光光谱与广泛的学习系统相结合,对白菜中的二可纳含量进行了表征
Di Wu1,2, Xiaorong Sun1,2, Yuhan Liu1,2
1Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China. liucl@btbu.edu.cn.
概括
本研究引入了一种使用光谱学和机器学习检测二可纳农药残留物的新方法. 该方法在识别和量化残留物方面实现了高精度,提高了蔬菜的食品安全.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 检测农药残留物对于蔬菜质量和食品安全至关重要.
- 需要准确的方法来监测污染物,如difenoconazole.
研究的目的:
- 开发一种使用光谱学和机器学习检测二可纳农药残留物的方法.
- 建立有效的模型,以确定二可纳的合格性和含量预测.
主要方法:
- 利用三维光谱法,找到最佳的激发波长 (420 nm).
- 开发了使用K-近邻 (KNN) 和决策树算法的资格模型.
- 使用非信息变量消除 (UVE) 和连续投影算法 (SPA) 进行波长选择.
- 使用广泛的学习系统 (BLS),部分最小平方回归 (PLSR) 和回声状态网络 (ESN) 构建了内容预测模型.
主要成果:
- 决策树算法在资格确定模型中实现了97%的准确性.
- 与BLS模型相结合的UVE在内容预测方面表现出色 (Rp2=0.959,RMSEP=1.358).
结论:
- 该研究成功地证明了将光谱学与农药残留分析的广泛学习系统相结合的可行性.
- 这种方法为开发在线监测蔬菜中的农药残留含量系统提供了有价值的参考.
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