在MEMS FT-NIR光谱学中对传统和新型基于神经网络的光谱传输方法进行比较评估
Ali Khater1, Ashar Seif Al-Nasr1, Omar Khater1
1Si-Ware Systems, Cairo, Egypt.
概括
本研究介绍了一种光谱残余神经传输网络 (SRNTN),用于里埃变换近红外 (FT-NIR) 光谱校准传输,在各种样品和条件下优于传统方法. SRNTN提供了强大的和可靠的光谱传输,特别是对于湿样品的新湿度叠加技术.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 由于仪器和环境的变化,在富里埃变换近红外 (FT-NIR) 光谱中校准转移具有挑战性.
- 像光谱空间转换 (SST) 和光谱对齐等现有方法在各种样本类型和条件上都有局限性.
- 基于微电机系统 (MEMS) 的FT-NIR传感器需要强大的校准传输策略来获得可靠的数据.
研究的目的:
- 评估传统的校准传输方法 (SST,光谱对齐) 与一种新的光谱残余神经传输网络 (SRNTN) 相比.
- 用各种样本集评估这些方法在基于MEMS的FT-NIR传感器上的性能.
- 引入和验证一种湿度叠加方法,以改善湿样中的光谱转移.
主要方法:
- 开发并应用了一种用于光谱传输的光谱残留神经传输网络 (SRNTN).
- 采用了 28 个不同样本 (农业,无机) 的标准校准套件来收集数据.
- 采用传统方法:光谱空间转换 (SST) 和光谱对齐.
- 引入了一种新的湿度叠加方法,用于湿样.
- 使用根平均平方误差 (RMSE),R2和平均范围改进来评估性能.
主要成果:
- 对于干燥/地面材料,SST方法表现出强的性能 (平均改善率高达45.9%).
- 光谱对齐对湿/整个样品有效 (平均范围改善高达18.8%).
- SRNTN表现出适应性和一致性,达到高达38.4%的平均范围改善和21.9%的RMSE减少.
- 在数据集之间,SRNTN表现出性能变化最低.
- 湿度叠加显著提高了湿样品的性能 (RMSE改善高达17.3%).
结论:
- 在各种条件下,SRNTN为FT-NIR频谱传输提供了强大而可靠的解决方案.
- 量身定制的校准策略,包括新的技术,如湿度叠加,对于精确的光谱转移至关重要.
- 该研究强调了SRNTN在提高基于MEMS的FT-NIR传感器的一致性和可靠性的潜力.
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