多路分解 随后是光复卷 时间衰变数据
Anne Bech Risum1, Jesper Løve Hinrich1, Åsmund Rinnan1
1Department of Food Science, University of Copenhagen, Rolighedsvej 26, DK-1958 Frederiksberg C, Denmark.
Analytical chemistry
|December 11, 2023
概括
结合PARAFAC和再卷积的新方法为时间解析发射光谱 (TRES) 数据提供了可靠的分析. 这种方法准确地解决复杂的混合物,即使有高噪音和广泛的仪器响应功能.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 时间分辨率发射光谱 (TRES) 由于仪器成本下降和LED光源的出现,越来越容易获得.
- 准确分析TRES数据对于理解复杂的光系统至关重要.
研究的目的:
- 通过整合并行因素分析 (PARAFAC) 和再卷积来引入和验证一种分析TRES数据的新方法.
- 将拟议方法的性能与已知技术 (如全局再卷,尾部贴合和SLICING方法) 进行比较.
主要方法:
- 拟议的方法结合了软建模技术PARAFAC和标准TRES装配方法重卷.
- 使用3个光体的测量TRES数据集和最多4个光体的模拟数据集来评估性能.
主要成果:
- 全球安装只有在高信号噪声比率 (SNR>15dB) 时才有效.
- SLICING方法在估计时间衰变方面存在局限性,原因是难以定义光谱尾巴.
- 新的PARAFAC-reconvolution方法展示了强大而准确的结果,优于其他技术,特别是在高噪音 (SNR降至5dB) 和广泛的仪器响应功能下.
结论:
- 综合PARAFAC-reconvolution方法为分析复杂的TRES数据提供了一种优越的方法,特别是在具有挑战性的条件下.
- 这种技术提高了来自TRES测量的光体表征的可靠性和准确性.
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