,已证明高预测准确度 (0.98).
- 确认了阿根廷和美国食品数据库的自动化策略的有效性和普遍性.
结论:
- 基于变压器的语言模型为分类食品加工水平提供了准确和高效的自动化解决方案.
- 开发的方法提供了一种可扩展和具有成本效益的方法,用于监测全球食品供应中的UPF.
- 这种自动化支持旨在规范UPF和减轻相关非传染性疾病风险的公共卫生倡议.
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