ResFusionNet-TSMT:使用表面增强的拉曼光谱检测农药的残留网络
Ailing Tan1, Yunhao He1, Bingru Shi1
1School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao, 066004, China.
Talanta
|September 19, 2025
概括
通过结合残余网络和变压器,ResFusionNet-TSMT通过使用表面增强拉曼散射 (SERS) 提高了农药检测. 这种深度学习模型在分类和量化农药残留物中实现了高精度,提高了农业安全.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 表面增强拉曼散射 (SERS) 是快速检测农药的一个关键技术.
- 挑战包括光谱干扰和信号变化,影响准确性.
- 需要先进的方法来同时进行分类和量化.
研究的目的:
- 引入ResFusionNet-TSMT,这是一个用于农药检测的新型深度学习框架.
- 提高同时进行农药分类和度量化的准确性.
- 为了解决SERS数据中的光谱干扰和信号变化.
主要方法:
- 开发了ResFusionNet-TSMT,集成了剩余网络 (ResNet) 和变压器.
- 采用双流架构用于原始和多尺度的光谱输入.
- 利用了注意力聚合和特征融合的变压器编码器以及联合学习的课堂注意力机制.
主要成果:
- 实现了99.21%的分类精度 (F1=99.08%) 和0.4640 ppmMAE (R2=0.8764) 的量化.
- 超过了传统的机器学习和最先进的深度学习方法.
- 在捕捉特征性光谱特征方面表现出有效性,特别是在低度的情况下.
结论:
- ResFusionNet-TSMT显著提高了基于SERS的农药检测准确度.
- 该模型显示了实时农业安全监测的巨大潜力.
- 通过废除研究和可视化验证的有效性.
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