Jagiellonian University in Kraków Faculty of Chemistry, Krakow, Poland.
Appl Spectrosc. 2023 Apr;77(4):426-432. doi: 10.1177/00037028231154278. Epub 2023 Feb 2.
An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist" in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use.
一种优雅且成熟的有效数据筛选概念，最初由 Abraham Savitzky 和 Marcel J.E. Golay 提出，无疑是一种非常有效的工具，但并非没有局限性和缺点。尽管存在这些缺点，但多年来，它已在许多光谱处理领域成为“垄断者”，声称在商业上优于替代方法，这些方法可能会获得等效甚至在某些情况下更可靠的结果。为了表明对光谱数据集执行的基本操作，如平滑或微分，不必等同于应用特定的单个算法，本文简要介绍了几种此类替代方法，并讨论了它们的实际实现。特别强调了快速傅里叶方法（FFT），它在信号处理的一般领域中得到了广泛应用。最后，共享了一个用户友好的 Matlab 例程，其中实现了概述的算法，以便用户可以选择并应用更适合其个人需求的光谱数据处理技术，而无需在使用前进行编码。
