一个基于物理的双分支聚变网络,用于用近红外光谱测定水中的总量的定量测定
Cailing Wang1, Shuhui Hao1, Guohao Zhang2
1College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 14, 2026
概括
我们开发了PICSEN,这是一个新的基于物理的网络,用于使用近红外光谱学准确量化水中的总 (TP). 这种方法克服了数据挑战,并确保物理一致的预测,以改善水质监测.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 使用近红外 (NIR) 光谱法对总 (TP) 的定量确定是具有挑战性的,因为光谱数据复杂.
- 传统模型往往缺乏物理原理,导致不准确和不一致的预测.
研究的目的:
- 开发一种使用NIR光谱的强大和物理一致的TP量化方法.
- 解决现有的数据驱动模型在处理高维和噪音频谱数据方面的局限性.
主要方法:
- 提出PICSEN,一个基于物理的卷积顺序双分支融合网络.
- 融合全球光谱特征 (CNN) 与局部顺序依赖 (GRU).
- 结合了基于物理的规范化术语和用于光谱重建的有效吸收代理.
主要成果:
- 实现了0.9380±0.0191的高平均R2,证明了强大的性能.
- 物理约束被确定为模型稳定性和物理一致性的关键驱动因素.
- PICSEN 显示出对噪声的适应性,并与已知的物理化学吸收区域保持一致.
结论:
- 通过NIR光谱,PICSEN提供了一种稳定且物理一致的方法来通过NIR光谱量化TP.
- 可适应的有效吸收代理框架具有各种区域水质监测应用的潜力.
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