农业的预测可持续性:对活性成分限制和禁令的机器学习分析
1University of São Paulo, Rua Quintino Bocaiuva, São Paulo, Brazil.
PLOS global public health
|April 22, 2025
概括
确定影响活性成分禁令的关键因素对于可持续的研发至关重要. 政府,非政府组织和黑名单对农业活性成分的限制产生了重大影响.
科学领域:
- 农业科学 农业科学
- 监管事务 监管事务
- 数据科学数据科学数据科学
背景情况:
- 活性成分开发需要超过3亿欧元和12年.
- 严格的法规和环境安全要求经常限制或禁止成分.
- 预测监管行动对于高效的研发投资至关重要.
研究的目的:
- 确定影响活性成分限制和禁止的关键参数.
- 在开发新活性成分的过程中加强决策.
- 通过减少可能被禁止的成分的研发来促进可持续性.
主要方法:
- 对监管清单的分析.
- 应用先进的机器学习技术.
- 用于参数识别的统计建模.
主要成果:
- 政府和非政府组织是主动成分禁令的主要影响者.
- 黑名单在很大程度上有助于限制和禁止农业活性成分.
- 食品法典 (Codex Alimentarius) 作为一个区域影响者,因国家而异.
结论:
- 机器学习和统计建模可以预测活性成分的限制.
- 洞察力帮助行业和政策制定者制定有效的监管策略.
- 根据知情的标准进行选择可以提高可持续性,减少研发浪费.
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