Institute of Chemistry , St. Petersburg State University , St. Petersburg , Russia 199034.
Laboratory of Artificial Sensory Systems , ITMO University , St. Petersburg , Russia 197101.
Anal Chem. 2018 May 1;90(9):5959-5964. doi: 10.1021/acs.analchem.8b01194. Epub 2018 Apr 10.
Smoothing of instrumental signals is an important prerequisite in data processing. Various smoothing methods were suggested through the last decades each having their own benefits and drawbacks. Most of the filtering methods are based on averaging in a certain window (e.g., Savitzky-Golay) or on frequency-domain representation (e.g., Fourier filtering). The present study introduces novel approach to signal filtering based on signal variance through PLS (projections on latent structures) regression. The influence of filtering parameters on the smoothed spectrum is explained and real world examples are shown.
仪器信号的平滑处理是数据处理的一个重要前提。在过去的几十年中，人们提出了各种平滑方法，每种方法都有其自身的优点和缺点。大多数滤波方法都是基于在某个窗口中进行平均（例如 Savitzky-Golay）或基于频域表示（例如 Fourier 滤波）。本研究通过偏最小二乘（PLS）回归引入了一种基于信号方差的信号滤波新方法。解释了滤波参数对平滑谱的影响，并展示了实际示例。
