机器学习辅助的光传感器阵列用于对类杀虫剂的定性和定量分析
Min Li1, Qiuli Pan2, Jun Wang2
1State Key Lab of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China.
Food chemistry
|September 9, 2023
概括
这项研究开发了一种新型传感器阵列,用于检测水果和蔬菜中的多重甲类杀虫剂 (PP). 该阵列成功地区分了类似的PP,提供了准确的识别和度预测.
科学领域:
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
背景情况:
- 在食品矩阵中同时检测结构相似的类杀虫剂 (PPs) 仍然是一个重要的分析挑战.
- 传统的纳米传感方法由于其高度的结构相似性,难以区分PP.
研究的目的:
- 开发一种能够区分结构相似的甲状腺农药 (PPs) 的传感器阵列.
- 为了实现高通量识别和在复杂样本中准确预测PP的度.
主要方法:
- 使用三种不同的纳米复合物复合物的传感器阵列的构建:罗达胺B-CD@Au,罗达胺6G-CD@Au和素6-CD@Au.
- 使用不同的受体/分析物和受体/染料亲和力,以及非线性光-分析物关系.
- 应用多变量模式识别和机器学习算法用于数据分析和预测.
主要成果:
- 成功地分辨出四种特定的 PPs:deltamethrin,fenvalerate,cyfluthrin 和 fenpropathrin.
- 实现了100%的分类准确性,用于在未知样本中高通量识别PP.
- 在预测 PP 度时,使用阶段性预测策略与机器学习相结合,证明了高准确度.
结论:
- 开发的传感器阵列提供了一个强大的平台,可以同时检测和区分结构相似的类杀虫剂.
- 这种方法显著提高了农产品中农药残留监测的分析能力.
- 传感器阵列和机器学习的结合为食品安全分析提供了强大的工具.
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