Park Yeonju, Noda Isao, Jung Young Mee
1 Department of Chemistry, Institute for Molecular Science and Fusion Technology, Kangwon National University, Chunchon, Republic of Korea.
2 Department of Materials Science and Engineering, University of Delaware, DE, USA.
Appl Spectrosc. 2018 May;72(5):765-775. doi: 10.1177/0003702817752126. Epub 2017 Dec 21.
Smooth factor analysis (SFA) is introduced as an effective method of removing heavy noise from spectral data sets. A modified form of the nonlinear iterative partial least squares (NIPALS) algorithm involving the smoothing of factors at each step is used in SFA. Compared with the conventional smoothing techniques for individual spectra, SFA is much more effective in the treatment of very noisy spectra (∼40% noise level). Smooth factor analysis invokes a large number of smooth factors to retain pertinent spectral information for high fidelity without distortion. This approach can be used as an effective general pretreatment procedure for multivariate spectral data analysis, such as principal component analysis (PCA) and partial least squares (PLS). This SFA method was also applied to the real experimental data, and its results successfully demonstrated the powerful potential for effective noise removal. Furthermore, this treatment is found to be very helpful to assist effective interpretation of two-dimensional correlation spectroscopy (2D-COS) spectra with very high noise level, which was not possible before.
平滑因子分析（SFA）被引入作为从光谱数据集中去除重噪声的有效方法。SFA中使用了一种改进形式的非线性迭代偏最小二乘法（NIPALS）算法，该算法在每一步都涉及因子的平滑处理。与用于单个光谱的传统平滑技术相比，SFA在处理噪声非常大的光谱（噪声水平约为40%）时更为有效。平滑因子分析调用大量平滑因子以在不失真的情况下保留相关光谱信息以实现高保真度。这种方法可以用作多元光谱数据分析（如主成分分析（PCA）和偏最小二乘法（PLS））的有效通用预处理程序。这种SFA方法也应用于实际实验数据，其结果成功证明了有效去除噪声的强大潜力。此外，发现这种处理对于辅助解释噪声水平非常高的二维相关光谱（2D-COS）光谱非常有帮助，而这在以前是不可能的。
