将机器学习与聚类触发的排放相结合,以追踪大米的地理来源
Hanyu Deng1, Peisheng Cao2, Qian Chen2
1College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.
Analytical chemistry
|December 25, 2025
概括
这项研究结合了聚类触发发射 (CTE) 和人工神经网络 (ANN),以准确识别大米.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 机器学习 机器学习
背景情况:
- 对食品安全和消费者保护而言,大米的地理原产地追踪至关重要.
- 米排放特性存在微妙差异,但直接检测起来具有挑战性.
- 机器学习 (ML) 为分析这些微小变化提供了一个潜在的解决方案.
研究的目的:
- 开发和验证一种用于确定大米的地理来源的新方法.
- 探索结合集群触发发射 (CTE) 与机器学习模型的有效性.
- 评估拟议方法的准确性和普遍性.
主要方法:
- 来自不同来源的样品使用聚类触发发射 (CTE) 进行分析,以获得光和光数据.
- 发射特性 (波长和寿命) 被用作机器学习模型的特征.
- 人工神经网络 (ANN) 被训练并测试了分类准确性.
主要成果:
- 人工神经网络 (ANN) 在评估的ML模型中表现优越.
- 结合CTE + ANN方法在一组测试大米样本上实现了96.4%的分类准确性.
- 该模型显示,未知大米样本的识别准确率为84.6%,并扩展到小麦.
结论:
- 整合CTE和ANN提供了一个强大的方法来追踪大米的地理来源.
- 这种方法有效地利用微妙的排放特性差异进行准确的识别.
- CTE + ANN 方法在其他农产品 (如小麦) 中的应用是有前途的.
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